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AutomaticWeather Station (AWS) Alat pengukur cuaca otomatis (Automatic Weather Station / AWS) merupakan alat yang Automatic Weather Station merupakan stasiun cuaca otomatis yang didesain untuk mengukur dan mencatat data parameter-parameter meteorologi (radiasi matahari, arah dan kecepatan angin, suhu udara, kelembaban udara, tekanan udara, curah hujan) secara otomatis dan terintegrasi untuk Berita Utama #Maritim #Inspeksi #Posko Tahun Baru #Posko Natal #Automatic Weather Station #AWS #Mobil Cuaca #Stasiun Geofisika # (16-23 Juli 2021) 15 Jul 2021; #Siaran Pers #Meteorologi #Apps Info BMKG; BMKG: Update Prakiraan Cuaca di Wilayah Indonesia dalam Sepekan ke Depan (31 Mei-07 Juni 2021) 31 Mei 2021; #Siaran Pers #Meteorologi # BMKGwas invited to The 1st International Panel for Director General of Meteorological Services of Islamic Countries. Provision of Critical Weather Information Services to Prevent and Curb Drought-Induced Forest Fire. BMKG Wins the Best Booth Award in Disaster Risk Reduction (DRR) Week 2017. AWS(Automatic Weather Stations) merupakan suatu peralatan atau sistem terpadu yang di disain untuk pengumpulan data cuaca secara otomatis serta di proses agar pengamatan menjadi lebih mudah. AWS ini umumnya dilengkapi dengan sensor, RTU (Remote Terminal Unit), Komputer, unit LED Display dan bagian-bagian lainnya. AWS dipasang pada ketinggian Alatini akan mengukur langsung radiasi yang dihasilkan oleh sinar matahari tanpa terhalang awan. Automatic Solar Radiation Station (ASRS) Dalam atmosfer bumi terdapat bermacam-macam radiasi seperti : 1. Direct Solar Radiation (S) yaitu radiasi langsung dari matahari yang sampai ke permukaan bumi. 2. AutomaticWeather Station (AWS) ialah sebuah sistem informasi monitoring cuaca terpadu dari Badan Meteorologi Klimatologi dan Geofisika (BMKG) berdasarkan alat pemantau cuaca milik BMKG yang tersebar diseluruh wilayah Indonesia. Berikutini adalah tata cara pengambilan sampel air hujan dari instrumen Automatic Rain Water Sampler (ARWS): Lihat jadwal sampling. Sesuaikan waktu pengambilan sampel air hujan dengan jadwal sampling yang dikirim dari kantor BMKG Pusat. Siapkan botol plastik 100 mL yang dikirim dari kantor BMKG Pusat. PuslitbangBMKG tahun 2014 tentang perbandingan data pengamatan parameter meteorologi antara metode manual dan otomatis melalui otomatisasi instrument cuaca dan iklim menggunakan Agroclimate Automatic Weather Station di Stasiun Dramaga, Stasiun Sicincin, Stasiun Banyuwangi, dan Stasiun Kediri melaporkan bahwa rata-rata error MarineAutomatic Weather Station (MAWS) (9 sensor) per unit. Rp 3.475.000,00. 6. Automatic Weather Observation System (AWOS) (9 sensor) per unit. ptsp[at]bmkg.go.id; T : Apakah pemohon bisa mengajukan permohonan tanpa harus datang ke PTSP? J : Bisa, dengan mengirimkan berkas permohonan yang lengkap, dan sudah di tanda tangani via email MFD8SEY. NASA/ADS Abstract To improve the quality and quantity of meteorological data over Indonesia, Meteorology Climatology and Geophysics Agency of Indonesia BMKG is continuously developing automatic weather observations. BMKG has 63 units Automatic Weather Station AWS and 165 units Automatic Weather Observation System AWOS both inside and outside the BMKG Station environment. To make the control of sensor conditions easier, especially for temperature, pressure, relative humidity, and rainfall sensors, an additional system is needed to monitor and warn when problems occur with these sensors. The correlation among weather parameters data is the key to monitoring the sensor condition, these data are going to be trained and tested with the Artificial neural network ANN method. Then, the sensor condition normal or error indicated can be well detected based on AWS’s data. The quality improvement of automatic weather station data is expected to increase the utilization of the data. Publication Journal of Physics Conference Series Pub Date February 2021 DOI Bibcode 2021JPhCS1816a2056W Automatic Weather Stations in an Edged Internet of Things IoT system publicationAutomatic Weather Stations AWS are extensively used for gathering meteorological and climatic data. The World Meteorological Organization WMO provides publications with guidelines for the implementation, installation, and usages of these stations. Nowadays, in the new era of the Internet of Things, there is an ever-increasing necessity for the...Context 1... on the literature [31] and our perspective, an Edged IoT system architecture has three 3 main layers, as illustrated in Figure 2, which are as follows Connected End devices at the edge of a network with embedded processing power, primitive intelligence, network connectivity, and sensing capabilities. ...... The technological advancement in weather predictions has shown the need for accurate measurement of atmospheric parameters which is of utmost importance to meteorologists. Of recent, there is a need for observing atmospheric weather parameters that will make scientist have access to real-time data they need to forecast or predict atmospheric weather conditions [2]. The need for accurate meteorological forecasts is on the rise because some sectors are in need of accurate weather prediction like the agriculture sector, maritime sector, and aviation sector. ...An atmospheric data acquisition device is designed to ease and improve on the current method of acquiring Temperature, Pressure, and Relative Humidity measurement at different altitudes. The proposed work aims to solve the problem of inadequate atmospheric data by monitoring atmospheric weather conditions using sensors while the microcontroller processes the data collected and relays it to the user. This research was carried out at the University of Uyo, between September 2018 and January, 2023. Considering that weather forecasting is of the utmost importance in our current society, the system has been built using a BME280 module for the atmospheric parameters acquisition, an ESP8266 as the microcontroller for Data processing, and a wireless module for processing and transfer of the data from the BME module, a NEO6M GPS module for longitude and latitude, a Li-ion cell to power the