A Novel Approach for Estimation of Sediment Load in Dam Reservoir With Hybrid Intelligent Algorithms - Karami, Hojat; DadrasAjirlou, Yashar; Jun, Changhyun; Bateni, Sayed M.; Band, Shahab S.; Mosavi, Amir; Moslehpour, Massoud; Chau, Kwok-Wing (Frontiers in Environmental Science, 2022)
Predicting the amount of sediment in water resource projects is one of the most important measures to be taken, while sediments have an unknown nature in their behavior. In this research, using the data recorded at the Mazrae station between 2002 and 2013, the amount of sediment in the catchment area of Maku Dam has been predicted using different models of intelligent algorithms. Recorded data including river flow (m3/s), sediment concentration (mg/L), and temperature (°C) were considered input data, and sediment load (ton/day) was considered output data. Initially, using the correlation test, the relationship between each input data with output data was considered. The results show high correlation of sediment concentration data and river flow with sediment load and low correlation of temperature data with these data. In order to find the best combination of data for prediction, the combination of single, binary, and triple data was considered in sensitivity analysis. In order to achieve the purpose of this study, first with the classical adaptive neuro-fuzzy inference system (ANFIS), the amount of sediment load was predicted, and then using evolutionary algorithms in ANFIS training, their performance was examined. The intelligent algorithms used in this study were ant colony optimization extended to continuous domain, particle swarm optimization, differential evolution, and genetic algorithm. The results showed that adaptive neuro-fuzzy inference system–ant colony optimization extended to continuous domain, adaptive neuro-fuzzy inference system–particle swarm optimization, adaptive neuro-fuzzy inference system–genetic algorithm, adaptive neuro-fuzzy inference system–differential evolution, and classical ANFIS had the best performance in predicting the amount of sediment load. In the meantime, it was observed that the coefficient of determination, root mean square error, and scatter index in the test mode for the adaptive neuro-fuzzy inference system–ant colony optimization extended to continuous domain algorithm with the best prediction dataset (sediment concentration + river flow) are equal to 0.991, 13.001, and (ton/day), 0.112, and those for the ANFIS with the weakest prediction (temperature + river flow) are equal to 0.490, 107.383 (ton/day), and 0.929, respectively. The present study showed that the use of intelligent algorithms in ANFIS training has been able to improve its performance in predicting the amount of sediment load in the catchment area of Maku Dam.