DJCCMT

Delta Journal of Computing, Communications and Media Technologies

ISSN:3092-8478

Advancing research and innovation at the intersection of computing technology and media. A publication of Southern Delta University, Ozoro.

Delta Journal of Computing, Communications and Media Technologies(DJCCMT) is an open access double-blind peer reviewed and refereed Journal that brings together reasoned thoughts, research, and industry practice in areas of Computing, Artificial Intelligence, Robotics, System Engineering, Data Science, Analytics, Embedded Systems, Information and System Security, Media Studies, Communication Technologies, Information Science, Library Science, Educational Technologies, Applied Computing, and related disciplines in a reader-friendly format. The Journal is published online monthly with print version issue in February, May, August and November.

Delta Journal of Computing, Communications and Media Technologies

Volume 1 · Issue 1 · December 2024
Title of Paper

AN EXPLAINABLE RANDOM FOREST FRAMEWORK FOR FLOOD PROBABILITY FORECASTING: A COMPARATIVE STUDY WITH HISTORICAL FLOOD RECORDS

Abstract

This study presents an explainable Random Forest framework for forecasting flood probability and evaluates its performance against historical flood records. Leveraging a publicly available hydrometeorological dataset sourced from kaggle.com, which includes rainfall, river discharge, soil moisture, and topographic variables, we preprocessed and partitioned the data into training (70 %) and testing (30 %) subsets. A Random Forest classifier was trained to predict binary flood events, and model explainability was achieved using SHAP (SHapley Additive exPlanations) values to quantify the contribution of each feature to individual predictions. Model performance was assessed through accuracy, precision, recall, F1‐score, and area under the ROC curve (AUC), and these metrics were compared to the documented occurrence of flood events in the historical record. Our framework achieved an AUC of 0.92, with precision and recall exceeding 0.85, indicating robust predictive capability. The SHAP‐based analysis revealed that antecedent rainfall, upstream discharge, and soil moisture were the most influential predictors, aligning closely with known flood‐generation mechanisms. A comparative analysis demonstrated that the explainable model not only matches but, in some cases, surpasses the baseline skill of traditional statistical approaches documented in regional flood reports. Furthermore, case‐study examinations of selected flood events highlight how feature contributions evolve in different hydrological contexts, offering actionable insights for risk managers. With high predictive accuracy with transparent interpretability, this work advances flood forecasting tools for operational deployment and supports data‐driven decision‐making in flood risk management.

Authors

Diala Leona Concord, Igwe Christian Uzoma,Chinedu Nkechi Blessing,Abah Emmanuel,Eromosele Peace O.,Chinedu Paschal Uchenna,Nwankwo Wilson

Keywords

AI,Random Forest ,Flood Forecasting ,SHAP Values ,Hydrometeorological Data, Flood Risk Assessment

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