components and a TP4056 circuit to recharge the Li-ion cell. A web application was incorporated to help the user interact and access the data to enable ease of understanding and real-time logging of the data collected. This work is targeted toward the weather forecasting sector, agricultural sector, and individuals which may wish to gather information about the atmosphere for knowledge consumption. The results show that this device has a good performance for capturing atmospheric parameters for real-time monitoring purposes.... The use of current technologies is also included. Finally, the study presents a case study developed in AWS AgroComp project and its results [9]. The work of Kulkarni et al. is about the nature of a weather display method using low-cost components that even any electronics enthusiast could design. ...Industrialization and rapid urbanization in almost every country adversely affect many of our environmental values, such as our core ecosystem, regional climate differences and global diversity. The difficulties we encounter as a result of the rapid change we experience cause us to encounter many problems in our daily lives. The background of these problems is rapid digitalization and the lack of sufficient infrastructure to process and analyze very large volumes of data. Inaccurate, incomplete or irrelevant data produced in the IoT detection layer causes weather forecast reports to drift away from the concepts of accuracy and reliability, and as a result, activities based on weather forecasting are disrupted. A sophisticated and difficult talent, weather forecasting needs the observation and processing of enormous volumes of data. In addition, rapid urbanization, abrupt climate changes and mass digitization make it more difficult for the forecasts to be accurate and reliable. Increasing data density and rapid urbanization and digitalization make it difficult for the forecasts to be accurate and reliable. This situation prevents people from taking precautions against bad weather conditions in cities and rural areas and turns into a vital problem. In this study, an intelligent anomaly detection approach is presented to minimize the weather forecasting problems that arise as a result of rapid urbanization and mass digitalization. The proposed solutions cover data processing at the edge of the IoT and include filtering out the missing, unnecessary or anomaly data that prevent the predictions from being more accurate and reliable from the data obtained through the sensors. Anomaly detection metrics of five different machine learning ML algorithms, including support vector classifier SVC, Adaboost, logistic regression LR, naive Bayes NB and random forest RF, were also compared in the study. These algorithms were used to create a data stream using the time, temperature, pressure, humidity and other sensor-generated information.... The results are then compared to land use data from the CORINE land cover inventory. The methodology provided promising results, which can be further improved by applying machine learning methods such as artificial neural networks, random forests and expert systems [12,18,19], and the results can be used for the application of forest policy as well as decision making [20][21][22][23][24]. ... Konstantinos IoannouThe detection of possible areas for the application of agroforestry is essential and involves the usage of various technics. The recognition of forest types using satellite or aerial imagery is the first step toward this goal. This is a tedious task involving the application of remote sensing techniques and a variety of computer software. The overall performance of this approach is very good and the resulting land use maps can be considered of high accuracy. However, there is also the need for performing high-speed characterization using techniques that can determine forest types automatically and produce quick and acceptable results without the need for specific software. This paper presents a comprehensive methodology that uses Normalized Difference Vegetation Index NDVI data derived from the Moderate Resolution Imaging Spectroradiometer instrument MODIS aboard the TERRA satellite. The software developed automatically downloads data using Google Earth Engine and processes them using Google Colab, which are both free-access platforms. The results from the analysis were exported to ArcGIS for evaluation and comparison against the CORINE land cover inventory using the latest update 2018.... The last prediction results are compared with other models and found that this gives little more accuracy than the others. In the paper [12], the Authors reviewed the technology used for the implementation of automated weather stations is being made. In addition, the Authors also introduced the advanced computing such as IoT, Edge Computing, In-Depth Learning, Low Power WAN LPWAN, etc. using upcoming AWS-based viewing systems. ...... For the measurements of ambient temperature and humidity, a DHT22 sensor AM2302 Waveshare, Waveshare Electronics, Shenzhen, China is used, which is interfaced with the Raspberry Pi. DHT22 is a commonly used sensor in the prototyping phase of IoT Internet of Things system developments [29], which is capable of performing periodic measurements every around two seconds, which is adequate for the task. ...The emerging use of low-temperature plasma in medicine, especially in wound treatment, calls for a better way of documenting the treatment parameters. This paper describes the development of a mobile sensory device referred to as MSD that can be used during the treatment to ease the documentation of important parameters in a streamlined process. These parameters include the patient’s general information, plasma source device used in the treatment, plasma treatment time, ambient humidity and temperature. MSD was developed as a standalone Raspberry Pi-based version and attachable module version for laptops and tablets. Both versions feature a user-friendly GUI, temperature–humidity sensor, microphone, treatment report generation and export. For the logging of plasma treatment time, a sound-based plasma detection system was developed, initially for