Forecasting long-term precipitation for water resource management: a new multi-step data-intelligent modelling approach
Mumtaz Ali, Ravinesh C. Deo, Yong Xiang, Ya Li, Zaher Mundher Yaseen
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.