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

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.

Report Access / View File

Overall Rating

0

5 Star

(0)

4 Star

(0)

3 Star

(0)

2 Star

(0)

1 Star

(0)

APA

Jeruto, C (2024). River flow modelling for flood prediction using artificial neural network in ungauged Perkerra catchment, Baringo County, Kenya. Afribary. Retrieved from https://tracking.afribary.com/works/river-flow-modelling-for-flood-prediction-using-artificial-neural-network-in-ungauged-perkerra-catchment-baringo-county-kenya

MLA 8th

Jeruto, Chebii "River flow modelling for flood prediction using artificial neural network in ungauged Perkerra catchment, Baringo County, Kenya" Afribary. Afribary, 04 Jun. 2024, https://tracking.afribary.com/works/river-flow-modelling-for-flood-prediction-using-artificial-neural-network-in-ungauged-perkerra-catchment-baringo-county-kenya. Accessed 21 Nov. 2024.

MLA7

Jeruto, Chebii . "River flow modelling for flood prediction using artificial neural network in ungauged Perkerra catchment, Baringo County, Kenya". Afribary, Afribary, 04 Jun. 2024. Web. 21 Nov. 2024. < https://tracking.afribary.com/works/river-flow-modelling-for-flood-prediction-using-artificial-neural-network-in-ungauged-perkerra-catchment-baringo-county-kenya >.

Chicago

Jeruto, Chebii . "River flow modelling for flood prediction using artificial neural network in ungauged Perkerra catchment, Baringo County, Kenya" Afribary (2024). Accessed November 21, 2024. https://tracking.afribary.com/works/river-flow-modelling-for-flood-prediction-using-artificial-neural-network-in-ungauged-perkerra-catchment-baringo-county-kenya

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

0

Document Details

Related Works

Status of Micro-Hydrokinetic River Technology Turbines Application for Rural Electrification in Africa

Effect of Micro Lime on The Ambient Cured Sugarcane Bagasse Ash-Based Geopolymer Concrete

Assessment of rainwater harvesting potential of Rachuonyo North Sub-Catchment in Kenya using the Australian water balance model

Influence of Key Shear Factors on the Shear Performance of Ultra-high Performance Fibre Reinforced Concrete Beam Containing Coarse Aggregate

Impact of Alkaline Concentration on the Mechanical Properties of Geopolymer Concrete Made up of Fly Ash and Sugarcane Bagasse Ash

Performance of Black Cotton Soil Stabilized with Silica Sand and Lime for Use as Road Subgrade

Assessment of groundwater vulnerability in Webuye Municipality, Kenya