Modeling and temporal prediction of water quality parameters using deep neural networks
Abstract
The quality of the water is directly related to its level of pollution, and for that, monitoring is necessary to identify the physical, chemical, and biological characteristics, considering the current legislation. This article presents a comparison of the Long-Short Term Memory (LSTM) and Perceptron Multilayer (MLP) neural network models to predict the pH, OD, BOD, Phosphorus, and Turbidity parameters of water quality. The error metrics RMSE and MSE were used, when the neural networks are configured with 10, 25, and 50 neurons. The LSTM network presented an average RMSE of 0:134, average MSE of 0:035, and average MAPE of 13:49. The MLP network presented average RMSE 0:085, average MSE of 0:01, and average MAPE of 13:03. The results of the experiments aim to contribute to the process of monitoring water quality and to assist water management planning through the appropriate machine learning model for predicting parameters.
Keywords:
Monitoring of water quality, LTSM, MLP
References
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Bandara, K., Bergmeir, C., and Smyl, S. (2020). Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach. Expert Systems with Applications, 140:112896.
Da Silva, I. N., Spatti, D. H., and Flauzino, R. A. (2010). Redes NEurais Artificiais para Engenharia e Ciências Aplicadas: Curso Prático. Artliber.
de Fravet, A. M. M. F. and Cruz, R. L. (2007). Qualidade da água utilizada para irrigação de hortaliças na região de botucatu-sp. Irriga, pages 144–155.
Derisio, J. C. (2016). Introdução ao controle de poluição ambiental. Oficina de Textos.
Dragoi, E., Kovács, Z., Juzsakova, T., Curteanu, S., and Cretescu, I. (2018). Environmental assesment of surface waters based on monitoring data and neuro-evolutive modelling. Process Safety and Environmental Protection, 120:136–145.
Gastaldini, M., Mendonça, A., Paiva, J., and Paiva, E. (2001). Conceitos para a avaliação da qualidade da água. PAIVA, JBD; PAIVA, EMCD Hidrologia aplicada à gestão de pequenas bacias hidrográficas. Porto Alegre: ABRH, pages 428–51.
Gastaldini, M. and Teixeira, E. (2001). Avaliação da qualidade da água. PAIVA, JBD; PAIVA, EMCD Hidrologia aplicada à gestão de pequenas bacias hidrográficas. Porto Alegre: ABRH, pages 453–90.
Goldschmidt, R. and Passos, E. (2005). Data mining: um guia Prático. Elsevier.
Haykin, S. (2007). Redes neurais: princípios e prática. Bookman Editora.
Heddam, S. (2016). Simultaneous modelling and forecasting of hourly dissolved oxygen concentration (do) using radial basis function neural network (rbfnn) based approach: a case study from the klamath river, oregon, usa. Modeling Earth Systems and Environment, 2(3):135.
khandelwal, R. (2019). Overview of different optimizers for neural networks.
Monard, M. C. and Baranauskas, J. A. (2003). Conceitos sobre aprendizado de máquina. Sistemas inteligentes-Fundamentos e aplicações, 1(1):1.
Olah, C. (2015). Understanding lstm networks, 2015. URL http://colah. github. io/posts/2015-08- Understanding-LSTMs.
Sahoo, M. M., Patra, K., and Khatua, K. (2015). Inference of water quality index using anfia and pca. Aquatic Procedia, 4:1099–1106.
Santos, A. M. d., Seixas, J. M. d., Pereira, B. d. B., and Medronho, R. d. A. (2005). Usando redes neurais artificiais e regressão logística na predição da hepatite a. Revista Brasileira de Epidemiologia, 8(2):117– 126.
Sarkar, A. and Pandey, P. (2015). River water quality modelling using artificial neural network technique. Aquatic Procedia, 4:1070–1077.
Solanki, A., Agrawal, H., and Khare, K. (2015). Predictive analysis of water quality parameters using deep learning. International Journal of Computer Applications, 125(9):0975–8887.
Yamak, P. T., Yujian, L., and Gadosey, P. K. (2019). A comparison between arima, lstm, and gru for time series forecasting. In Proceedings of the 2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence, ACAI 2019, page 49–55, New York, NY, USA. Association for Computing Machinery.
Published
2020-06-30
How to Cite
ALMEIDA, Anderson; AMARIS, Marcos; MERLIN, Bruno; VERAS, Allan .
Modeling and temporal prediction of water quality parameters using deep neural networks. In: WORKSHOP ON COMPUTING APPLIED TO THE MANAGEMENT OF THE ENVIRONMENT AND NATURAL RESOURCES (WCAMA), 11. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 121-130.
ISSN 2595-6124.
DOI: https://doi.org/10.5753/wcama.2020.11026.