three medically certified plasma source devices kINPen MED, plasma care, and PlasmaDerm Flex. Experimental validation of the developed detection system shows accurate and reliable detection is achievable at 5 cm measurement distance in quiet and noisy environments for all devices. All in all, the developed tool is a first step to a more automated, integrated, and streamlined approach of plasma treatment documentation that can help prevent user variability.... In the same way, in the article [11] the author comments that, in Greece, there is a growing need for automated observation systems that provide scientists with the realtime data necessary to design and implement environmental policies. Therefore, this article reviews the technologies most currently used to implement weather stations, where they use the Internet of Things, Edge Computing, Deep Learning, LPWAN and more. ...Jeffry Ricaldi Cerdan Laberiano Andrade ArenasCurrently, pollution and global warming is a very controversial problem, due to the various consequences and effects it generates on health and the environment. There are studies that highlight that the main pollutants are due to human action that generates the extermination of certain ecosystems, as well as the increase in various acute and chronic diseases. The ecosystems most neglected by the authorities and the citizens are the wetlands, which can be seen reflected in the wetlands of Ventanilla, whose surface has been reduced from 1,500 to hectares due to overpopulation and contamination by the citizens themselves in recent years. These accessions endanger the extermination of the habitat of 126 birds and 27 species of native plants that inhabit a certain place, which is of great concern because these ecosystems are rapidly degrading. That is why, in the face of this problem, the design of a weather station applying the internet of things is proposed, which aims to inform the caretakers of the current state of the wetlands through a web server, where it will serve to carry out preventive actions. regarding the care of a certain ecosystem that are essential for the stabilization of CO2 emissions. This system is made up of the ESP32 platform, which will activate the emergency lights and a siren when the DHT 22, BMP180, ML8511 and MQ135 sensors detect abnormal values in temperature, humidity, atmospheric pressure, altitude, UV radiation and toxic gases.... In some cases, the low sensor cost criterion is formulated implicitly, as a low-cost assumption of the entire system Madokoro et al. [7], Shahadat et al. [19], Singh et al. [21], or as a remark that the lower cost of weather sensors directly translates into a larger number of weather stations Adityawarman and Matondang [2]. In many cases, the application of the low-cost criterion can be expected, based on the types of sensors used Mestre et al. [25], Chiba et al. [10], Nomura et al. [11], Hill et al. [13], Almalki et al. [9], Kim et al. [26], Kuo et al. [27], and Ioannou et al. [28]. ...... Low-Cost Sensors [25] stationary high resolution, stability over different weather conditions [7] mobile analysis based on literature review [19] stationary not specified [2] stationary not specified [21] stationary analysis based on literature review [22] stationary low cost [8] mobile weight, size, range, resolution, cost [9] both 1 reliability in high temperatures, energy efficiency [23] stationary low cost [20] portable not specified [26] stationary analysis based on literature review [27] stationary analysis based on literature review [28] stationary analysis based on literature review [24] stationary low cost this paper both 1 response time of a sensor in the cyber-physical subsystem, two defined factors of information accuracy 1 mobile, stationary. ...... However, modern low-cost sensors, calibrated by the manufacturers, are believed to be able to ensure getting measured data at good or, at least, sufficient accuracy. This means that in current weather stations, low-cost sensors are willingly used [2,[7][8][9][18][19][20][21][22][23][24][25][26][27][28], and was the premise for formulating the two minor criteria of sensor selection. ...Agnieszka ChodorekRobert Ryszard Chodorek Paweł SitekSmart-city management systems use information about the environment, including the current values of weather factors. The specificity of the urban sites requires a high density of weather measurement points, which forces the use of low-cost sensors. A typical problem of devices using low-cost sensors is the lack of legalization of the sensors and the resulting inaccuracy and uncertainty of measurement, which one can attempt to solve by additional sensor calibration. In this paper, we propose a different approach to this problem, the two-stage selection of sensors, carried out on the basis of both the literature pre-selection and experiments actual selection. We formulated the criteria of the sensor selection for the needs of the sources of weather information the major one, which is the fast response time of a sensor in a cyber-physical subsystem and two minor ones, which are based on the intrinsic information quality dimensions related to measurement information. These criteria were tested by using a set of twelve weather sensors from different manufacturers. Results show that the two-stage sensor selection allows us to choose the least energy consuming due to the major criterion and the most accurate due to the minor criteria set of weather sensors, and is able to replace some methods of sensor selection reported in the literature. The proposed method is, however, more versatile and can be used to select any sensors with a response time comparable to electric ones, and for the application of low-cost sensors that are not related to weather stations.... Stasiun pengamatan cuaca berfungsi pemantauan dan pengamatan cuaca dan perubahan kejadian alam berdasarkan pembacaan sensor terhadap kondisi suhu, temperatur, udara dan kelembapan suatu daerah pada kurun waktu tertentu [7]. Badan Meteorologi Klimatologi dan Geofisika BMKG sebagai institusi yang melakukan tugas pemantauan cuaca telah memiliki jaringan pengamatan cuaca secara otomatis atau Automatic Weather