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This article presents the application of genetic programming for forecasting water level and river discharge, which is the most important task for water resources management. GPdotNET Version 4 as a free open source software was used in this study. The daily data of rainfall were collected at TD12 upstream station, located Thung Yai district, Nakhon Si Thammarat province, and the daily data of rainfall, water level, and river discharge were collected at TD07 upstream station, located Phra Saeng district, Surat Thani province for 5 years during 2013 and 2017 in order to forecast water level and river discharge at TD07 one day ahead. Those data were divided into two data sets, i.e. the first 70 % for training and the rest 30 % for testing. To evaluate the model performance, three statistical indices were analyzed, i.e. correlation coefficient (r), root mean squared error (RMSE), and mean absolute error (MAE). The study results were found that 1 day ahead water level forecasting gave the excellent performance with the values of r, RMSE, and MAE for training and testing processes as 0.988, 0.307 m, 0.127 m and 0.984, 0.321 m, 0.144 m, respectively. And 1 day ahead river discharge gave the excellent performance with the values of r, RMSE, and MAE for training and testing processes as 0.986, 22.429 m³/s, 9.927 m³/s and 0.982, 21.794 m³/s, 10.514 m³/s, respectively.
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