Abstract Climate change has increased frequency, severity and areal extent of droughts across the world in the last few decades magnifying their adverse impacts. Prediction of droughts is immensely helpful in early warning and preparing the most vulnerable communities to their adverse impacts. For the first time, this study investigated the potential of developing drought prediction models over Pakistan using three state-of-the-art Machine Learning (ML) techniques; Support Vector Machine (SVM), Artificial Neural Network (ANN) and k-Nearest Neighbour (KNN). Three categories of droughts; moderate, severe, and extreme considering two major cropping seasons called Rabi and Kharif were estimated using Standardized Precipitation Evaporation Index (SPEI) and then predicted using the predictor data obtained from the National Centres for Environmental Prediction/National Centre for Atmospheric Research (NCEP/NCAR) reanalysis database. Also, for the first time in drought modelling, a novel feature selection approach called Recursive Feature Elimination (RFE) was used for identifying optimum sets of predictors. In validation, SVM-based models were able to better capture the temporal and spatial characteristics of droughts over Pakistan compared to those by ANN and KNN-based models. KNN which was used in developing drought models for the first time displayed limited performance in comparison to that by SVM and ANN-based drought models, in validation. It was found that in the Rabi season SPEI is positively correlated with relative humidity over the Mediterranean Sea and the region north of the Caspian Sea. In the Kharif season, SPEI is positively correlated with the humid region over the south-eastern part of the Bay of Bengal and the regions north of the Mediterranean and Caspian Seas. In developing a drought prediction model for Pakistan, relative humidity, temperature and wind speed should be considered with a domain which encompasses the Mediterranean Sea, the region north of the Caspian Sea, the Indian Ocean and the Arabian Sea.

中文翻译：

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使用机器学习算法预测巴基斯坦的干旱

摘要 在过去的几十年里，气候变化增加了世界各地干旱的频率、严重程度和面积，扩大了它们的不利影响。干旱预测对于早期预警和让最脆弱的社区做好应对其不利影响的准备非常有帮助。本研究首次调查了使用三种最先进的机器学习 (ML) 技术在巴基斯坦开发干旱预测模型的潜力；支持向量机 (SVM)、人工神经网络 (ANN) 和 k-最近邻 (KNN)。三类干旱；中度、重度、使用标准化降水蒸发指数 (SPEI) 估计两个主要作物季节，即 Rabi 和 Kharif，然后使用从国家环境预测中心/国家大气研究中心 (NCEP/NCAR) 再分析数据库获得的预测数据进行预测。此外，在干旱建模中，首次使用了一种称为递归特征消除 (RFE) 的新型特征选择方法来确定最佳预测变量集。在验证中，与基于 ANN 和 KNN 的模型相比，基于 SVM 的模型能够更好地捕捉巴基斯坦干旱的时空特征。与 SVM 和基于 ANN 的干旱模型相比，首次用于开发干旱模型的 KNN 在验证中表现出有限的性能。研究发现，在拉比季节，SPEI 与地中海和里海以北地区的相对湿度呈正相关。在 Kharif 季节，SPEI 与孟加拉湾东南部以及地中海和里海以北地区的潮湿地区呈正相关。在为巴基斯坦开发干旱预测模型时，应考虑包含地中海、里海以北地区、印度洋和阿拉伯海的区域的相对湿度、温度和风速。SPEI 与孟加拉湾东南部以及地中海和里海以北地区的潮湿地区呈正相关。在为巴基斯坦开发干旱预测模型时，应考虑包含地中海、里海以北地区、印度洋和阿拉伯海的区域的相对湿度、温度和风速。SPEI 与孟加拉湾东南部以及地中海和里海以北地区的潮湿地区呈正相关。在为巴基斯坦开发干旱预测模型时，应考虑包含地中海、里海以北地区、印度洋和阿拉伯海的区域的相对湿度、温度和风速。