Multiple linear regression and long short-term memory for evaluating water levels in irrigation and drainage systems: an application in the Bac Hung Hai irrigation and drainage system, Vietnam
2022
Chien Pham Van | Doanh Nguyen-Ngoc
In this context, multiple linear regression (MLR) and long short-term memory (LSTM) are presented to evaluate water levels in irrigation and drainage systems based on the available water levels at inlet and outlet locations. The Bac Hung Hai irrigation and drainage system is chosen as an example for demonstrating the MLR and LSTM models. Six statistical metrics including root mean square error (RMSE), mean absolute error (MAE), mean error (ME), Willmott's score (WS), Pearson's correlation coefficient (r), and Nash–Sutcliffe efficiency (NSE) are implemented for quantitatively assessing the agreement between estimated and observed water levels at 12 locations of interest within the system in the period from 2000 to 2021 (with an interval time of 6 hours). The results showed that MLR and LSTM models can be used for evaluating water levels with high accuracy. The values of dimensional statistical errors equal only about 6% of the maximum water level monitoring at the locations of interest for both MLR and LSTM models. The values of dimensionless statistical errors range from 0.76 to 0.99 for all 12 locations of interest in the studied system. In addition, both models are benchmarked and could be used for other agricultural systems. HIGHLIGHTS Multiple linear regression and long short-term memory models are presented for evaluating water levels in irrigation and drainage systems.; Both multiple linear regression and long short-term memory model reproduced well the observed water levels.; The LSTM model proved to be a more suitable approach than the multiple linear regression for evaluating water levels at different locations of interest.;
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