Garuda - Garba Rujukan Digital

Predictive Maintenance Automatic Weather Station Sensor Error Detection using Long Short-Term Memory Santoso, Bayu; Ryan, Muhammad; Wicaksana, Haryas Subyantara; Ananda, Naufal; Budiawan, Irvan; Mukhlish, Faqihza; Kurniadi, Deddy
Ultima Computing : Jurnal Sistem Komputer Vol 15 No 2 (2023): Ultima Computing : Jurnal Sistem Komputer
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/sk.v15i2.3403

Weather information plays a crucial role in various sectors due to Indonesia's wide range of weather and extreme climate phenomena. Automatic Weather Stations (AWS) are automated equipment designed to measure and collect meteorological parameters such as atmospheric pressure, rainfall, relative humidity, atmospheric temperature, wind speed, and wind direction. Occasionally, AWS sensors may produce erroneous values without the technicians' awareness. This study aims to develop sensors error detection system for predictive maintenance on AWS using the Long Short-Term Memory (LSTM) model. The AWS dataset from Jatiwangi, West Java, covering the period from 2017 to 2021, will be utilized in the study. The study revolves around developing and testing four distinct LSTM models dedicated to each sensor: RR, TT, RH, and PP. The research methodology involves a phased approach, encompassing model training on 70% of the available dataset, subsequent validation using 25% of the data, and finally, testing on 5% of the dataset alongside the calibration dataset. Research outcomes demonstrate notably high accuracy, exceeding 90% for the RR, TT, and PP models, while the RH model achieves above 85%. Moreover, the research highlights that Probability of Detection (POD) values generally trend high, surpassing 0.8, with a low False Alarm Rate (FAR), typically below 0.1, except for the RH model. Sensor condition requirements will adhere to the rules set by World Meteorological Organization (WMO) and adhere to the permitted tolerance limits for each weather parameter.

Design of drought early warning system based on standardized precipitation index prediction using hybrid ARIMA-MLP in Banten province Soekirno, Santoso; Ananda, Naufal; Wicaksana, Haryas Subyantara; Yulizar, David; Prabowo, Muhammad Agung; Adi, Suko Prayitno; Santoso, Bayu
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1878-1887

Drought Early Warning System (DEWS) is an effort to disseminate early warning information based on climate and hydrology aspects. The DEWS design uses ARIMA, MLP, and hybrid ARIMA-MLP models to predict drought based on SPI for 1, 3, and 6 months. Predictions were made using ERA5 monthly rainfall data from 1981-2022 corrected based on observation data on 9 grids of observation rain gauges in Banten Province. The design of the ARIMA model is determined by selecting the combination of p and q parameters with the lowest AIC value, while the MLP architecture is determined by referring to the study literature and by trial and error testing. ARIMA models and hybrid models are not able to follow actual data fluctuations and have high error values in both SPI1, SPI3, and SPI6, so they are not recommended in this study. The MLP model has the best prediction ability, namely in SPI6 prediction with NSE value reaching >0.5 and RMSE value.

Predictive Maintenance Automatic Weather Station Sensor Error Detection using Long Short-Term Memory Santoso, Bayu; Ryan, Muhammad; Wicaksana, Haryas Subyantara; Ananda, Naufal; Budiawan, Irvan; Mukhlish, Faqihza; Kurniadi, Deddy
ULTIMA Computing Vol 15 No 2 (2023): Ultima Computing : Jurnal Sistem Komputer
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/sk.v15i2.3403

Weather information plays a crucial role in various sectors due to Indonesia's wide range of weather and extreme climate phenomena. Automatic Weather Stations (AWS) are automated equipment designed to measure and collect meteorological parameters such as atmospheric pressure, rainfall, relative humidity, atmospheric temperature, wind speed, and wind direction. Occasionally, AWS sensors may produce erroneous values without the technicians' awareness. This study aims to develop sensors error detection system for predictive maintenance on AWS using the Long Short-Term Memory (LSTM) model. The AWS dataset from Jatiwangi, West Java, covering the period from 2017 to 2021, will be utilized in the study. The study revolves around developing and testing four distinct LSTM models dedicated to each sensor: RR, TT, RH, and PP. The research methodology involves a phased approach, encompassing model training on 70% of the available dataset, subsequent validation using 25% of the data, and finally, testing on 5% of the dataset alongside the calibration dataset. Research outcomes demonstrate notably high accuracy, exceeding 90% for the RR, TT, and PP models, while the RH model achieves above 85%. Moreover, the research highlights that Probability of Detection (POD) values generally trend high, surpassing 0.8, with a low False Alarm Rate (FAR), typically below 0.1, except for the RH model. Sensor condition requirements will adhere to the rules set by World Meteorological Organization (WMO) and adhere to the permitted tolerance limits for each weather parameter.

