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Prediction of droughts over Pakistan using machine learning algorithms

机译:使用机器学习算法预测巴基斯坦干旱

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摘要

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.
机译:在过去的几十年中,气候变化在世界各地的频率,严重程度和全世界的程度的程度增加,从而放大了它们的不利影响。对干旱的预测在早期警告和准备最脆弱的社区时对其不利影响的影响非常有用。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;考虑到两个主要的种植季节,估计使用标准化的降水蒸发指数(SPEI)估算了叫做Rabi和Kharif的两个主要种植季节,然后使用从国家环境预测/国家大气研究中心获得的预测数据预测(NCEP / NCAR )重新分析数据库。此外,在干旱建模中,使用称为递归特征消除(RFE)的新颖特征选择方法用于识别最佳的预测器集。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获巴基斯坦对巴基斯坦干旱的时间和空间特征。用于开发干旱模型的knn首次显示有限的性能,与SVM和基于ANN的干旱模型在验证中相比。结果发现,在拉比季节中,Spei与地中海和里海海北地区的地区相对湿度呈正相关。在Kharif赛季,Spei与孟加拉湾的东南部和地中海和中海海域北部的地区的潮湿地区正相关。在开发巴基斯坦的干旱预测模型时,应考虑涵盖地中海,里海,印度洋和阿拉伯海北部地区地中海的领域的域名,相对湿度,温度和风速。

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  • 来源
    《Advances in Water Resources 》 |2020年第5期| 103562.1-103562.15| 共15页
  • 作者单位

    Univ Teknol Malaysia Fac Engn Sch Civil Engn Johor Baharu 81310 Malaysia|Lasbela Univ Agr Water & Marine Sci LUAWMS Fac Engn Sci & Technol Uthal 90150 Balochistan Pakistan;

    Victoria Univ Coll Engn & Sci Inst Sustainabil & Innovat POB 14428 Melbourne Vic 8001 Australia;

    Univ Teknol Malaysia Fac Engn Sch Civil Engn Johor Baharu 81310 Malaysia;

    Univ Teknol Malaysia Fac Engn Sch Civil Engn Johor Baharu 81310 Malaysia|Lasbela Univ Agr Water & Marine Sci LUAWMS Fac Engn Sci & Technol Uthal 90150 Balochistan Pakistan;

    Univ Teknol Malaysia Fac Engn Sch Civil Engn Johor Baharu 81310 Malaysia|Fed Univ Dutse Fac Sci Dept Environm Sci PMB 7156 Dutse Nigeria;

    Univ Teknol Malaysia Fac Engn Sch Civil Engn Johor Baharu 81310 Malaysia|Lasbela Univ Agr Water & Marine Sci LUAWMS Fac Engn Sci & Technol Uthal 90150 Balochistan Pakistan;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Drought prediction; Machine learning; Pakistan; Support Vector Machines; Artificial Neural Network; k-Nearest Neighbour;

    机译:干旱预测;机器学习;巴基斯坦;支持向量机;人工神经网络;k离邻居;

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