Abstract

The problem of reservoir inflow forecasting plays a critical role in reservoir management. However, reservoir inflow forecasting must be necessarily accurate and timely. This paper presents practical machine learning (ML) technique and the optimal lead time for reservoir inflow forecasting. The two well-knows ML: Support Vector Regression (SVR) and Random Forests (RF), were used to predict water inflow volume into the reservoir. Both methods will improve the efficiency of the reservoir inflow more accurately by using reservoir information data (amounts of rainfall and inflow) and climate indices (Sea Surface Temperatures (SSTs) and ocean indices). All historical monthly time series reservoir information for 18 years (between 1998 and 2015) is collected from the Hui Nam Sai Reservoir Project, located in Nakhon Si Thammarat Province, Thailand. Our experimental results showed Random Forest with climate indices gave the best performance method. We also found that the top three optimal lead time for reservoir inflow forecasting were the 10-month (t+10), 2-month (t+2) and 6-month (t+6) ahead with giving the least of RMSE of 4.41, 4.58 and 4.62, respectively. Similarly, when using ASE as a criterion, the top three optimal lead time for reservoir inflow forecasting were the 3-month (t+3), 10-month (t+10), and 5-month (t+5) with giving the least of ASE of 3.11, 3.14, and 3.16 respectively.