جستجوی مقالات مرتبط با کلیدواژه « شاخص استاندارد خشکسالی هیدرولوژیکی » در نشریات گروه « مهندسی آب »

Hydrological drought affects water allocation and hydropower generation. Therefore, hydrological drought prediction can help for optimal water resources management. In this paper, short-term hydrological drought was predicted using deep learning. Doing so, Standardized Hydrological Drought Index (SHDI) was calculated in one-, three- and six-month periods. Then, 36 combinations and three different modes of input variables were considered to predict SHDI. In the next step, various optimization algorithms, including grasshopper optimization algorithm, salt swarm algorithm, biogeography-based optimization, and particle swarm optimization combined with ANN were used to predict SHDI. Here, 70% of the data were considered for training and the remaining 30% for test. Finally, the performance of deep learning (DNN) compared to the combined ANN-SSA, -BBO, -PSO and -GOA were discussed. The results of ANN compared to the combined models showed that the combined models performed better, but they were less campatible to DNN. The DNN on one hand, does not need to define the input combination, and on the other hand could reduce the value of RMSE to 0.32.