ABSTRACT

In this study the capability of two advanced hybrid artificial intelligence-based methods is investigated in modelling meteorological droughts based on the standardized precipitation index (SPI) for various time windows. The outcomes are compared with adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA). The best-fitted distribution functions are found to vary with respect to stations and time windows. For Canakkale, Istanbul and Tekirdag stations, the correlation coefficients (CC) of hybridized method of ANFIS with gray wolf optimization (ANFIS-GWO) and ARIMA are in the range of 0.88–0.94, 0.88–0.96 and 0.86–0.94, respectively. The performance index also showed that the ANFIS-GWO provides superior accuracy in modelling droughts, with the minimum value (PI = 0.78) for SPI12 of Canakkale station. Forward-chaining cross-validation and the P value of the Chi-squared test also confirm that ANFIS-GWO is the superior model, in which there is not a significant difference between the trend of the predicted categories and that of the real categories for all stations and SPIs.

摘要

在这项研究中，研究了两种先进的基于人工智能的混合方法在基于不同时间窗口的标准化降水指数 (SPI) 建模气象干旱中的能力。将结果与自适应神经模糊推理系统 (ANFIS)、支持向量回归 (SVR)、自回归综合移动平均 (ARIMA) 和季节性自回归综合移动平均 (SARIMA) 进行比较。发现最佳拟合分布函数随站点和时间窗口而变化。对于恰纳卡莱、伊斯坦布尔和泰基尔达台站，ANFIS 与灰狼优化（ANFIS-GWO）和 ARIMA 的杂交方法的相关系数（CC）分别在 0.88-0.94、0.88-0.96 和 0.86-0.94 的范围内。PI  = 0.78) 用于恰纳卡莱站的 SPI12。前向链交叉验证和卡方检验的 P 值也证实了 ANFIS-GWO 是优越的模型，其中预测类别的趋势与真实类别的趋势之间没有显着差异。站和 SPI。