Prediction of droughts over Pakistan using machine learning algorithms

被引:152
|
作者
Khan, Najeebullah [1 ,3 ]
Sachindra, D. A. [2 ]
Shahid, Shamsuddin [1 ]
Ahmed, Kamal [1 ,3 ]
Shiru, Mohammed Sanusi [1 ,4 ]
Nawaz, Nadeem [1 ,3 ]
机构
[1] Univ Teknol Malaysia, Fac Engn, Sch Civil Engn, Johor Baharu 81310, Malaysia
[2] Victoria Univ, Coll Engn & Sci, Inst Sustainabil & Innovat, POB 14428, Melbourne, Vic 8001, Australia
[3] Lasbela Univ Agr Water & Marine Sci LUAWMS, Fac Engn Sci & Technol, Uthal 90150, Balochistan, Pakistan
[4] Fed Univ Dutse, Fac Sci, Dept Environm Sci, PMB 7156, Dutse, Nigeria
关键词
Drought prediction; Machine learning; Pakistan; Support Vector Machines; Artificial Neural Network; k-Nearest Neighbour; ARTIFICIAL NEURAL-NETWORK; STANDARDIZED PRECIPITATION; AGRICULTURAL DROUGHT; SEASONAL FORECAST; GRIDDED DATASET; HYBRID MODELS; RIVER-BASIN; HEAT-WAVE; CLIMATE; TEMPERATURE;
D O I
10.1016/j.advwatres.2020.103562
中图分类号
TV21 [水资源调查与水利规划];
学科分类号
081501 ;
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.
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收藏
页数:15
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