Station AWS yang tersebar di seluruh wilayah Indonesia [8]. ...... Stasiun pengamatan cuaca otomatis lebih dikenal dengan istilah AWS demikian juga dengan istilah di dokumen Guide to Meteorological Instruments and Methods of Observation Nomor 8 dari World Meteorological Organisation WMO [7], [9], [10], namun untuk membedakan kategori stasiun pengamatan tersebut BMKG membagi 3 tiga tipe stasiun. Stasiun pengamatan cuaca terdiri dari Automatic Rain Gauge ARG, Agroclimat Automatic Weather Station AAWS dan Automatic Weather Station AWS, dimana ketiganya hanya dibedakan dalam parameter cuaca yang diamati dan jumlah sensor yang dipasang. ...Stasiun Pengamatan Cuaca pada Badan Meteorologi Klimatologi dan Geofisika BMKG telah merapatkan jaringan stasiun pengamatan cuaca guna menghasilkan akurasi data yang lebih baik. BMKG memiliki kurang lebih 1000 dan jumlah ini masih jauh dari ideal untuk kerapatan jaringan pengamatan cuaca se-Indonesia. Stasiun pengamatan cuaca yang terbagi dalam 3 tiga type yaitu Automatic Rain Gauge ARG, Automatic Weather Station AWS dan Agroclimate Automatic Weather Station AAWS. Pemuktahiran sistem pengiriman data dari stasiun pengamat cuaca terhadap protokol pengiriman File Transfer Protocol FTP melalui modem General Packet Radio Service GPRS setiap 10 menit, dengan upgrade teknologi Internet of Things IoT perlu peninjauan terhadap kinerja operasional sistem komunikasi data. Karakteristik data yang kecil sangat cocok pada teknologi Internet of Things dengan menggunakan protokol Message Queuing Telemetry Transport MQTT guna monitoring data-data cuaca secara real-time. Berdasarkan hasil kajian dan penelitian dengan pengujian yang dilakukan terhadap metode komunikasi protokol FTP dengan protokol IoT MQTT pada stasiun AWS menggunakan analisa dengan metode PIECES Performance, Information, Economic, Control, Efisiency dan Service menunjukkan protokol MQTT yang berbasis IoT sebagai konsep komunikasi data yang tepat dimasa depan mengantikan protokol FTP... Assim, tecnologias digitais são alternativas ou complementos para análises tradicionais. Em IoT e Deep Learning há monitoramento de condições climáticas em estações meteorológicas automáticas, em tempo real, de baixo custo, com avançada transmissão de dados [15]. Uso de IoT associada a redes neurais para analisar fatores e emissões de gases poluentes dióxido e monóxido de carbono e dióxido de enxofre, a fim de reduzir efeito estufa [16]. ...... This study uses MAE, RMSE, and RMSLE to compare the performance of different models. Most studies use the above three indicators a lot for data comparison [38][39][40]. They are widely used to objectively assess the accuracy of a regression equation by analyzing differences between observations and estimates. ...Kyung-Su ChuCheong-Hyeon OhJung-Ryel ChoiByung-Sik KimIn recent years, Korea has seen abnormal changes in precipitation and temperature driven by climate change. These changes highlight the increased risks of climate disasters and rainfall damage. Even with weather forecasts providing quantitative rainfall estimates, it is still difficult to estimate the damage caused by rainfall. Damaged by rainfalls differently for inch watershed, but there is a limit to the analysis coherent to the characteristic factors of the inch watershed. It is time-consuming to analyze rainfall and runoff using hydrological models every time it rains. Therefore, in fact, many analyses rely on simple rainfall data, and in coastal basins, hydrological analysis and physical model analysis are often difficult. To address the issue in this study, watershed characteristic factors such as drainage area A, mean drainage elevation H, mean drainage slope S, drainage density D, runoff curve number CN, watershed parameter Lp, and form factor Rs etc. and hydrologic factors were collected and calculated as independent variables, and the threshold rainfall calculated by the Ministry of Land, Infrastructure and Transport MOLIT was calculated as a dependent variable and used in the machine learning technique. As for machine learning techniques, this study uses the support vector machine method SVM, the random forest method, and eXtreme Gradient Boosting XGBoost. As a result, XGBoost showed good results in performance evaluation with RMSE 20, MAE 14, and RMSLE and the threshold rainfall of the ungauged watersheds was calculated using the XGBoost technique and verified through past rainfall events and damage cases. As a result of the verification, it was confirmed that there were cases of damage in the basin where the threshold rainfall was low. If the application results of this study are used, it is judged that it is possible to accurately predict flooding-induced rainfall by calculating the threshold rainfall in the ungauged watersheds where rainfall-outflow analysis is difficult, and through this result, it is possible to prepare for areas vulnerable to flooding. To improve the quality and quantity of meteorological data over Indonesia, Meteorology Climatology and Geophysics Agency of Indonesia BMKG is continuously developing automatic weather observations. BMKG has 63 units Automatic Weather Station AWS and 165 units Automatic Weather Observation System AWOS both inside and outside the BMKG Station environment. To make the control of sensor conditions easier, especially for temperature, pressure, relative humidity, and rainfall sensors, an additional system is needed to monitor and warn when problems occur with these sensors. The correlation among weather parameters data is the key to monitoring the sensor condition, these data are going to be trained and tested with the Artificial neural network ANN method. Then, the sensor condition normal or error indicated can be well detected based on AWS’s data. The quality improvement of automatic weather station data is expected to increase the utilization of the may be subject to copyright. Discover the world's research25+ million members160+ million publication billion citationsJoin for free Journal of Physics Conference SeriesPAPER • OPEN ACCESSTemperature, pressure, relative humidity and rainfall sensors early errordetection system for automatic weather station AWS with artificialneural network