Use of Artificial Neural Networks and Multiple Linear Regression Model for the Prediction of Dissolved Oxygen in Rivers: Case Study of Hydrographic Basin of River Nyando, Kenya

<div>Mean annual variations of water quality parameters in the Nyando River basin from 2006–2011: (a) total phosphates; (b) total nitrates; (c) electrical conductivity; (d) turbidity; (e) pH; (f) temperature; (g) discharge; (h) total suspended solids (TSSs); (i) dissolved oxygen (the box plots denote 25th, 50th, and 75th percentiles and the whiskers indicate the 5th and 95th percentiles). (j) Spatial distribution of DO concentration within the basin.</div>

Complexity

fig2

Figure 2

Figure 2: Use of Artificial Neural Networks and Multiple Linear Regression Model for the Prediction of Dissolved Oxygen in Rivers: Case Study of Hydrographic Basin of River Nyando, Kenya 

Figure 2 | Use of Artificial Neural Networks and Multiple Linear Regression Model for the Prediction of Dissolved Oxygen in Rivers: Case Study of Hydrographic Basin of River Nyando, Kenya 