Abstract
Abstract In the present study, for the first time, a new strategy based on a combination of the hybrid least-squares support-vector machine (LS-SVM) and flower pollination algorithm (FPA), average 24 general circulation model (GCM) output, and delta change factor method has been developed to achieve the impacts of climate change on runoff and suspended sediment load (SSL) in the Lighvan Basin in the period (2020–2099). Also, the results of modeling were compared to those of LS-SVM and adaptive neuro-fuzzy inference system (ANFIS) methods. The comparison of runoff and SSL modeling results showed that the LS-SVM-FPA algorithm had the best results and the ANFIS algorithm had the worst results. After the acceptable performance of the LS-SVM-FPA algorithm was proved, the algorithm was used to predict runoff and SSL under climate change conditions based on ensemble GCM outputs for periods (2020–2034, 2035–2049, 2070–2084, and 2085–2099) under three scenarios of RCP2.6, RCP4.5, and RCP8.5. The results showed a decrease in the runoff in all periods and scenarios, except for the two near periods under the RCP2.6 scenario for runoff. The predicted runoff and SSL time series also showed that the SSL values were lower than the average observation period, except for 2036–2039 (up to an 8% increase in 2038).
Highlights
The suspended sediment movements are important in different fields, such as water resource management, water structure designs, and river and dam engineering
Results of this study showed that leastsquares support-vector machine (LS-SVM) and ANN produced better results
Since each combination of inputs can have a different effect on the accuracy of the results, 11 combinations with 0–10-month period lag time are defined for the inputs of the investigated algorithms
Summary
The suspended sediment movements are important in different fields, such as water resource management, water structure designs, and river and dam engineering. Modeling the amount of suspended sediment in the river is an essential issue to design water storage and flow control facilities, such as dams and canals. Modeling the nonlinear relationship between suspended sediments and river flow using different nonlinear methods has become one of the important challenges for different scientific societies, such as engineering and water resources management. Machine learning algorithms have been successfully applied in modeling various water resources and hydrology problems. Dariane & Azimi ( ) used the adaptive neuro-fuzzy inference system (ANFIS), and Seifi & Riahi ( ) used the least-square support-vector machine (LSSVM) for modeling different parameters in water resource management and hydrology. Results of this study showed that LS-SVM and ANN produced better results
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