Jheklos Gomes da Silva, Ricardo André Cavalcante de Souza, Obionor De Oliveira Nobrega
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Machine learning models applied to estimate the water temperature of rivers and reservoirs
Water temperature in rivers and reservoirs plays a crucial role in aquatic ecology, as inadequate conditions can promote the overgrowth of harmful algae and bacteria, resulting in the production of harmful toxins for human and animal health, and affecting water quality. To effectively manage water resources, continuous monitoring of these bodies is crucial. However, existing technological devices rarely offer continuous and real-time data collection, necessitating an alternative approach. The aim of this study was to compare the performance of four machine learning models (Linear Regression, Stochastic Model, Extra Tree, and Multilayer Perceptron Neural Network) in estimating water temperature in Pernambuco, Brazil's rivers and reservoirs. Statistical metrics showed that all models achieved a satisfactory capacity, with the Multilayer Perceptron Neural Network demonstrating slightly superior performance in reservoirs and rivers where it obtained the best result with a Mean Squared Error: 0.343, Root Mean Squared Error: 0.585, Mean Absolute Error: 0.445 and Coefficient of Determination: 0.595. Consequently, the MLPNN model was chosen for the development of virtual sensors. In addition to an interface that allows users to access a map and obtain estimated water temperature information for various locations, facilitating informed decision-making and resource management.
期刊介绍:
The main goal of this journal is the enhancement of academic activities of IMEKO and a wider dissemination of scientific output from IMEKO TC events. High-quality papers presented at IMEKO conferences, workshops or congresses are seleted by the event organizers and the authors are invited to publish an enhanced version of their paper in this journal. The journal also publishes scientific articles on measurement and instrumentation not related to an IMEKO event.