Prediction of Multi-Scalar Standardized Precipitation Index by Using Artificial Intelligence and Regression Models
Prediction of Multi-Scalar Standardized Precipitation Index by Using Artificial Intelligence and Regression Models
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DOI：
10.3390/cli9020028
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发表时间：
2021-02-01
期刊：
影响因子：
3.7
通讯作者：
Kuriqi, Alban
中科院分区：
文献类型：
--
作者：
Malik, Anurag;Kumar, Anil;Kuriqi, Alban
对干旱的准确监测和预测至关重要。它们在灌溉系统的最佳运作、风险管理、抗旱准备和缓解方面发挥着至关重要的作用。在这项工作中，研究了包括多层感知器神经网络（MLPNN）和协同神经模糊推理系统（CANFIS）在内的人工智能（AI）模型，以及包括多元线性回归（MLR）在内的回归模型，用于印度北阿坎德邦加尔瓦尔地区的多标量标准化降水指数（SPI）预测。利用可利用年份的月降水资料，在1月、3月、6月、9月、12月和24月6个不同尺度上计算SPI。作为MLPNN、CANFIS和MLR模型输入的显著滞后是通过利用部分自相关函数（PACF）获得的，对于SPI-1、SPI-3、SPI-6、SPI-9、SPI-12和SPI-24，其显著水平为5%。利用MLPNN、CANFIS和MLR模型预测的多尺度SPI值，通过不同的性能评价指标和目视解译，与计算的多时间尺度SPI值进行比较。结果表明，CANFIS在Dehradun（3、6、9和12个月尺度）、Chamoli和Tehri Garhwal（1、3、6、9和12个月尺度）、Haridwar和Pauri Garhwal（1、3、6和9个月尺度）、Rudraprayag（1、3和6个月尺度）和Uttarkashi（3个月尺度）站点的干旱预测中表现更为可靠。MLPNN模型在Dehradun（1个月和24个月）、Tehri Garhwal和Chamoli（24个月）、Haridwar（12个月和24个月）、Pauri Garhwal（12个月）、Rudraprayag（9个月、12个月和24个月）和Uttarkashi（1个月和6个月）站表现最佳，MLR模型在Pauri Garhwal（24个月）和Uttarkashi（9个月、12个月和24个月）站表现最佳。此外，建模方法可以建立一个简单可靠的专家智能机制，用于预测多标量SPI，并为所研究站点的气象干旱补救安排做出决策。
Accurate monitoring and forecasting of drought are crucial. They play a vital role in the optimal functioning of irrigation systems, risk management, drought readiness, and alleviation. In this work, Artificial Intelligence (AI) models, comprising Multi-layer Perceptron Neural Network (MLPNN) and Co-Active Neuro-Fuzzy Inference System (CANFIS), and regression, model including Multiple Linear Regression (MLR), were investigated for multi-scalar Standardized Precipitation Index (SPI) prediction in the Garhwal region of Uttarakhand State, India. The SPI was computed on six different scales, i.e., 1-, 3-, 6-, 9-, 12-, and 24-month, by deploying monthly rainfall information of available years. The significant lags as inputs for the MLPNN, CANFIS, and MLR models were obtained by utilizing Partial Autocorrelation Function (PACF) with a significant level equal to 5% for SPI-1, SPI-3, SPI-6, SPI-9, SPI-12, and SPI-24. The predicted multi-scalar SPI values utilizing the MLPNN, CANFIS, and MLR models were compared with calculated SPI of multi-time scales through different performance evaluation indicators and visual interpretation. The appraisals of results indicated that CANFIS performance was more reliable for drought prediction at Dehradun (3-, 6-, 9-, and 12-month scales), Chamoli and Tehri Garhwal (1-, 3-, 6-, 9-, and 12-month scales), Haridwar and Pauri Garhwal (1-, 3-, 6-, and 9-month scales), Rudraprayag (1-, 3-, and 6-month scales), and Uttarkashi (3-month scale) stations. The MLPNN model was best at Dehradun (1- and 24- month scales), Tehri Garhwal and Chamoli (24-month scale), Haridwar (12- and 24-month scales), Pauri Garhwal (12-month scale), Rudraprayag (9-, 12-, and 24-month), and Uttarkashi (1- and 6-month scales) stations, while the MLR model was found to be optimal at Pauri Garhwal (24-month scale) and Uttarkashi (9-, 12-, and 24-month scales) stations. Furthermore, the modeling approach can foster a straightforward and trustworthy expert intelligent mechanism for projecting multi-scalar SPI and decision making for remedial arrangements to tackle meteorological drought at the stations under study.