In this research, a new framework has been introduced for rainfall temporal variability evaluation by using combination of monthly rainfall data sets in three synoptic stations, Principal Component Analysis (PCA), Adaptive Neuro Fuzzy Inference System (ANFIS), Grasshopper Optimization Algorithm (GOA), and Innovative Trend Analysis (ITA) methodology. The first five components were chosen as inputs of the soft-computing models, based on PCA. The GOA was used for training the ANFIS model, in order to estimate the monthly rainfall. The outputs of the ANFIS-GOA were compared to the rainfall estimates by ANFIS-Particle Swarm Optimization (ANFIS-PSO) and ANFIS-Genetic Algorithm (ANFIS-GA). Moreover, various statistical indices, such as mean absolute error (MAE), percent bias (PBIAS) and Nash-Sutcliffe Efficiency (NSE) were used to evaluate the soft-computing models’ performance. Results indicated that ANFIS-GOA had higher accuracy in estimating the rainfall (values of MAE, NSE and PBIAS were 0.21, 0.92 and 0.16 for Mehrabad station, 0.16, 0.94 and 0.14 for Semnan station and 0.24, 0.91 and 0.17 for Noshahr station, respectively) in the testing phase. These values showed significant improvements (67.8%, 21% and 40% for Mehrabad station, 69.2%, 17.5% and 33.3% for Semnan station and 57.1%, 21.3% and 37% for Noshahr station) versus indices related to standalone ANFIS model, which reflected the supremacy and higher accuracy of ANFIS-GOA model in rainfall prediction for different climatic conditions. It was also concluded that the ANFIS-GOA, ANFIS-PSO, and ANFIS-GA models performed superior to the standalone ANFIS-based model, respectively. Furthermore, possible trends in monthly rainfall have been detected by ITA, which is a new graphical model. Results showed significant decreasing trends in January and July for all the rainfall values in Mehrabad station. By comparison of the results obtained from ANFIS and the hybrid models with observed data, it was also concluded that the trends of observed data were close to the ANFIS-GOA predictions.

在这项研究中，通过使用三个天气站，主成分分析（PCA），自适应神经模糊推理系统（ANFIS），蝗虫优化算法（GOA） ，以及创新趋势分析（ITA）方法。基于PCA，选择了前五个组件作为软计算模型的输入。GOA用于训练ANFIS模型，以估计月降雨量。通过ANFIS-粒子群优化（ANFIS-PSO）和ANFIS-遗传算法（ANFIS-GA），将ANFIS-GOA的输出与降雨量估算值进行了比较。此外，还有各种统计指标，例如平均绝对误差（MAE），百分比偏差（PBIAS）和纳什-苏特克利夫效率（NSE）用于评估软计算模型的性能。结果表明，ANFIS-GOA的降雨估算具有更高的准确性（梅哈拉巴德站的MAE，NSE和PBIAS值分别为0.21、0.92和0.16，塞姆南站分别为0.16、0.94和0.14，诺沙尔站分别为0.24、0.91和0.17 ）处于测试阶段。与独立ANFIS模型相关的指标相比，这些值显示出显着改善（Mehrabad站为67.8％，21％和40％，Semnan站为69.2％，17.5％和33.3％，Noshahr站为57.1％，21.3％和37％），这反映了ANFIS-GOA模型在不同气候条件下的降雨预测中的至高性和较高的准确性。还得出结论，ANFIS-GOA，ANFIS-PSO，和ANFIS-GA模型的性能分别优于独立的基于ANFIS的模型。此外，ITA已经检测到月降雨量的可能趋势，这是一个新的图形模型。结果表明，梅赫拉巴德站所有降雨值在1月和7月都有明显的下降趋势。通过比较从ANFIS和混合模型获得的结果与观察到的数据，还得出结论，观察到的数据趋势接近ANFIS-GOA预测。