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Drought, climate change, and demand make precipitation forecast a very important issue in water resource management. The present study aims to develop a forecasting model for monthly precipitation in the basin of the province of East Azarbaijan in Iran over a ten-year period using the multilayer perceptron neural network (MLP) and support vector regression (SVR) models. In this study, the flow regime optimization algorithm (FRA) was applied to optimize the multilayer neural network and support vector machine. The flow regime optimization algorithm not only identifies the parameters of the SVR and MLP models but also replaces the training algorithms. The decision tree model (M5T) was also used to forecast precipitation and compare it with the results of hybrid models. Principal component analysis (PCA) was used to identify effective indicators for precipitation forecast. In the first scenario, the input data include temperature data with a delay of one to twelve months, the second scenario includes precipitation data with a delay of one to twelve months, and the third scenario includes precipitation and temperature data with a delay of one to three months. The mean absolute error (MAE) and Nash-Sutcliffe error (NSE) indices were used to evaluate the performance of the models. The results showed that the proposed MLP-FRA outperformed all the other examined models. Regarding the uncertainties of the models, it was also shown that the MLP-FRA model had a lower uncertainty band width than other models, and a higher percentage of the data will fall within the range of the confidence band. As the selected scenario, Scenario 3 had a better performance. Finally, monthly precipitation maps were generated based on the MLP-FRA model and Scenario 3 using the weighted interpolation method, which showed significant precipitation in spring and winter and a low level of precipitation in summer. The results of the present study showed that MLP-FRA has high capability to predict hydrological variables and can be used in future research.Consecutive droughts and increasing water demands mean that prediction and planning for the usage of precipitation are necessary for decision makers [1,2]. In addition, precipitation forecasting is necessary to prevent floods and construct flood-controlling structures. What is more, precipitation forecasting is one of the main issues of the water resources planning that can reduce the effects of drought [3]. Researchers have used various statistical and hydrological models to predict precipitation; however, it should be noted that precipitation patterns are dependent on various parameters and have a nonlinear behavior. Therefore, these complexities result in uncertainties in precipitation forecasting models. In recent years, researchers have used soft computing models to forecast hydrological variables such as precipitation [4]. Models such as artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM), or improved regression models and optimization al...

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In this research, the advanced multilayer perceptron (MLP) models are utilized to predict the free rate of expansion that usually occurs around the pipeline (PL) because of waves. The MLP model was structured by integrating it with three optimization algorithms: particle swarm optimization (PSO), whale algorithm (WA), and colliding bodies’ optimization (CBO). The sediment size, wave characteristics, and PL geometry were used as the inputs for the applied models. Moreover, the scour rate, vertical scour rate along the pipeline, and scour rate at both right and left sides of the pipeline were predicted as the model outputs. Results of the three suggested models, MLP-CBO, MLP-WA, and MLP-PSO, for both testing and training sessions were assessed based on different statistical indices. The results indicated that the MLP-CBO model performed better in comparison to the MLP-PSO, MLP-WA, regression, and empirical models. The MLP-CBO can be used as a powerful soft-computing model for predictions.

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