TY - GEN
T1 - Application of Machine Learning in Daily Reservoir Inflow Prediction of the Bhumibol Dam, Thailand
AU - Kraisangka, Jidapa
AU - Rittima, Areeya
AU - Sawangphol, Wudhichart
AU - Phankamolsil, Yutthana
AU - Tabucanon, Allan Sriratana
AU - Talaluxmana, Yutthana
AU - Vudhivanich, Varawoot
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - To mitigate floods and droughts in Thailand, the reservoir operations need accurate and reliable hydro-parameter information, e.g., inflow, to support decision making. In this paper, we explore and develop the predictive models for predicting the next-day inflow of the Bhumibol Dam, one of the major reservoirs of Thailand. We applied the machine learning techniques including decision tree, support vector regression, random forest, and extreme gradient boosting (XGBoost). Daily reservoir and climate Data from 2000 to 2021 were used in the analysis. After the series of experiments of model development, we finalize the model with the random forest algorithm having the best performance of MAE=4.232, MSE=83.823, and R2=0.867. However, we believe that the models and the feature sets can be further explored and developed to achieve the better accuracy. As a result, we could practically incorporate the inflow prediction model to aid decision making in the reservoir operation.
AB - To mitigate floods and droughts in Thailand, the reservoir operations need accurate and reliable hydro-parameter information, e.g., inflow, to support decision making. In this paper, we explore and develop the predictive models for predicting the next-day inflow of the Bhumibol Dam, one of the major reservoirs of Thailand. We applied the machine learning techniques including decision tree, support vector regression, random forest, and extreme gradient boosting (XGBoost). Daily reservoir and climate Data from 2000 to 2021 were used in the analysis. After the series of experiments of model development, we finalize the model with the random forest algorithm having the best performance of MAE=4.232, MSE=83.823, and R2=0.867. However, we believe that the models and the feature sets can be further explored and developed to achieve the better accuracy. As a result, we could practically incorporate the inflow prediction model to aid decision making in the reservoir operation.
KW - Bhumibol Dam
KW - Daily inflow prediction
KW - Machine learning
KW - Reservoir
UR - http://www.scopus.com/inward/record.url?scp=85133348812&partnerID=8YFLogxK
U2 - 10.1109/ECTI-CON54298.2022.9795552
DO - 10.1109/ECTI-CON54298.2022.9795552
M3 - Conference contribution
AN - SCOPUS:85133348812
T3 - 19th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2022
BT - 19th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2022
Y2 - 24 May 2022 through 27 May 2022
ER -
