Application of multiple spatial interpolation approaches to annual rainfall data in the Wadi Cheliff basin (north Algeria)

Summary, in English

This study addresses a challenging problem of predicting mean annual precipitation across arid and semi-arid areas in northern Algeria, utilizing deterministic, geostatistical (GS), and machine learning (ML) models. Through the analysis of data spanning nearly five decades and encompassing 150 monitoring stations, the result of Random Forest showed the highest training performance, with R square value (of 0.9524) and the Root Mean Square Error (of 24.98). Elevation emerges as a critical factor, enhancing prediction accuracy in mountainous and complex terrains when used as an auxiliary variable. Cluster analysis further refines our understanding of station distribution and precipitation characteristics, identifying four distinct clusters, each exhibiting unique precipitation patterns and elevation zones. This study helps for a better understanding of precipitation prediction, encouraging the integration of additional variables and the exploration of climate change impacts, thereby contributing to informed environmental management and adaptation strategies across diverse climatic and terrain scenarios.

• Author: Mohammed Achite; Paraskevas Tsangaratos; Gaetano Pellicone; Babak Mohammadi; Tommaso Caloiero
• Department/s: Dept of Physical Geography and Ecosystem Science
• Publishing year: 2024-03
• Language: English
• Publication/Series: Ain Shams Engineering Journal
• Volume: 15
• Issue: 3
• Links: http://dx.doi.org/10.1016/j.asej.2023.102578
• Document type: Journal article
• Publisher: Ain Shams University
• Topic: Physical Geography; Water Engineering
• Keywords: Spatial interpolation; Deterministic techniques; Geostatistical analysis; Machine learning; Rainfall
• Status: Published
• ISBN/ISSN/Other: ISSN: 2090-4479