Quest for Empirical Solution to Runoff Prediction in Nigeria via Random Forest Ensemble: Pilot Study

Domaine:

climateenvironment and energy

Type de record:

paper

Créateur:

DepB.OE.BW A

Éditeur:

Cre

Hôte:

Environmental factors and features often change to result in either harsh weather conditions or rainfall, which often calms the weather as well as provides fast, significant downstream hydrology known as runoff with a variety of implications such as erosion, water quality, and infrastructures. These, in turn, impact the quality of life, sewage systems, agriculture, and tourism of a nation, to mention a few. It chaotic, complex and dynamic nature has necessitated studies in the quest for future direction of such runoff via prediction models. With little successes in use of knowledge driven models – many studies have now turned to data-driven models. Dataset is retrieved from Metrological Center in Lagos, Nigeria for the period 1999–2023. The retrieved dataset was split: 70% for train dataset, and 30% for test dataset. Our study used the Random Forest ensemble. Result yields a sensitivity of 0.9, specificity 0.19, accuracy of 0.74, and improvement rate of 0.12. Other ensembles underperformed as compared to proposed model. Study reveals annual rainfall is an effect of variation cycle. Models will help simulate future floods and provide, lead time warnings in flood management. Keywords: Runoff, Random Forest, flood resources, hydrology management, Nigeria

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