Flood Area Prediction using a Stacked Ensemble of Tree-Based Algorithms

Olusogo Julius Adetunji

doi:10.35377/saucis...1626057

EN

Flood Area Prediction using a Stacked Ensemble of Tree-Based Algorithms

Abstract

Floods cause significant loss of life, property damage, and long-term socioeconomic disruptions, with over 100 annual deaths globally. This research addresses the drawbacks of the existing models, such as overfitting effects, inadequate dataset and limited study areas through the adoption of a stacked ensemble-based model. The model contained five different tree - based models namely hoeffding tree, decision tree, functional tree, reduced error pruning (REP) tree and decision stump algorithms. The model was implemented as a system using MATLAB Simulink, version 2020a on laptop with 4GB Memory. Experimental results indicate that REP Tree performed better than other four individual tree algorithms with accuracy of 98.74%, 97.81% and 97.43% for Dataset A, Dataset B and Dataset C respectively. For Dataset A, stacked ensemble model performed better than single algorithms with accuracy, precision, specificity, f1score and recall of 99.62%, 99.51%, 99.51%, 99.63% and 99.73% respectively. For Dataset B, stacked ensemble model also performed better than single algorithms with accuracy, precision, specificity, f1score and recall of 98.45%, 99.11%, 98.12%, 97.37% and 99.06% respectively. For Dataset C, stacked ensemble model performed better than single algorithms with accuracy, precision, specificity, f1score and recall of 98.75%, 99.25%, 99.64%, 99.90% and 99.24% respectively. Our model’s 99.62% accuracy on Dataset A demonstrates potential for integration with real-time sensor networks, enabling scalable flood early-warning systems in vulnerable regions like Lagos and Kuala Lumpur.

Keywords

References

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Details

Primary Language

English

Subjects

Computer Software

Journal Section

Research Article

Authors

Olusogo Julius Adetunji ^*
0000-0001-6056-2476
Nigeria

Early Pub Date

June 30, 2025

Publication Date

June 30, 2025

Submission Date

January 25, 2025

Acceptance Date

June 13, 2025

Published in Issue

Year 2025 Volume: 8 Number: 2

DOI

https://doi.org/10.35377/saucis...1626057

IZ

https://izlik.org/JA27ZC22LY

Cite

RIS / Bibtex

APA

Adetunji, O. J. (2025). Flood Area Prediction using a Stacked Ensemble of Tree-Based Algorithms. Sakarya University Journal of Computer and Information Sciences, 8(2), 322-345. https://doi.org/10.35377/saucis...1626057

AMA

1.Adetunji OJ. Flood Area Prediction using a Stacked Ensemble of Tree-Based Algorithms. SAUCIS. 2025;8(2):322-345. doi:10.35377/saucis.1626057

Chicago

Adetunji, Olusogo Julius. 2025. “Flood Area Prediction Using a Stacked Ensemble of Tree-Based Algorithms”. Sakarya University Journal of Computer and Information Sciences 8 (2): 322-45. https://doi.org/10.35377/saucis. 1626057.

EndNote

Adetunji OJ (June 1, 2025) Flood Area Prediction using a Stacked Ensemble of Tree-Based Algorithms. Sakarya University Journal of Computer and Information Sciences 8 2 322–345.

IEEE

[1]O. J. Adetunji, “Flood Area Prediction using a Stacked Ensemble of Tree-Based Algorithms”, SAUCIS, vol. 8, no. 2, pp. 322–345, June 2025, doi: 10.35377/saucis...1626057.

ISNAD

Adetunji, Olusogo Julius. “Flood Area Prediction Using a Stacked Ensemble of Tree-Based Algorithms”. Sakarya University Journal of Computer and Information Sciences 8/2 (June 1, 2025): 322-345. https://doi.org/10.35377/saucis. 1626057.

JAMA

1.Adetunji OJ. Flood Area Prediction using a Stacked Ensemble of Tree-Based Algorithms. SAUCIS. 2025;8:322–345.

MLA

Adetunji, Olusogo Julius. “Flood Area Prediction Using a Stacked Ensemble of Tree-Based Algorithms”. Sakarya University Journal of Computer and Information Sciences, vol. 8, no. 2, June 2025, pp. 322-45, doi:10.35377/saucis. 1626057.

Vancouver

1.Olusogo Julius Adetunji. Flood Area Prediction using a Stacked Ensemble of Tree-Based Algorithms. SAUCIS. 2025 Jun. 1;8(2):322-45. doi:10.35377/saucis. 1626057