Forecasting long-term precipitation for water resource management: a new multi-step data-intelligent modelling approach
Article
Article Title | Forecasting long-term precipitation for water resource management: a new multi-step data-intelligent modelling approach |
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ERA Journal ID | 30188 |
Article Category | Article |
Authors | Ali, Mumtaz (Author), Deo, Ravinesh C. (Author), Xiang, Yong (Author), Li, Ya (Author) and Yaseen, Zaher Mundher (Author) |
Journal Title | Hydrological Sciences Journal |
Journal Citation | 65 (16), pp. 2693-2708 |
Number of Pages | 16 |
Year | 2020 |
Place of Publication | United Kingdom |
ISSN | 0262-6667 |
2150-3435 | |
Digital Object Identifier (DOI) | https://doi.org/10.1080/02626667.2020.1808219 |
Web Address (URL) | https://www.tandfonline.com/doi/full/10.1080/02626667.2020.1808219 |
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. |
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 |
ANZSRC Field of Research 2020 | 460510. Recommender systems |
410404. Environmental management | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Deakin University |
School of Sciences | |
Southwest University, China | |
Ton Duc Thang University, Vietnam | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q5z4y/forecasting-long-term-precipitation-for-water-resource-management-a-new-multi-step-data-intelligent-modelling-approach
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