Modeling and predicting suspended sediment load under climate change conditions: a new hybridization strategy

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 A hybrid strategy has been developed for modeling and predicting suspended sediment load (SSL) under climate change.; A hybrid strategy was compared to standalone LS-SVM and adaptive neuro-fuzzy inference system.; This approach has the potential to model and predict various hydrological variables.; The ensemble general circulation model of fifth report and RCP scenarios were employed for predicting SSL.;

Keywords

• climate change
• flower pollination algorithm
• hybridization strategy
• least-squares support-vector machine
• suspended sediment loads