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Abstract
A fuzzy neural network method is proposed to predict minimum daily dissolved oxygen concentration in the Bow River, in Calgary, Canada. Owing to the highly complex and uncertain physical system, a data-driven and fuzzy number based approach is preferred over traditional approaches. The inputs to the model are abiotic factors, namely water temperature and flow rate. An approach to select the optimum architecture of the neural network is proposed. The total uncertainty of the system is captured in the fuzzy numbers weights and biases of the neural network. Model predictions are compared to the traditional, non-fuzzy approach, which shows that the proposed method captures more low DO events. Model output is then used to quantify the risk of low DO for different conditions.
Highlights
The dissolved oxygen (DO) concentration of a water body is the most fundamental indicator of overall aquatic ecosystem health [1,2,3,4,5]
50%:25%:25% data-division scenario, with nH varying between 1 and 20 neurons. This simulation was repeated 100 times to account for the random selection of data and the upper and lower limits of mean squared error (MSE) for each of these simulations are shown in grey
Intermediate intervals are necessary the results demonstrate that symmetric about the modal value
Summary
The dissolved oxygen (DO) concentration of a water body is the most fundamental indicator of overall aquatic ecosystem health [1,2,3,4,5]. Low DO concentrations can increase the risk of adverse effects to the aquatic environment. While the impact of long-term effects is largely unknown, low DO can have immediate and devastating effects on ecosystems [6]. Changes in watershed land-use due to urbanisation, the interaction of numerous factors, over a relatively small area and across different temporal scales means that DO is difficult to predict in urban areas [2,8]. Rapid changes in the urban environment (e.g., land-use changes or major flood events) means that the factors and regimes influencing DO in the riverine environment might change rapidly
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