Babak Mohammadi
Doktorand
Application of multiple spatial interpolation approaches to annual rainfall data in the Wadi Cheliff basin (north Algeria)
Författare
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.
Avdelning/ar
- Institutionen för naturgeografi och ekosystemvetenskap
Publiceringsår
2024-03
Språk
Engelska
Publikation/Tidskrift/Serie
Ain Shams Engineering Journal
Volym
15
Issue
3
Dokumenttyp
Artikel i tidskrift
Förlag
Ain Shams University
Ämne
- Physical Geography
- Water Engineering
Nyckelord
- Spatial interpolation
- Deterministic techniques
- Geostatistical analysis
- Machine learning
- Rainfall
Status
Published
ISBN/ISSN/Övrigt
- ISSN: 2090-4479