论文标题
使用ANFIS，SVM和ANN杂交的六种进化优化算法对地下水水平进行建模和不确定性分析
Modeling and Uncertainty Analysis of Groundwater Level Using Six Evolutionary Optimization Algorithms Hybridized with ANFIS, SVM, and ANN
论文作者
论文摘要
在本研究中，将六种元神经方案与人工神经网络（ANN），自适应神经模糊界面系统（ANFIS）和支持向量机（SVM）杂交，以预测每月的地下水水平（GWL），评估预测和空间变异分析的不确定性分析。六个方案，包括蚱hopper优化算法（GOA），猫群（CSO），杂草算法优化（WA），遗传算法（GA），磷虾算法（KA）和粒子群群优化（PSO）用于杂交ANN，SVM和ANFIS模型。选择了144个月的Ardebil Plain（伊朗）的地下水水平（GWL）数据以评估混合模型。应用主成分分析（PCA）的预处理技术可将输入组合从每月时间序列降低到12个月的预测间隔。结果表明，ANFIS-GOA优于其他混合模型，用于预测在测试阶段的第一个压电计和第三个压电机中GWL的其他混合模型。具有优化算法的混合模型的性能要比没有杂交的经典ANN，ANFIS和SVM模型的性能要好得多。在训练阶段，Anfis-GOA与独立ANFI的改善百分比为14.4％，3％，17.8％和181％，在训练阶段中为40.7％，55％，25％，25％和132％的RMSE，MAE，NSE和PBIAS分别为测试阶段。在火车步骤中，打电量6的改进为15％，4％，13％和208％，在测试步骤中分别为33％，44.6％，16.3％和173％，这清楚地证实了GWL模型中开发的杂交方案的优势。不确定性分析表明，ANFIS-GOA和SVM分别具有其他模型中最佳和最差的性能。通常，果阿提高了ANFIS，ANN和SVM模型的准确性。
In the present study, six meta-heuristic schemes are hybridized with artificial neural network (ANN), adaptive neuro-fuzzy interface system (ANFIS), and support vector machine (SVM), to predict monthly groundwater level (GWL), evaluate uncertainty analysis of predictions and spatial variation analysis. The six schemes, including grasshopper optimization algorithm (GOA), cat swarm optimization (CSO), weed algorithm (WA), genetic algorithm (GA), krill algorithm (KA), and particle swarm optimization (PSO), were used to hybridize for improving the performance of ANN, SVM, and ANFIS models. Groundwater level (GWL) data of Ardebil plain (Iran) for a period of 144 months were selected to evaluate the hybrid models. The pre-processing technique of principal component analysis (PCA) was applied to reduce input combinations from monthly time series up to 12-month prediction intervals. The results showed that the ANFIS-GOA was superior to the other hybrid models for predicting GWL in the first piezometer and third piezometer in the testing stage. The performance of hybrid models with optimization algorithms was far better than that of classical ANN, ANFIS, and SVM models without hybridization. The percent of improvements in the ANFIS-GOA versus standalone ANFIS in piezometer 10 were 14.4%, 3%, 17.8%, and 181% for RMSE, MAE, NSE, and PBIAS in the training stage and 40.7%, 55%, 25%, and 132% in testing stage, respectively. The improvements for piezometer 6 in train step were 15%, 4%, 13%, and 208% and in the test step were 33%, 44.6%, 16.3%, and 173%, respectively, that clearly confirm the superiority of developed hybridization schemes in GWL modeling. Uncertainty analysis showed that ANFIS-GOA and SVM had, respectively, the best and worst performances among other models. In general, GOA enhanced the accuracy of the ANFIS, ANN, and SVM models.
