Forecasting long-term precipitation for water resource management: a new multi-step data-intelligent modelling approach

Ali, Mumtaz and Deo, Ravinesh C. ORCID: https://orcid.org/0000-0002-2290-6749 and Xiang, Yong and Li, Ya and Yaseen, Zaher Mundher (2020) Forecasting long-term precipitation for water resource management: a new multi-step data-intelligent modelling approach. Hydrological Sciences Journal, 65 (16). pp. 2693-2708. ISSN 0262-6667


Abstract

A new multi-step, hybrid artificial intelligence-based model is proposed to forecast future precipitation anomalies using relevant historical climate data coupled with large-scale climate oscillation features derived from the most relevant synoptic-scale climate mode indices. First, NSGA (non-dominated sorting genetic algorithm), as a feature selection strategy, is incorporated to search for statistically relevant inputs from climate data (temperature and humidity), sea-surface temperatures (Niño3, Niño3.4 and Niño4) and synoptic-scale indices (SOI, PDO, IOD, EMI, SAM). Next, the SVD (singular value decomposition) algorithm is applied to decompose all selected inputs, thus capturing the most relevant oscillatory features more clearly; then, the monthly lagged data are incorporated into a random forest model to generate future precipitation anomalies. The proposed model is applied in four districts of Pakistan and benchmarked by means of a standalone kernel ridge regression (KRR) model that is integrated with NSGA-SVD (hybrid NSGA-SVD-KRR) and the NSGA-RF and NSGA-KRR baseline models. Based on its high predictive accuracy and versatility, the new model appears to be a pertinent tool for precipitation anomaly forecasting.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 -)
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 -)
Date Deposited: 03 Nov 2020 01:46
Last Modified: 23 Apr 2021 00:25
Uncontrolled Keywords: multi-step model, precipitation forecasting, large scale climate indices, non-dominated sorting genetic algorithm (NSGA), singular value decomposition (SVD), random forest (RF), water resources management
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080105 Expert Systems
05 Environmental Sciences > 0502 Environmental Science and Management > 050206 Environmental Monitoring
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4605 Data management and data science > 460510 Recommender systems
41 ENVIRONMENTAL SCIENCES > 4104 Environmental management > 410404 Environmental management
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970105 Expanding Knowledge in the Environmental Sciences
Identification Number or DOI: https://doi.org/10.1080/02626667.2020.1808219
URI: http://eprints.usq.edu.au/id/eprint/40016

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