Abstract

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.

Highlights

  • The impact of urbanization on urban streams has been well established, with the term urban stream syndrome (USS) commonly used to describe the detrimental effects of high urbanization on the aquatic health of streams

  • Temperature is an important factor in the impairment of aquatic habitat suitability within urban streams [8,9,10]

  • Brook trout, which is common in Southern Ontario, has a chronic temperature limit of 19 ◦C and a critical thermal maximum (CTMax) of 29 ◦C [16]

Read more

Summary

Introduction

The impact of urbanization on urban streams has been well established, with the term urban stream syndrome (USS) commonly used to describe the detrimental effects of high urbanization on the aquatic health of streams. Temperature is an important factor in the impairment of aquatic habitat suitability within urban streams [8,9,10]. Increased temperatures can impact streams in multiple ways, such as by increasing chemical and biological processes, which in turn reduces dissolved oxygen and causes stress in the aquatic organisms [11,12]. Fish species in cold-water streams are vulnerable to rapid and chronic temperature increases since it increases their susceptibility to diseases and parasites, reduces reproductive success, and retards the growth of juveniles [13,14,15]. The redside dace is a small fish in the minnow family that inhabits cool- and cold-water streams with a target water temperature of less than 24 ◦C [17]. The ability to forecast stream temperature is crucial to the protection of cold- and cool-water habitats from anthropogenic heat sources

Objectives
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call