TY - GEN
T1 - Rainfall Estimation Using Machine Learning Approaches with Raingauge, Radar, and Satellite Data
AU - Putra, Maulana
AU - Rosid, Mohammad Syamsu
AU - Handoko, Djati
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Rainfall is an essential variable in studying weather and has a vital role in the hydrological cycle. Therefore, accurate rainfall information becomes an urgent need. Machine learning approaches have also shown remarkable developments in various areas of life including rainfall estimation. This research combined rain gauge, radar and satellite data with several algorithms such as Decision Tree, Random Forest, Adaptive Boosting, and Gradient Boosting. Interestingly, it was found that the Gradient Boosting learning ensemble algorithm is best applied in rainfall estimation, with a correlation value (R2) is 0.740 and RMSE value is 1,765. On the other hand, weather radar reflectivity data significantly influences the integration of the data built into the model, suggesting that details of optimizing data are significantly required.
AB - Rainfall is an essential variable in studying weather and has a vital role in the hydrological cycle. Therefore, accurate rainfall information becomes an urgent need. Machine learning approaches have also shown remarkable developments in various areas of life including rainfall estimation. This research combined rain gauge, radar and satellite data with several algorithms such as Decision Tree, Random Forest, Adaptive Boosting, and Gradient Boosting. Interestingly, it was found that the Gradient Boosting learning ensemble algorithm is best applied in rainfall estimation, with a correlation value (R2) is 0.740 and RMSE value is 1,765. On the other hand, weather radar reflectivity data significantly influences the integration of the data built into the model, suggesting that details of optimizing data are significantly required.
KW - estimation
KW - gradient boosting
KW - machine learning
KW - rainfall
UR - http://www.scopus.com/inward/record.url?scp=85142072610&partnerID=8YFLogxK
U2 - 10.1109/ICELTICs56128.2022.9932109
DO - 10.1109/ICELTICs56128.2022.9932109
M3 - Conference contribution
AN - SCOPUS:85142072610
T3 - Proceedings of the International Conference on Electrical Engineering and Informatics
SP - 25
EP - 30
BT - 2022 International Conference on Electrical Engineering and Informatics, ICELTICs 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 International Conference on Electrical Engineering and Informatics, ICELTICs 2022
Y2 - 27 September 2022 through 28 September 2022
ER -