ANN backpropagationTo cite this article P Wellyantama and S Soekirno 2021 J. Phys. Conf. Ser. 1816 012056View the article online for updates and content was downloaded from IP address on 09/03/2021 at 0630 Content from this work may be used under the terms of the Creative Commons Attribution licence. Any further distributionof this work must maintain attribution to the authors and the title of the work, journal citation and under licence by IOP Publishing LtdThe 10th International Conference on Theoretical and Applied Physics ICTAP2020Journal of Physics Conference Series 1816 2021 012056IOP Publishingdoi pressure, relative humidity and rainfall sensors early error detection system for automatic weather station AWS with artificial neural network ANN backpropagation P Wellyantama1 and S Soekirno1 1Physics Department, University of Indonesia, Depok, West Java, Indonesia E-mail pradawellyantama Abstract. To improve the quality and quantity of meteorological data over Indonesia, Meteorology Climatology and Geophysics Agency of Indonesia BMKG is continuously developing automatic weather observations. BMKG has 63 units Automatic Weather Station AWS and 165 units Automatic Weather Observation System AWOS both inside and outside the BMKG Station environment. To make the control of sensor conditions easier, especially for temperature, pressure, relative humidity, and rainfall sensors, an additional system is needed to monitor and warn when problems occur with these sensors. The correlation among weather parameters data is the key to monitoring the sensor condition, these data are going to be trained and tested with the Artificial neural network ANN method. Then, the sensor condition normal or error indicated can be well detected based on AWS’s data. The quality improvement of automatic weather station data is expected to increase the utilization of the data. 1. Introduction Indonesia is a very large archipelago country with an area of about km2, Indonesia has 17,508 islands and a long coastline of about 81,000 km [1]. In Indonesia, weather information has an important role both, to plan and to operate daily life in various sectors. From the construction development, economy, social, transportation, tourism, health, etc. In the construction development sector for buildings, airports and ports require information about wind direction, wind speed, and tides, in the economic sector, the analysis of inflation in a region requires information on wave height, the tourism sector requires weather forecast data, temperature, humidity, wave height, and the land, sea, and air transportation sector requires weather information data, air pressure, wave height, and significant weather maps. The Meteorology Climatology and Geophysics Agency BMKG has 183 Meteorological Stations that observe and provide weather information spread across Indonesia. Weather observations are carried out manually or by using human power to observe weather parameters using conventional weather instruments and there are also automatic observations using digital weather instruments. Of the 183 meteorological stations, 62 use fully automatic observation, and the rest use a conventional instrument. BMKG has 63 units meteorological AWS automatic weather station and 165 units AWOS automatic weather observation system spread throughout Indonesia, both inside and outside of the Meteorological Station zone. Some digital instruments usually unnoticed if there is a problem with the values generated by the sensor, if they are not compared to other instruments or if there is no event that validates the value. This makes the The 10th International Conference on Theoretical and Applied Physics ICTAP2020Journal of Physics Conference Series 1816 2021 012056IOP Publishingdoi process plays an important role to maintain data quality. BMKG always calibrates the equipment every 6 months, but between the 6 months it does not rule out the possibility of potential problems in measuring values, especially for electronic or digital equipment. The eligibility conditions for meteorological instruments adhere to the regulations of the World Meteorological Organization WMO CIMO of 2014, where the measurement tolerances are 1 temperature maximum of 2 humidity maximum 3%, 3 air pressure maximum of hPa, 3 maximum wind speed of m/s, 4 wind direction maximum 5o, 5 rainfall maximum 5%, 6 sun radiation maximum 5%. To make the control of sensor conditions easier, especially temperature, pressure, humidity, and rainfall sensors, we need a system that can monitor and detect when problems occur with these sensors. The correlation among weather parameters is the key to controlling the sensor conditions to be trained and tested using the ANN backpropagation method. This ANN system design works by learning the correlation and pattern of each sensor data during the training phase. In the testing phase, the condition of the test data will be predicted. If any sensor outputs a value that is unusual or different from the pattern studied by ANN, the system will give a warning indicating sensor failure. With better quality weather observation data, it will improve the quality of providing weather information, so that the use of weather information becomes more accurate and useful. In a study [2] entitled Machine Learning-Based Calibration of Low-Cost Air Temperature Sensors Using Environmental Data, and research [3] entitled Temperature error correction based on BP neural network in meteorological wireless sensor network, they tried to approach a calibration using software and models, but only limited to the temperature sensor. In this study, we try to do the same approach, but for more sensors. The next approach to sensor error detection is studied based on the correlation pattern among sensors, this was done in a study [4] entitled Soft Sensors for Instrument Fault Accommodation in Semiactive Motorcycle Suspension Systems. The detection of a condition in classification has been carried out in a research conducted by [5] entitled Intelligent Multi-Sensor Control Device for Recognition of Gas-Air Mixture Samples with the Use of Artificial Neural Networks, which classifies and detection odors with electronic noses using ANN. From the researches above, the ANN model has good results, so this paper will try to apply the ANN-BP method for an early detection approach for error indication of more than one sensor on AWS in a result that is classified as error or normal. 