Water demand in watershed forecasting using a hybrid model based on autoregressive moving average and deep neural networks

Increasing water demand is exacerbating water shortages in water-scarce regions (such as India, China, and Iran). Effective water demand forecasting is essential for the sustainable management of water supply systems in watersheds. To alleviate the contradiction between water supply and demand in the basin, with water demand for economic growth as the main target, a hybrid moving autoregressive and deep neural network model (ARMA-DNN) was developed in this study, and four commonly used statistical indicators (MAE, RMSE, MSE, and R-2) were selected to evaluate the performance of the model. Finally, the validity and practicality of the model were verified by taking the Minjiang River basin in China as an example. The results show that (a) the model can predict future water demand more accurately under the conditions of actual water consumption changes, (b) the ideal agricultural production in the Minjiang River Basin is predicted to be reached 2.26 x-10(9)t in 2021, and (c) the highest industrial economic efficiency in Chengdu is 1.51 x -10(9)yuan, while water satisfaction reaches 102%. This means that effective water demand forecasting can alleviate water demand conflicts under climate change conditions to a certain extent. At the same time, watershed managers can develop different water allocation schemes based on the prediction results of the hybrid ARMA-DNN model.

Automated summary

Increasing water demand has worsened water shortages in water-scarce regions. Effective water demand forecasting is crucial for sustainable water management. A hybrid ARMA-DNN model was developed in this study and validated using the Minjiang River basin as an example, demonstrating its accuracy in predicting water demand and its potential to alleviate conflicts between water supply and demand.