Skip to content
DraconiticResearch
Search Sources API
178,033 studies31 sourcesUpdated 22 min ago
Peer-reviewedCC BYGeocarto International

Flood susceptibility mapping using ensemble machine learning models and SHAP analysis in the Shebelle River Basin, Somalia

  • Radhwan A. A. Saleh1
  • Ahmed M. Al-Areeq2
  • Abdikarim Moallim Mohamoud2
  • Hafsa Nuradin Mohamed2
  • Husam Musa Baalousha3
  1. 1Faculty of Engineering and Architecture, Nişantaşı University, Istanbul, Turkey
  2. 2Department of Civil and Environmental Engineering, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia
  3. 3Interdisciplinary Research Center for Membranes and Water Security (IRC-MWS), King Fahd University of Petroleum & Minerals (KFUPM), Dhahran, Saudi Arabia
Read full textDOICite

Abstract

The Shebelle River Basin in Somalia is perennially threatened by devastating floods, yet it remains profoundly understudied, with existing conventional models, incomplete variable selection, and a lack of model interpretability. This study addresses these gaps by proposing an Explainable Stacking Ensemble Model designed for high-precision and transparent flood risk assessment. Our sophisticated stacking architecture integrates five diverse base models, XGBoost, LightGBM, and CatBoost, whose predictions are synthesised by a CatBoost meta-classifier to achieve superior predictive performance. The model was trained on a comprehensive set of seventeen conditioning factors using a balanced dataset of 4,154 locations. Our proposed model demonstrated exceptional performance, achieving (Accuracy: 91.26%, F1-Score: 91.36%, ROC-AUC: 0.9723) and outperforming Gradient Boosting Machines, Extra Trees, XGBoost, LightGBM, and CatBoost. Crucially, to dismantle the ‘black-box’ nature of complex ensembles, we embedded SHAP (SHapley Additive exPlanations) analysis directly into the framework. This provides unparalleled interpretability, revealing Topographic Wetness Index (TWI), elevation, rainfall, and stream order as the most influential factors driving flood susceptibility. The resulting high-resolution risk map accurately identifies high-risk zones along the middle and lower reaches of the Shebelle River, aligning with historical events.

Related

  1. Role of Topo-Climatic Factors in Landslide Susceptibility Mapping: A Machine Learning Perspective
    Saba Shakeel Raina, Basudev Biswal
    EarthArXivPreprintCC BY 4.0Full textGeneral earth scienceEarthArXiv
  2. Landslide susceptibility mapping using machine learning models and analytical hierarchy process along NH-513, Pasighat to Yingkiong, Arunachal Pradesh, India
    Mrinaljyoti Adhyapok, Mrinal Kumar Dutta
    Discover GeosciencePeer-reviewedCC BYFull textEnvironmental science & pollutionDOAJ
  3. Multi-hazard susceptibility mapping in a karst context using a machine-learning method (MaxEnt)
    Hedieh Soltanpour, Kamal Serrhini, Joel C. Gill +2
    Natural hazards and earth system sciencesPeer-reviewedCC BYFull text6 citedGeology & geochemistryOpenAlex
  4. Uncertainty analysis of a machine learning-aided ensemble forecast for coastal flood maps
    Jeremy Rohmer, Sophie Lecacheux, Déborah Idier +5
    Stochastic Environmental Research and Risk AssessmentPeer-reviewedPublic domainFull text1 citedClimate scienceHAL
  5. Machine-learning modelling of regional landslide susceptibility in eThekwini, South Africa
    Cher Petersen, Paidamwoyo Mhangara, Laven Naidoo +1
    Scientific ReportsPeer-reviewedCC BY-NC-ND 4.0Full textEnvironmental science & pollutionCrossref

Cite

BibTeXRIS
BibTeX
@article{saleh2026flood,
  title = {{Flood susceptibility mapping using ensemble machine learning models and SHAP analysis in the Shebelle River Basin, Somalia}},
  author = {Radhwan A. A. Saleh and Ahmed M. Al-Areeq and Abdikarim Moallim Mohamoud and Hafsa Nuradin Mohamed and Husam Musa Baalousha},
  journal = {Geocarto International},
  year = {2026},
  month = nov,
  doi = {10.1080/10106049.2026.2756764},
  url = {https://doi.org/10.1080/10106049.2026.2756764},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Flood susceptibility mapping using ensemble machine learning models and SHAP analysis in the Shebelle River Basin, Somalia
AU  - Radhwan A. A. Saleh
AU  - Ahmed M. Al-Areeq
AU  - Abdikarim Moallim Mohamoud
AU  - Hafsa Nuradin Mohamed
AU  - Husam Musa Baalousha
PY  - 2026
DA  - 2026/11/03
JO  - Geocarto International
DO  - 10.1080/10106049.2026.2756764
UR  - https://doi.org/10.1080/10106049.2026.2756764
AB  - The Shebelle River Basin in Somalia is perennially threatened by devastating floods, yet it remains profoundly understudied, with existing conventional models, incomplete variable selection, and a lack of model interpretability. This study addresses these gaps by proposing an Explainable Stacking Ensemble Model designed for high-precision and transparent flood risk assessment. Our sophisticated stacking architecture integrates five diverse base models, XGBoost, LightGBM, and CatBoost, whose predictions are synthesised by a CatBoost meta-classifier to achieve superior predictive performance. The model was trained on a comprehensive set of seventeen conditioning factors using a balanced dataset of 4,154 locations. Our proposed model demonstrated exceptional performance, achieving (Accuracy: 91.26%, F1-Score: 91.36%, ROC-AUC: 0.9723) and outperforming Gradient Boosting Machines, Extra Trees, XGBoost, LightGBM, and CatBoost. Crucially, to dismantle the ‘black-box’ nature of complex ensembles, we embedded SHAP (SHapley Additive exPlanations) analysis directly into the framework. This provides unparalleled interpretability, revealing Topographic Wetness Index (TWI), elevation, rainfall, and stream order as the most influential factors driving flood susceptibility. The resulting high-resolution risk map accurately identifies high-risk zones along the middle and lower reaches of the Shebelle River, aligning with historical events.
SN  - 1752-0762
SN  - 1010-6049
ER  -