Hybrid neuro fuzzy inference systems for simulating catchment sediment yield

Abstract

Increasing sediment yield is one of the important environmental challenges in river basins resulting from changing land use. The current study develops an adaptive neuro fuzzy inference system (ANFIS) hybridized with evolutionary algorithms to predict annual sediment yield at the catchment scale considering some key factors affecting the alteration of the sediment yield. The key factors consist of the area of the sub-catchments, average slope of the sub-catchments, rainfall, and forest index, and the output of the model is sediment yield. Several indices such as the Nash–Sutcliffe efficiency (NSE), root mean square error and vulnerability index (VI) were applied to evaluate the performance of the models. Moreover, hybrid models were compared in terms of complexities to select the best approach. Based on the results in Talar River basin in Iran, several hybrid models in which particle swarm optimization (PSO), genetic algorithm, invasive weed optimization, biogeography-based optimization, and shuffled complex evolution used to train the neuro fuzzy network are able to generate reliable sediment yield models. The NSE of all previously listed models is more than 0.8 which means they are robust for assessing sediment yield resulting from land use change with a focus on deforestation. The proposed models are fairly similar in terms of computational complexities which implies no priority for selecting the best model. However, PSO-ANFIS performed slightly better than the other models especially in terms of accuracy of the outputs due to a high NSE (0.92) and a low VI (1.9 Mg/ha). Using the proposed models is recommended due to the lower required time and data compared to a physically based models such as the The Soil and Water Assessment Tool. However, some drawbacks restrict the application of the proposed model. For example, the proposed models cannot be used for small temporal scales.