Prediction of droughts over Pakistan using machine learning algorithms

• Published in: Advances in water resources Vol. 139; p. 103562
• Main Authors: Khan, Najeebullah, Sachindra, D.A., Shahid, Shamsuddin, Ahmed, Kamal, Shiru, Mohammed Sanusi, Nawaz, Nadeem
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.05.2020
• Subjects: Arabian Sea; Artificial Neural Network; Bay of Bengal; Caspian Sea; climate change; drought; Drought prediction; evaporation; humid zones; Indian Ocean; k-Nearest Neighbour; Machine learning; Mediterranean Sea; neural networks; Pakistan; prediction; relative humidity; Support Vector Machines; temperature; water resources; wind speed; Caspian Sea; Arabian Sea; Pakistan; Indian Ocean; Mediterranean Sea; Bay of Bengal; k-Nearest Neighbour; Pakistan; Artificial Neural Network; Support Vector Machines; Drought prediction; Machine learning
• ISSN: 0309-1708; 1872-9657
• DOI: 10.1016/j.advwatres.2020.103562

•For the first time drought prediction models were developed for Pakistan.•Support Vector Machine better captured spatiotemporal characteristics of droughts.•k-Nearest Neighbour showed limited ability in predicting characteristics of droughts.•Relative humidity, temperature and wind speed are indicators of droughts in Pakistan. 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.