Machine learning for flood forecasting:case study: Nzoia river basin, western Kenya.
Abstract
In Kenya property destruction and loss of life has occurred due to serious incidents of floods,
along the Nzoia River catchment area Western Kenya. Despite having flood warning models
along the Nzoia River basin; with a flood warning system at Rwambwa gauge station that sends
out alerts on the river levels. These models are linear models and have overlooked the peak
streamflows. A reliable intelligent nonlinear model that is capable of handling nonlinear
estimation streamflow (discharge) problem is crucial in flood control operations.
This research explores applicability and performance of flood forecasting models in the Nzoia
River basin, Western Kenya, using two types of artificial neural network (ANNs), namely MLPANN-FF
a feedforward multilayer perceptron (MLP) network and GA-ANN-FF a genetic
algorithm optimized multilayer perceptron feedforward neural network model. The aim of this
study is to compare the performance of these two models (MLP-ANN-FF and GA-ANN-FF) and
recommend the most suitable for this problem.
The historical daily rainfall, and average temperature and discharge flow, obtained from Kenya
Metrological Department (KMD) were used as inputs to the two ANN models for discharge flow
(streamflow) forecast for Nzoia River basin at Rwambwa river gauge. The characteristic
parameters such as number of neurons within hidden layers and the selection of input variables
for the MLP-ANN-FF were optimized using genetic algorithm (GA), hence yielding a GA-ANNFF
model. These two models were trained, cross verified and tested with daily rainfall, average
temperature, and discharge flow.
The architectural topology that trained well on MLP-ANN-FF model was one with 9 input
variables, 2 hidden layers and 1 discharge flow output; a 9:7:12:1 configuration setting. This was
later optimized with the genetic algorithm (GA) to develop a GA-ANN-FF model that was able
to optimize the input variables reducing them from 9 to 4 inputs, and reducing the number of
neurons in the 2 hidden layers yielding a 4:6:4:1 GA-ANN-FF model.
The conventional ANN (MLP-ANN-FF) and a GA-ANN-FF model were used as the benchmark
10% was used in testing the overall performance of the models. The results revealed that the GAANN-FF
(4:6:4:1) model was able to yield better accuracy in performance for Nzoia River basin
at Rwambwa River gauge, with least input variables, and number of neurons in the hidden layers
though it took longer on the computation time. With a MSE of 0.021 and an r (correlation
coefficient) of the desired and estimated discharge flow of 0.887 (89%), GA-ANN-FF performed
satisfactory better than MLP-ANN-FF (9:7:12:1) with 9 input variables an MSE of 0.024 and r
(correlation coefficient) of 0.84 (80%).
The results showed that ANN integrated with GA has a better accuracy and therefore most
suitable in developing flood forecast models with low MSE. This finding is important because it
will eventually enable relevant agents in water resource planning and flood management and the
public aware when a flood might occur and the areas that would be affected to avoid disaster
caused by floods.
Citation
School of Computing and Informatics,Publisher
University of Nairobi
Description
Masters