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Forecasting long-term precipitation for water resource management: a new multi-step data-intelligent modelling approach
Hydrological Sciences Journal ( IF 3.5 ) Pub Date : 2020-11-10 , DOI: 10.1080/02626667.2020.1808219
Mumtaz Ali 1 , Ravinesh C. Deo 2 , Yong Xiang 1 , Ya Li 3 , Zaher Mundher Yaseen 4
Affiliation  

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.

中文翻译:

预测水资源管理的长期降水:一种新的多步数据智能建模方法

摘要 提出了一种新的基于多步、混合人工智能的模型,利用相关的历史气候数据以及从最相关的天气尺度气候模式指数得出的大尺度气候振荡特征来预测未来的降水异常。首先,将 NSGA(非支配排序遗传算法)作为特征选择策略,从气候数据(温度和湿度)、海面温度(Niño3、Niño3.4 和 Niño4)和天气数据中搜索统计相关的输入。 - 尺度指数(SOI、PDO、IOD、EMI、SAM)。接下来,应用SVD(奇异值分解)算法来分解所有选定的输入,从而更清晰地捕捉最相关的振荡特征;然后,每月滞后数据被纳入随机森林模型,以生成未来的降水异常。所提出的模型应用于巴基斯坦的四个地区,并通过与 NSGA-SVD(混合 NSGA-SVD-KRR)以及 NSGA-RF 和 NSGA-KRR 基线模型集成的独立核岭回归(KRR)模型进行基准测试. 基于其高预测准确性和多功能性,新模型似乎是降水异常预测的相关工具。
更新日期:2020-11-10
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