Artificial Neural Networks and Regression Modeling for Water Resources Management in the Upper Indus Basin

Abstract

A flood is a natural disaster. Heavy rainfall and overflow frequently cause enclosed land areas to fill with water, resulting in considerable loss of human life and property, including damage to buildings, bridges, electric supply networks, and transportation, and economic concern. This work was carried out in the Upper Indus Basin (UIB). We developed an artificial intelligence model for forecasting the flood events in this study. Long-short term memory (LSTM) and seasonal auto-regressive integrated moving average (SARIMA) were used in this study to forecast flood events. This study used a dataset from 1971–2009 and divided it into training, testing, and forecasting from 1971–2004, 2005–2009, and 2010–2014, respectively. The best statistical analysis result was observed with the LSTM model, which documented the value of root mean squared error (RMSE) at 22.79 and 35.05 for training and testing, respectively. Hence, the results of the study highlight that the LSTM model was the most suitable among the artificial neural networks for flood event forecasts. This current study will help in the forecasting of high storms for effective water resources management.

Keywords

• Upper Indus Basin
• flood forecasting
• LSTM
• SARIMA
• water resources management