2. Method Figure1. Schematic of the AWS sensor condition early detection system. The 10th International Conference on Theoretical and Applied Physics ICTAP2020Journal of Physics Conference Series 1816 2021 012056IOP Publishingdoi design of the early detection system begins with the design of the ANN backpropagation model, the model is built with pattern recognition in training data on observations of weather parameters, temperature, humidity, pressure, and rain at Tanjung Priok Maritime Station for 4 years, from 2017 until 2020. The data training is carried out using Rstudio software. The composition of data for training is 80% data. The training is carried out so that the network can recognize the patterns generated from the input and output pairs. The data input consists of weather parameters, temperature, humidity, pressure, and rain, and the output is a label of the sensor's condition, normal or an error indication. After the model produces the best accuracy in training and testing data, then the ANN model is used to estimate and to detect the condition of the AWS sensors, especially pressure, temperature, humidity, and rain sensors. The details of the research steps are Preprocessing data Before the data was processed using ANN, the data were compiled and conditioned, with a composition of ± 50% actual data and ± 50% in the form of synthetic data. The synthetic data mean the actual data that has been added and subtracted in value according to WMO CIMO regulation 2014 to obtain data in the form of damaged sensor label values. Figure 2. AWS Tanjung Priok. ANN Design ANN design is done by determining the amount of input data used in training, the number of hidden layers used and the number of outputs desired. The data used as input are temperature, humidity, pressure, and rain observation data at the Tanjung Priok Maritime Meteorological Station from 2017 to 2020, with details of the network architecture as follows Figure 3. ANN architecture of temperature and humidity. The 10th International Conference on Theoretical and Applied Physics ICTAP2020Journal of Physics Conference Series 1816 2021 012056IOP Publishingdoi 4. ANN architecture Air pressure. Figure 5. ANN rainfall architecture. Figure 6. Research algorithm. The 10th International Conference on Theoretical and Applied Physics ICTAP2020Journal of Physics Conference Series 1816 2021 012056IOP Publishingdoi Pattern Recognition training. In the training process, the maritime meteorological station’s conventional weather observation data for 4 years are arranged into 2 output conditions, namely 1 Output conditions "sensor in normal conditions", where all input values are the original values of weather observations for the past 4 years. 2 The output condition is "problematic sensor", where all input values are added and also subtracted from the value that exceeds the tolerance limits of the CIMO World Meteorological Organization WMO No. 8 of 2014, where the measurement tolerance is as follows a temperature maximum b Humidity maximum 3%, c Air pressure maximum of hPa , d Rainfall maximum 5%. The input and output data during the training are in the form of 1 Input temperature, humidity, and pressure data, the output temperature sensor label indication is damaged or normal, 2 Input temperature, humidity, and pressure data, the output humidity sensor label indicates damaged or normal . 3 Temperature, humidity, and pressure data input, the output pressure indication is damaged or normal. 4 Temperature, humidity, and rain data input, output rain label indication of damage or normal in all rain categories except 1-3mm rain which has additional pressure data input. Testing and estimation Data testing is carried out aimed to determine whether the network can recognize patterns of training data from the input data provided. If the resulting error value has reached the target, the resulting output can be used as estimation data. The model validation value is obtained from the accuracy coefficient with the following value interpretation Table 1. The relation between accuracy coefficient and interpretation [6] - 20 % - % - % - % - 100 % Very low Low Moderate High Very high The estimation is done after the pattern recognition process is carried out by the network when the training is complete and the model has been tested with good accuracy values. Input data consist of AWS Tanjung Priok’s temperature, humidity, pressure, and rain data and the output is a classification of sensor conditions a Normal, or b The temperature sensor is indicated as damaged, or c The humidity sensor is indicated as damaged, or d The pressure sensor is indicated as damaged, or e The rain sensor is indicated to be damaged. 3. Result and Discussion Test result Temperature Sensor. After the data training was carried out, then testing was carried out with the remaining 20% of the data, with the target data being the previously known sensor conditions. In the testing temperature sensor conditions, obtained a very high accuracy value is 99%, false negative prediction is “normal”, which it should “error indication” value is and false positive prediction is “error indication”, which it should “normal” value is with the graph of the independent variable contribution as follows The 10th International Conference on Theoretical and Applied Physics ICTAP2020Journal of Physics Conference Series 1816 2021 012056IOP Publishingdoi 7. Contribution of the independent variable, temperature sensor label output. The figure above shows the intensity of the contribution of the independent variable in training and testing for the output temperature sensor condition label, where the highest contribution is the value of the temperature sensor itself. Humidity Sensors. After the data training was carried out, testing was carried out with the remaining 20% of the data, with the target data being in the form of previously known conditions. In testing the