River flow modelling for flood prediction using artificial neural network in ungauged Perkerra catchment, Baringo County, Kenya

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Abstract

rtificial neural network (ANN) modelling has been applied successfully in hydrology to predict future flows based on previous rainfall-runoff values. For a long time, flooding has been experienced in the surrounding areas of the Rift Valley lakes including Lake Baringo, fed by the River Perkerra, due to the rising water levels because of the above-normal rainfall season, resulting in massive socioeconomic losses. The study aims at predicting the occurrence of floods in River Perkerra using an ANN model with the input data being 417 consistent pairs of daily rainfall and discharge, and simulated runoff as the output. The model was trained, tested and validated producing a best fit regression with R2 of 0.951 for training, 0.938 for validation, 0.953 for testing giving an average of 0.949 indicating a close relationship between the input and output values. The overall best validation performance, RMSE, was 0.9204 m3/s indicating high efficiency of the FFNN model developed to predict floods. Flows greater than 14 m3/s, Q1, were the extreme flood events closely associated with socioeconomic losses. This prediction of Q1 value is crucial in the formulation and implementation of measures and policies by the County Government that will mitigate adverse impacts of predicted floods in the catchment.

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River flow modelling for flood prediction using artificial neural network in ungauged Perkerra catchment, Baringo County, Kenya

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