Abstract
To analyze the cumulative risks to the water environment, the backpropagation artificial neural network (BP-ANN), a self-adapting algorithm, was proposed in this study. A new comprehensive indicator of cumulative risks was formed by combining the water risk assessment tool proposed by the World Wide Fund for Nature or World Wildlife Fund (WWF), Deutsche Investitions und Entwicklungsgesellschaft mbH (DEG), and the cumulative environmental risk assessment system proposed by the US Environmental Protection Agency (USEPA). Eleven training algorithms were selected and optimized based on the mean square error (MSE) of prediction results. Data concerning evaluating indicators and cumulative risk indexes of the Liao River collected from 2005 to 2017 in the cities of Tieling, Shenyang, and Panjin, China, were used as input and output data to train, validate, and test the BP-ANN. Levenberg Marquardt backpropagation was the most accurate algorithm, with an MSE of 3.33 × 10−6. After optimization, there were six hidden layers in the model. The correlation coefficient of the BP-ANN with LM exceeded 80%. These findings suggest that the BP-ANN model is applicable to prediction of cumulative risks to the water environment. The model was sensitive to the number of wastewater treatment facilities and the wastewater treatment rate along the river. Based on the sensitivity analysis, the contributing factors can be controlled to reduce the cumulative risk.
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Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
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This research was supported by National Science and Technology Major Project (grant number 2018ZX07601001) and Natural Science Foundation of Liaoning Province (grant number 20180510052).
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ES revised it critically for intellectual content. YS analyzed and interpreted the data regarding the Liao River and was a major contributor in writing the manuscript. YL was accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. MZ managed and coordinated the research activity planning and execution.
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Shi, E., Shang, Y., Li, Y. et al. A cumulative-risk assessment method based on an artificial neural network model for the water environment. Environ Sci Pollut Res 28, 46176–46185 (2021). https://doi.org/10.1007/s11356-021-12540-6
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DOI: https://doi.org/10.1007/s11356-021-12540-6