temperature sensor conditions, a very high accuracy value was obtained false negative prediction is “normal”, which it should “error indication” value is and false positive prediction is “error indication”, which it should “normal” of with a graph of the independent variable contribution as follows Figure 8. Contribution of the independent variable, humidity sensor label output. The figure above shows the intensity of the contribution of the independent variable in training and testing for the output humidity sensor condition label, where the highest contribution is the value of the humidity sensor itself. Pressure Sensor. After the data training was carried out, testing was carried out with the remaining 20% of the data, with the target data being in the form of previously known conditions. In testing the temperature sensor conditions, obtained a very high accuracy value of 100%, false negative prediction is “normal”, which it should “error indication” value is 0% and false positive prediction is “error The 10th International Conference on Theoretical and Applied Physics ICTAP2020Journal of Physics Conference Series 1816 2021 012056IOP Publishingdoi which it should “normal” value is 0%, with a contribution graph independent variable as follows Figure 9. Contribution of the independent variable, pressure sensor label output. The figure above shows the intensity of the contribution of the independent variable in training and testing for the output Pressure sensor condition label, where the highest contribution is the value of the Pressure sensor itself. Rain Sensor. After the data training was carried out, testing was carried out with the remaining 20% of the data, with the target data being in the form of previously known conditions. In testing the temperature sensor conditions, obtained a very high accuracy value on average of 82%, an average false negative prediction is “normal”, which it should “error indication” value is and false positive prediction is “error indication”, which it should “normal” value is with details a Rainfall 1-3 mm, the test accuracy is 77%, false-negative and false-positive b Rainfall 3-20 mm testing accuracy is 82%, false-negative 0%, and false-positive c Rainfall 20-50 mm has 82% accuracy testing, 0% false-negative and false-positive. d Rainfall above 50 mm has 91% accuracy testing, false-negative 0%, and false-positive With the graph of the independent variable contribution as follows Figure 10. Contribution of the independent variable, 1-3mm, and 3-20mm rain sensor label output. The 10th International Conference on Theoretical and Applied Physics ICTAP2020Journal of Physics Conference Series 1816 2021 012056IOP Publishingdoi 11. Contribution of the independent variable, rain sensor label output 20-50mm and> 50mm. The Figure above shows the intensity of the contribution of the independent variable in training and testing for the output rain sensor condition label, where the highest contribution is the value of the rain sensor itself. Estimation Results After the training and data testing process, based on the high accuracy results above, the sensor condition estimation process is carried out. The data to be estimated is the latest AWS Tanjung Priok data on October 16 - 18, 2020 with the following results Table 2. The estimation results of the AWS Tanjung Priok sensor condition label. Estimated of sensor condition labels Error Indication for Pressure sensor Error Indication for Pressure sensor The 10th International Conference on Theoretical and Applied Physics ICTAP2020Journal of Physics Conference Series 1816 2021 012056IOP Publishingdoi on the model obtained from training and tested with previous data, and used to estimate the AWS Tanjung Priok sensor data for 16-18 October 2020, it was found that almost all were in normal condition, 2 conditions indicated that the pressure sensor had an error, on October 17 at and UTC, which can be seen at those 2 times the pressure value suddenly decreased significantly, but other weather parameters were still in conditions not much different from the previous time. 4. Conclusion The sensor condition, especially temperature, humidity, pressure, and rain on AWS Tanjung Priok can be estimated using the ANN backpropagation method, where the accuracy results between the model output and the target during training and testing show very high values. Based on this model, the estimation results of the AWS Tanjung Priok sensor conditions on 16-18 October 2020 are almost all in normal conditions, 2 conditions indicated that the pressure sensor had an error, on October 17 at and UTC, this can be seen at the 2 times the pressure value decreased significantly, but other weather parameters are still not much different from the previous time. Based on the results of this estimation, it is hoped that it can serve as a warning to the nearest Maritime Meteorological Station so that checks can be carried out as soon as possible and if damage occurs, replacement or repair of sensor hardware can be carried out so that the quality of AWS data can always be maintained. Acknowledgment This research was supported by the grant of PITTA Publikasi Internasional Terindeks Untuk Tugas Akhir Mahasiswa of Universitas Indonesia under the contract number NKB-1005/ We would like to acknowledge the Indonesian Agency for Meteorology Climatology and Geophysics for supporting data and facilities. References [1] Dahuri R 2004 Pengelolaan Sumber Daya Wilayah Pesisir dan Lautan Secara Terpadu, Edisi Revisi Jakarta Pradnya Paramita [2] Yamamoto K, Togami T, Yamaguchi N, Ninomiya S 2017 Machine Learning-Based Calibration of Low-Cost Air Temperature Sensors Using Environmental Data. Sensors 176 1290 [3] Wang B 2017 Temperature error correction based on BP neural network in meteorological wireless sensor network. Int. J. Sensor Networks 234 [4] Capriglione D, et al 2020 Soft Sensors for Instrument Fault Accommodation in Semiactive Motorcycle Suspension Systems IEEE transactions on instrumentation and measurement 69 5 [5] Kulagin V P, et al 2017 Intelligent Multi-Sensor Control Device for Recognition of Gas-Air Mixture Samples with the Use of Artificial Neural Networks IEEE [6] Sugiyono 