Using a deep learning approach to estimate floods based on area precipitation pattern

In recent years, due to drought in the country, the issue of management of available water resources is extremely important, and this attention is increasingly to the management of reservoirs and forecasting the volume of water in order to provide appropriate exploitation policies. On the other hand, seasonal and excessive rainfall caused dramatic changes in the bedding of rivers and catchments, which examines the forecasting models in the event of heavy rains, which in addition to preventing damage in addition to preventing damage. Due to the occurrence of floods, surplus water can also be used in the desired direction. Therefore, not developing a proper operation policy, especially in drought conditions, can cause a lot of damage to water-consuming sectors. Proper forecasting of water flows and reservoir inventories leads to the use of control curves for the optimal use of dams and reservoir systems. In this paper, due to the importance of the subject, a model based on deep learning and Mann-Kendall experimental test was used to estimate the flood rate in the Kan-Sulqan area. The results showed that the monthly difference in flood forecast for the convolution neural network is 0.00654 and for the Men-Kendall method is 0.19532. Also, the error rates of MSE, RMSE, MAE and MPE for the neural network were equal to 0.0019, 0.0439, 0.0239, and 0.0159, respectively, which shows the high accuracy of this method in estimating the flood rate in the region.