Investigation on the Potential to Integrate Different Artificial Intelligence Models with Metaheuristic Algorithms for Improving River Suspended Sediment Predictions
Investigation on the Potential to Integrate Different Artificial Intelligence Models with Metaheuristic Algorithms for Improving River Suspended Sediment Predictions
复制标题
DOI：
10.3390/app9194149
复制
发表时间：
2019-10-01
影响因子：
2.7
通讯作者：
EL-Shafie, Ahmed
中科院分区：
文献类型：
--
作者：
Ehteram, Mohammad;Ghotbi, Samira;EL-Shafie, Ahmed
由于泥沙运动改变了土壤质量，因此泥沙预报是水文学和水利科学的重要研究领域。虽然自适应神经模糊系统（ANFIS）和多层前馈神经网络（MFNN）已被广泛应用于水文变量的模拟，但提高上述模型的精度是水文工作者的一个重要问题。本文采用蝙蝠算法（BA）和杂草算法（WA）对ANFIS和MFNN模型进行了改进。因此，本文介绍了改进的ANFIS和MFNN模型：ANFIS-BA，ANFIS-WA，MFNN-BA和MFNN-WA。该模型进行了验证，应用河流流量，降雨量和月悬沙负荷（SSL）的Atrek流域在伊朗。此外，七个输入组被用来预测每月SSL。通过均方根误差（RMSE），Nash-Sutcliff效率（NSE），标准差比（RSR），百分比偏差（PBIAS）指数和不确定性分析确定最佳模型。对于ANFIS-BA模型，RMSE和RSR分别为1.5 - 2.5吨/天和5%-25%。此外，NSE的变化范围在非常好和良好性能之间（0.75至0.85和0.85至1）。不确定性分析表明，ANFIS-BA模型比其他模型具有更高的可靠性。因此，ANFIS-BA模型具有很高的预测SSL的潜力。
Suspended sediment load (SLL) prediction is a significant field in hydrology and hydraulic sciences, as sedimentation processes change the soil quality. Although the adaptive neuro fuzzy system (ANFIS) and multilayer feed-forward neural network (MFNN) have been widely used to simulate hydrological variables, improving the accuracy of the above models is an important issue for hydrologists. In this article, the ANFIS and MFNN models were improved by the bat algorithm (BA) and weed algorithm (WA). Thus, the current paper introduces improved ANFIS and MFNN models: ANFIS-BA, ANFIS-WA, MFNN-BA, and MFNN-WA. The models were validated by applying river discharge, rainfall, and monthly suspended sediment load (SSL) for the Atrek basin in Iran. In addition, seven input groups were used to predict monthly SSL. The best models were identified through root-mean-square error (RMSE), Nash-Sutcliff efficiency (NSE), standard deviation ratio (RSR), percent bias (PBIAS) indices, and uncertainty analysis. For the ANFIS-BA model, RMSE and RSR varied from 1.5 to 2.5 ton/d and from 5% to 25%, respectively. In addition, a variation range of NSE was between very good and good performance (0.75 to 0.85 and 0.85 to 1). The uncertainty analysis showed that the ANFIS-BA had more reliable performance compared to other models. Thus, the ANFIS-BA model has high potential for predicting SSL.