Application of multiple spatial interpolation approaches to annual rainfall data in the Wadi Cheliff basin (north Algeria)
Achite, Mohammed ; Tsangaratos, Paraskevas ; Pellicone, Gaetano ; Mohammadi, Babak LU
and Caloiero, Tommaso
(2024)
In Ain Shams Engineering Journal
15(3).
- Abstract
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... (More)
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.
(Less)
- author
- Achite, Mohammed
; Tsangaratos, Paraskevas
; Pellicone, Gaetano
; Mohammadi, Babak
LU
and Caloiero, Tommaso
- organization
- publishing date
- 2024-03
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Spatial interpolation, Deterministic techniques, Geostatistical analysis, Machine learning, Rainfall
- in
- Ain Shams Engineering Journal
- volume
- 15
- issue
- 3
- article number
- 102578
- publisher
- Ain Shams University
- external identifiers
-
- scopus:85178174011
- ISSN
- 2090-4479
- DOI
- 10.1016/j.asej.2023.102578
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 2023 Faculty of Engineering, Ain Shams University
- id
- 37cdedb7-e0f9-48cd-bfdc-e32f36b0f374
- date added to LUP
- 2024-01-08 14:44:48
- date last changed
- 2024-01-24 10:53:37
@article{37cdedb7-e0f9-48cd-bfdc-e32f36b0f374,
abstract = {{<p>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.</p>}},
author = {{Achite, Mohammed and Tsangaratos, Paraskevas and Pellicone, Gaetano and Mohammadi, Babak and Caloiero, Tommaso}},
issn = {{2090-4479}},
keywords = {{Spatial interpolation; Deterministic techniques; Geostatistical analysis; Machine learning; Rainfall}},
language = {{eng}},
number = {{3}},
publisher = {{Ain Shams University}},
series = {{Ain Shams Engineering Journal}},
title = {{Application of multiple spatial interpolation approaches to annual rainfall data in the Wadi Cheliff basin (north Algeria)}},
url = {{http://dx.doi.org/10.1016/j.asej.2023.102578}},
doi = {{10.1016/j.asej.2023.102578}},
volume = {{15}},
year = {{2024}},
}