气候变化领域集成服务门户(CCPortal): Prediction of droughts over Pakistan using machine learning algorithms

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DOI	10.1016/j.advwatres.2020.103562
	Prediction of droughts over Pakistan using machine learning algorithms
	Khan N.; Sachindra D.A.; Shahid S.; Ahmed K.; Shiru M.S.; Nawaz N.
发表日期	2020
ISSN	0309-1708
卷号	139
英文摘要	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. © 2020
关键词	Atmospheric humidity Climate change Drought Forecasting Learning algorithms Nearest neighbor search Neural networks Support vector machines Wind Atmospheric research K nearest neighbours (k-NN)K-nearest neighbours National centres for environmental predictions Pakistan Recursive feature elimination Temporal and spatial Vulnerable communities Learning systems algorithm artificial neural network climate prediction drought machine learning numerical model precipitation (climatology)Arabian Sea Caspian Sea Indian Ocean Pakistan
语种	英语
来源机构	Advances in Water Resources
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/131818
推荐引用方式 GB/T 7714	Khan N.,Sachindra D.A.,Shahid S.,et al. Prediction of droughts over Pakistan using machine learning algorithms[J]. Advances in Water Resources,2020,139.
APA	Khan N.,Sachindra D.A.,Shahid S.,Ahmed K.,Shiru M.S.,&Nawaz N..(2020).Prediction of droughts over Pakistan using machine learning algorithms.,139.
MLA	Khan N.,et al."Prediction of droughts over Pakistan using machine learning algorithms".139(2020).