Spatio-temporal evolution of mineral phosphorus modelling in tropical hypereutrophic lagoon bay: Tiagba Lagoon Bay (Côte d′Ivoire)
This work describes a new approach to prediction of spatio-temporal evolution of mineral phosphorus in water bodies, particularly in Tiagba Lagoon Bay. Originality of this study lie on the use of artificial neural networks, principally multilayer perceptron, as modelling tool. Two approaches of spatio-temporal evolution of this nutrient were done: static evolution and dynamic evolutions. Data bases, formed by 3 966 and 4 627 data, served for static and dynamic modelling of this nutrient respectively. Weights of network connection are determined using Levenberg-Marquardt algorithm during execution of Multilayer Perceptron. Results obtained show that models 5-14-1 and 6-14-2 can predict to 70.30% and approximately 70% respectively for static and dynamic evolution of mineral phosphorus in this bay. These models, judged satisfactory, could be used for other studies led to rehabilitation and protection of this aquatic ecosystem for its long development.