Multivariate Imputation Chained Equation on Solar Radiation in Automatic Weather Station Akbar, Gema; Prajitno, Prawito; Ariffudin; Ananda, Naufal
Jurnal Penelitian Pendidikan IPA Vol 10 No 7 (2024): July
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v10i7.7679

Solar radiation is one of the crucial weather observation variables Its variable has a role in renewable energy solutions, agriculture, meteorology, and hydrology. AWS is one of instrument that use to observing weather especially solar radiation. AWS has a pyranometer sensor used to measure solar radiation. Unfortunately, the instrument has problem like the igh cost of supplying, installing, maintaining, and calibrating the equipment. Due to this, there is a lot of empty data, and the actual data cannot be properly measured. Imputation of solar radiation data using MICE algorithm can be solution. This study using BLR, NRR and RFR estimator to estimating solar radiation data. AWS Staklim Banten as target and other AWS as input. The period from January 1, 2018 - February 12, 2024. The performance evaluation of the solar radiation imputation estimator is still according to WMO operational requirements for solar radiation measurements, which can be seen from the resulting MAPE value < 8%.

Evaluasi Spasial Estimasi Curah Hujan pada Radar Cuaca Menggunakan Metode Z-R Marshall-Palmer di Wilayah Jawa Barat Ananda, Naufal; Mukhlish, Faqihza; Wicaksana, Haryas Subyantara; Budiawan, Irvan
Jurnal Otomasi Kontrol dan Instrumentasi Vol 16 No 1 (2024): Jurnal Otomasi Kontrol dan Instrumentasi
Publisher : Pusat Teknologi Instrumentasi dan Otomasi (PTIO) - Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/joki.2024.16.1.4

Rainfall is one of the weather parameters that affect various sectors. High rainfall intensity can trigger hydrometeorological disasters, so rainfall observation data is vital to monitor rainfall conditions in an area. An automatic rain gauge is an instrument that measures rainfall at an observation point, but the instrument has reasonably low coverage and has yet to reach the entire region. Weather radar is a remote sensing instrument capable of spatially estimating rainfall. Weather radar data can be used to estimate rainfall using the Marshall-Palmer Z-R method. The application of the method can be an alternative for areas that do not have rainfall observation equipment. However, the estimation needs to be evaluated to improve the accuracy of the estimation value. Based on the evaluation, the highest coefficient of determination was 0.92, and the lowest was 0.67. The lowest RMSE value was 2.40, the highest was 6.76, the highest ME value was 16.59, and the lowest was 5.93; the highest bias was 12.90, and the lowest was 5.30. The study results show that the weather radar can operate according to the specifications of the maximum observation distance of up to 220 KM, but the farther the observation distance to a point, the higher the performance of rainfall estimation accuracy.

Estimasi Kecepatan Angin Permukaan pada Jaringan Anemometer Menggunakan Temporal Convolutional Network Wicaksana, Haryas; Mukhlish, Faqihza; Ananda, Naufal; Budiawan, Irvan; Khamdi, Arif Nur; Habib , Abdul Hamid Al
Jurnal Otomasi Kontrol dan Instrumentasi Vol 16 No 1 (2024): Jurnal Otomasi Kontrol dan Instrumentasi
Publisher : Pusat Teknologi Instrumentasi dan Otomasi (PTIO) - Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/joki.2024.16.1.5

Surface winds in various locations are measured simultaneously using a multisite anemometer network. This network is susceptible to system failures due to sensor damage, causing a data gap during sensor removal and reinstallation. This research develops a wind speed estimation model on a multisite anemometer using the Temporal Convolutional Network (TCN) algorithm. TCN processes time domain signals in parallel, thus significantly cutting the computation time. Minutely wind speed data set was obtained from four anemometers at Juanda International Airport in Surabaya from January 1, 2022 – December 24, 2023. The model design comprises data pre-processing, dominant wind direction analysis, hyperparameter determination, training, and testing on actual data. TCN estimation models are divided into easterly, westerly, transitional, and all-directional models. These wind speed estimation models strongly correlate with actual data, with correlation coefficients of 0.70, 0.77, and 0.87. Overall, the accuracy of the TCN-based estimation model conforms to World Meteorological Organization (WMO) requirements for wind speed measurements. It achieves RMSE<5 m/s and MAE<3 m/s. As for computation duration, TCN processes the training for 87 seconds per epoch and completes the estimation in 37 seconds, much faster than CNN-BiDLSTM’'s training duration of 2206 seconds per epoch and estimation completion of 548 seconds.

Title

Found 6 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 6 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 6 Documents
Search