Implementação de modelos computacionais na predição temporal e espaço-temporal de parâmetros de qualidade de água

Abstract

The quality of water is directy related to is level of pollution caused by anthropic and industrial actions, with a consequent reduction in the availability of quality water. Therefore, limological monitorig of the basic parameters os water quality is carried out, as away of obtaining data that guide the decision-making of water resouces management bodies. In this context, the present study has the implementation of machine learning algorithms to predict temporal and spatiotemporal water quality parameter data. The ML techniques used were linear regression, ramdom forest, MLP and LSTM neural networks. Two collection points from a Water Resources Management Unit in São Paulo, Brazil were used. Models are evaluated using MAPE( mean absolute percentage eror) and RMSE( root mean squared erro) metrics. Therefore, in temporal prediction, the LSTM technique presented the best performace in relation to the other techniques and the data used, as it has the lowest average RMSE result, with 2.47. However, in spatiotemporal prediction, MLP has the best performace both in relation to the other techniques and the data used , as it has the lowest averagee results of MAPE and RMSE, respectively, 5.94% and 1.34. Thus, these performaces of neural networks can be justified by the non-linearity of the parameter data. Other than that, the results of the experiments aim to contribute to the water quality monitorng process and assist in the planning of water management, so that it meets current legislation and enales the indication of public policies, through machine learning models in prediction of water quality parametes.