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Babak Mohammadi

Doctoral student

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

Author

  • Mohammed Achite
  • Paraskevas Tsangaratos
  • Gaetano Pellicone
  • Babak Mohammadi
  • Tommaso Caloiero

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.

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

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