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.

Real-time monitoring of river water quality is at the forefront of a proactive urban water management strategy to meet the global challenge of vital freshwater resource sustainability. The concentration of dissolved oxygen (DO) is a primary indicator of the health state of the aquatic habitats, and its modeling is crucial for river water quality management. This paper investigates the importance of the choices of different techniques for preprocessing and stochastic modeling for developing a simple and reliable linear stochastic model for forecasting DO in urban rivers. We describe several methods of evaluation, preprocessing, and modeling for the DO parameter time series in the Credit River, Ontario, Canada, to achieve the optimum data preprocessing and input selection techniques and consequently obtain the optimum performance of the stochastic models as an effective river management tool. The Manly normalization and standardization (Std) methods were chosen for preprocessing the time series. Modeling the preprocessed time series using the stochastic autoregressive integrated moving average (ARIMA) model resulted in very accurate forecasts with a negligible difference from sole normalization and spectral analysis (Sf) methods.

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