A neural network-based prediction model in water monitoring networks

Abstract

To improve the prediction accuracy of ammonia nitrogen in water monitoring networks, the combination of a bio-inspired algorithm and back propagation neural network (BPNN) has often been deployed. However, due to the limitations of the bio-inspired algorithm, it would also fall into the local optimal. In this paper, the seagull optimization algorithm (SOA) was used to optimize the structure of BPNN to obtain a better prediction model. Then, an improved SOA (ISOA) was proposed, and the common functional validation method was used to verify its optimization performance. Finally, the ISOA was applied to improve BPNN, which is known as the improved seagull optimization algorithm–back propagation (ISOA–BP) model. The simulation results showed that the prediction accuracy of ammonia nitrogen was greatly improved and the proposed model can be better applied to the prediction of complex water quality parameters in water monitoring networks. HIGHLIGHTS The structure of BPNN was optimized to obtain a better prediction model by using the seagull optimization algorithm (SOA).; We proposed an improving SOA (ISOA) and used the common functional validation method to verify its optimization performance.; The ISOA was used to improve BPNN, via the improved seagull optimization algorithm–back propagation (ISOA–BP) model.;

Keywords

• ammonia nitrogen prediction
• back propagation neural network
• improved seagull optimization algorithm
• neural network
• seagull optimization algorithm
• water monitoring