Hydropower is among the cleanest sources of energy. However, the rate of hydropower generation is profoundly affected by the inflow to the dam reservoirs. In this study, the Grey wolf optimization (GWO) method coupled with an adaptive neuro-fuzzy inference system (ANFIS) to forecast the hydropower generation. For this purpose, the Dez basin average of rainfall was calculated using Thiessen polygons. Twenty input combinations, including the inflow to the dam, the rainfall and the hydropower in the previous months were used, while the output in all the scenarios was one month of hydropower generation. Then, the coupled model was used to forecast the hydropower generation. Results indicated that the method was promising. GWO-ANFIS was capable of predicting the hydropower generation satisfactorily, while the ANFIS failed in nine input-output combinations.

In this research, two scenarios of drought forecast were studied. In the first scenario, the time series of monthly streamflow were converted into the Standardized Hydrological Drought Index (SHDI), a similar index to the well-known Standardized Precipitation Index (SPI). Multi-layer feed-forward artificial neural network (FFANN) was trained with the SHDI time series to forecast the hydrological drought of Karoon River in southwestern Iran. In the second scenario, the time series of monthly streamflow discharge was forecasted directly and then converted to the SHDI. Principal component analysis (PCA) and forward selection (FS) techniques were applied to remove dependency of inputs and reduce the number of input variables, respectively. Moreover, uncertainty of SHDI and monthly streamflow discharge forecasts were investigated using a Monte-Carlo simulation approach. Findings indicated that the results of the first scenario were considerably better than the second scenario and that the SHDI adequately forecasted hydrological drought. The Monte-Carlo simulations demonstrated that all of forecasted values lie within the 95% confidence intervals.

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The main aims and contributions of the present paper are to use new soft computing methods for the simulation of scour geometry (depth/height and locations) in a comparative framework. Five models were used for the prediction of the dimension and location of the scour pit. The five developed models in this study are multilayer perceptron (MLP) neural network, radial basis functions (RBF) neural network, adaptive neuro fuzzy inference systems (ANFIS), multiple linear regression (MLR), and multiple non-linear regression (MNLR) in comparison with empirical equations. Four non-dimensional geometry parameters of scour hole shape are predicted by these models including the maximum scour depth (S), the distance of S from the weir (XS), the maximum height of downstream deposited sediments (hd), and distance of hd from the weir (XD). The best results over train data derived for XS/Z and hd/Z by the MLP model with R 2 are 0.95 and 0.96 respectively; the best predictions for S/Z and XD/Z are from the ANFIS model with R 2 0.91 and 0.96 respectively. The results indicate that the application of MLP and ANFIS results in the accurate prediction of scour geometry for the designing of stable grade control structures in alluvial irrigation channels.

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The aim of this study was to analyse spatial and temporal homogeneity and trends in seasonal and annual precipitation and their fluctuations during 1957–2016 in Iran. For testing homogeneity, the Pettitt‐Whitney–Mann, the Standard Normal Homogeneity or Alexandersson's SNHT, Buishand's and the Von Neumann tests were used and for trend analysis, Mann‐Kendall and Sequential Mann‐Kendall tests were used. Homogeneity tests showed that most of the seasonal and annual precipitation time series were homogeneous and the change point was detected only at a few stations. Results indicated that in general, there was an upward trend during 1957–1986, followed by a significant downward trend during 1987–2016. The sequential Mann‐Kendall test detected several possible change points over time, although most of them were found statistically non‐significant at 95% confidence level. Upward and downward trends started in annual and seasonal precipitation after 1960 and 1990, respectively, at most stations, except in spring. In spring, an upward trend started after 1990 at some stations which showed a shift in precipitation from winter to spring. Also, the Sen's slope estimator indicated a higher downward trend in the northern parts of Iran. Two findings are worthy of note based on the results of this study. First, in general, a significant downward trend occurred in the whole country after1990. Second, a shift in precipitation from winter to spring was detected at some stations in different homogeneous regions.

Hydrological drought forecasting plays a substantial role in water resources management. Hydrological drought highly affects the water allocation and hydropower generation. In this research, short term hydrological drought forecasted based on the hybridized of novel nature-inspired optimization algorithms and Artificial Neural Networks (ANN). For this purpose, the Standardized Hydrological Drought Index (SHDI) and the Standardized Precipitation Index (SPI) were calculated in one, three, and six aggregated months. Then, three states where proposed for SHDI forecasting, and 36 input-output combinations were extracted based on the cross-correlation analysis. In the next step, newly proposed optimization algorithms, including Grasshopper Optimization Algorithm (GOA), Salp Swarm algorithm (SSA), Biogeography-based optimization (BBO), and Particle Swarm Optimization (PSO) hybridized with the ANN were utilized for SHDI forecasting and the results compared to the conventional ANN. Results indicated that the hybridized model outperformed compared to the conventional ANN. PSO performed better than the other optimization algorithms. The best models forecasted SHDI1 with R2 = 0.68 and RMSE = 0.58, SHDI3 with R 2 = 0.81 and RMSE = 0.45 and SHDI6 with R 2 = 0.82 and RMSE = 0.40.