A neural network-based prediction model in water monitoring networks
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 optima...
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
IWA Publishing
2021
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/56e2819a9fc84021b0c270cbc14599ea |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| Sumario: | 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.; |
|---|