基于anfiss的软计算模型预测印度干旱区有效干旱指数

IF 2.1 4区 环境科学与生态学 Q2 ENGINEERING, CIVIL
Ayilobeni Kikon, B. M. Dodamani, S. Barma, Sujay Raghavendra Naganna
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引用次数: 2

摘要

干旱是一种自然灾害,其特点是一个地区降水少。为了更好地评价干旱对人类福祉造成的影响,干旱指数变得越来越重要。本文利用印度拉贾斯坦邦焦特布尔地区1964 - 2013年(约50年)的月降水数据,推导了有效干旱指数(EDI)。在广义回归神经网络(GRNN)的基础上,结合遗传算法自适应神经模糊推理系统(GA-ANFIS)和粒子群优化算法ANFIS (PSO-ANFIS)等机器学习模型对EDI指数进行预测。利用部分自相关函数(partial autocorrelation function, PACF),构建了2、3、5个输入组合的月度EDI预测模型,并基于不同的绩效指标对模型的预测结果进行了评价。比较了不同组合模型的结果。在2输入和3输入组合模型中,GA-ANFIS和PSO-ANFIS表现出较好的性能,R2 = 0.75,而在5输入组合模型中,GA-ANFIS表现出较好的性能,R2 = 0.78。利用散点图、泰勒图和小提琴图对结果进行了适当的描述。总体而言,GA-ANFIS和PSO-ANFIS模型优于GRNN模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ANFIS-based soft computing models for forecasting effective drought index over an arid region of India
Drought is a natural hazard that is characterized by a low amount of precipitation in a region. In order to evaluate the drought-related issues that cause chaos for human well-being, drought indices have become increasingly important. In this study, the monthly precipitation data from 1964 to 2013 (about 50 years) of the Jodhpur district in the drought-prone Rajasthan state of India was used to derive the effective drought index (EDI). The machine learning models hybridized with evolutionary optimizers such as the genetic algorithm adaptive neuro-fuzzy inference system (GA-ANFIS) and particle swarm optimization ANFIS (PSO-ANFIS) were used in addition to the generalized regression neural network (GRNN) to predict the EDI index. Using the partial autocorrelation function (PACF), models for forecasting the monthly EDI were constructed with 2-, 3- and 5-input combinations to evaluate their outcomes based on various performance indices. The results of the different combination models were compared. With reference to 2-input and 3-input combination models, both GA-ANFIS and PSO-ANFIS show better performance results with R2 = 0.75, while among the models with 5-input combination, GA-ANFIS depicts better performance results compared to other models with R2 = 0.78. The results are presented suitably with the aid of scatter plots, Taylor's diagram and violin plots. Overall, the GA-ANFIS and PSO-ANFIS models outperformed the GRNN model.
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来源期刊
CiteScore
4.10
自引率
21.10%
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