Genetic-Algorithm-Optimized Sequential Model for Water Temperature Prediction

Sustainability

Pijush Samui¹  Deepak Kumar¹  Hossein Bonakdari²  Bahram Gharabaghi³  Stephen Stajkowski³ 

[1] Department of Civil Engineering, National Institute of Technology Patna, Patna-800001, India;Department of Soils and Agri-Food Engineering, Laval University, Québec, QC G1V0A6, Canada;School of Engineering, University of Guelph, Guelph, ON NIG 2W1, Canada;

关键词: sequential model;    water temperature;    LSTM;    genetic algorithm;   

DOI  :  10.3390/su12135374

来源: DOAJ

【 摘 要 】

Advances in establishing real-time river water quality monitoring networks combined with novel artificial intelligence techniques for more accurate forecasting is at the forefront of urban water management. The preservation and improvement of the quality of our impaired urban streams are at the core of the global challenge of ensuring water sustainability. This work adopted a genetic-algorithm (GA)-optimized long short-term memory (LSTM) technique to predict river water temperature (WT) as a key indicator of the health state of the aquatic habitat, where its modeling is crucial for effective urban water quality management. To our knowledge, this is the first attempt to adopt a GA-LSTM to predict the WT in urban rivers. In recent research trends, large volumes of real-time water quality data, including water temperature, conductivity, pH, and turbidity, are constantly being collected. Specifically, in the field of water quality management, this provides countless opportunities for understanding water quality impairment and forecasting, and to develop models for aquatic habitat assessment purposes. The main objective of this research was to develop a reliable and simple urban river water temperature forecasting tool using advanced machine learning methods that can be used in conjunction with a real-time network of water quality monitoring stations for proactive water quality management. We proposed a hybrid time series regression model for WT forecasting. This hybrid approach was applied to solve problems regarding the time window size and architectural factors (number of units) of the LSTM network. We have chosen an hourly water temperature record collected over 5 years as the input. Furthermore, to check its robustness, a recurrent neural network (RNN) was also tested as a benchmark model and the performances were compared. The experimental results revealed that the hybrid model of the GA-LSTM network outperformed the RNN and the basic problem of determining the optimal time window and number of units of the memory cell was solved. This research concluded that the GA-LSTM can be used as an advanced deep learning technique for time series analysis.

【 授权许可】

Unknown