2008 Metode Penelitian Kunatitatif Kualitatif dan R&D Bandung Alfabeta ... Artificial Neural Networks ANNs are frequently used in meteorology science CIE and cloud classification [40,41], solar irradiance and wind speed forecasting [42][43][44][45][46][47], atmospheric pollution distribution [48,49], and rainfall [50,51]. ANN classification models serve to classify input information into certain categories or targets. ...Digital sky images are studied for the definition of sky conditions in accordance with the CIE Standard General Sky Guide. Likewise, adequate image-processing methods are analyzed that highlight key image information, prior to the application of Artificial Neural Network classification algorithms. Twenty-two image-processing methods are reviewed and applied to a broad and unbiased dataset of 1500 sky images recorded in Burgos, Spain, over an extensive experimental campaign. The dataset comprises one hundred images of each CIE standard sky type, previously classified from simultaneous sky scanner data. Color spaces, spectral features, and texture filters image-processing methods are applied. While the use of the traditional RGB color space for image-processing yielded good results ANN accuracy equal to other color spaces, such as Hue Saturation Value HSV, which may be more appropriate, increased the accuracy of their global classifications. The use of either the green or the blue monochromatic channels improved sky classification, both for the fifteen CIE standard sky types and for simpler classification into clear, partial, and overcast conditions. The main conclusion was that specific image-processing methods could improve ANN-algorithm accuracy, depending on the image information required for the classification WangZhi DengKe XuTao LiuIn recent years, meteorological environment has become a topic of concern to people. Various meteorological disasters threaten human life and production. Accurate and timely acquisition of meteorological data has become a prerequisite for dealing with various aspects of production and life, and also laid a foundation for weather prediction. For a long time, meteorological data acquisition system combined with modern information technology has gradually become a hot spot in the field of meteorological monitoring and computer research. The continuous development of NB-IoT technology has brought new elements to the research of meteorological monitoring system. This paper designs a weather station system based on NB-IoT, including data acquisition module, main controller module, NB-IoT wireless communication module, energy capture module, low power consumption scheme, measurement of air temperature is strongly influenced by environmental factors such as solar radiation, humidity, wind speed and rainfall. This is problematic in low-cost air temperature sensors, which lack a radiation shield or a forced aspiration system, exposing them to direct sunlight and condensation. In this study, we developed a machine learning-based calibration method for air temperature measurement by a low-cost sensor. An artificial neural network ANN was used to balance the effect of multiple environmental factors on the measurements. Data were collected over 305 days, at three different locations in Japan, and used to evaluate the performance of the approach. Data collected at the same location and at different locations were used for training and testing, and the former was also used for k-fold cross-validation, demonstrating an average improvement in mean absolute error MAE from to by applying our method. Some calibration failures were noted, due to abrupt changes in environmental conditions such as solar radiation or rainfall. The MAE was shown to decrease even when the data collected in different nearby locations were used for training and testing. However, the results also showed that negative effects arose when data obtained from widely-separated locations were used, because of the significant environmental differences between paper describes the development and experimental verification of an Instrument Fault Accommodation IFA scheme for front and rear suspension stroke sensors in motorcycles equipped with electronic controlled semi-active suspension systems. In particular, the IFA scheme is based on the use of Nonlinear Auto-Regressive with eXogenous inputs NARX Neural Networks NN employed as soft sensors for feeding the suspension control strategy back with measurement even in presence of faults occurred on the sensors. Different NN architectures have been trained and tuned by considering real data acquired during several measurement campaigns. The performance has been compared with that of the well-known Half-Car Model HCM. Very satisfying results allow the Soft sensor to be really integrated into fault-tolerant control systems. In experimental road tests an implementation of the proposed IFA scheme on a low-cost microcontroller for automotive applications, showed to be in real-time. In the paper these experimental results are shown to prove the good performance of the IFA scheme in different motorcycle operating conditions. Baowei WangXiaodu GuLi MaShuangshuang YanUsing meteorological wireless sensor network WSN to monitor the air temperature AT can greatly reduce the costs of monitoring. And it has the characteristics of easy deployment and high mobility. But low cost sensor is easily affected by external environment, often leading to inaccurate measurements. Previous research has shown that there is a close relationship between AT and solar radiation SR. Therefore, We designed a back propagation BP neural network model using SR as the input parameter to establish the relationship between SR and AT error ATE with all the data in May. Then we used the trained BP model to correct the errors in other months. We evaluated the performance on the datasets in previous research and then compared the maximum absolute error, mean absolute error and standard deviation respectively. The experimental results show that our method achieves competitive performance. It proves that BP neural network is very suitable for solving this problem due to its powerful functions of non-linear fitting.