Abstract:
The process of predicting water quality over a catchment area is complex due to the inherently nonlinear interactions between the
water quality parameters and their temporal and spatial variability. The empirical, conceptual, and physical distributed models for
the simulation of hydrological interactions may not adequately represent the nonlinear dynamics in the process of water quality
prediction, especially in watersheds with scarce water quality monitoring networks. To overcome the lack of data in water quality
monitoring and prediction, this paper presents an approach based on the feedforward neural network (FNN) model for the
simulation and prediction of dissolved oxygen (DO) in the Nyando River basin in Kenya. To understand the influence of the
contributing factors to the DO variations, the model considered the inputs from the available water quality parameters (WQPs)
including discharge, electrical conductivity (EC), pH, turbidity, temperature, total phosphates (TPs), and total nitrates (TNs) as
the basin land-use and land-cover (LULC) percentages. The performance of the FNN model is compared with the multiple linear
regression (MLR) model. For both FNN and MLR models, the use of the eight water quality parameters yielded the best DO
prediction results with respective Pearson correlation coefficient R values of 0.8546 and 0.6199. In the model optimization, EC, TP,
TN, pH, and temperature were most significant contributing water quality parameters with 85.5% in DO prediction. For both
models, LULC gave the best results with successful prediction of DO at nearly 98% degree of accuracy, with the combination of
LULC and the water quality parameters presenting the same degree of accuracy for both FNN and MLR models.
