Abstract
River ecological health evaluation is a complex problem, and although there are many existing river evaluation methods, it is still a challenge to synthesize them and better express the nonlinear relationships among multiple influencing factors of river ecosystems. In this paper, we try to evaluate the ecological health of Dagu River using artificial neural network, and train and validate the designed network to establish the corresponding river health evaluation model of Dagu River. The analysis of the results shows that the mean absolute error (MAE) of the test data is 4.98, and as a comparison, the MAE is 8.125 when multiple linear regression is chosen as the baseline algorithm, indicating that the artificial neural network can accurately evaluate the river health condition of Dagu River. By using artificial neural network to evaluate the river health, the method has wide practicability and provides the basis for the credibility of river health.
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References
Shan C, Dong Z, Lu D et al (2021) Study on river health assessment based on a fuzzy matter-element extension model. Ecol Ind 127:107742
Zhou Y, Yue D, Li S et al (2022) Ecosystem health assessment in debris flow-prone areas: a case study of Bailong River Basin in China. J Clean Prod 357:131887
Petesse ML, Siqueira-Souza FK, de Carvalho Freitas CE et al (2016) Selection of reference lakes and adaptation of a fish multimetric index of biotic integrity to six amazon floodplain lakes. Ecol Eng 97:535–544
Shan C, Yang J, Dong Z et al (2020) Study on river health assessment weight calculation. Pol J Environ Stud 29(2):1839–1848
Fallahizadeh S, Kermani M, Esrafili A et al (2021) The effects of meteorological parameters on PM10: Health impacts assessment using AirQ+ model and prediction by an artificial neural network (ANN)—ScienceDirect. Urban Clim 38
Bansal S, Ganesan G (2019) Advanced evaluation methodology for water quality assessment using artificial neural network approach. Water Resour Manage 33(9):3127–3141
Anmala J, Venkateshwarlu T (2019) Statistical assessment and neural network modeling of stream water quality observations of Green River watershed, KY, USA. Water Supply 19(5–6):1831–1840
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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Pang, Z., Liu, Y., Liu, Z., Liu, C. (2023). River Health Assessment Based on an Artificial Neural Network. In: Park, J.S., Yang, L.T., Pan, Y., Park, J.H. (eds) Advances in Computer Science and Ubiquitous Computing. CUTECSA 2022. Lecture Notes in Electrical Engineering, vol 1028. Springer, Singapore. https://doi.org/10.1007/978-981-99-1252-0_59
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DOI: https://doi.org/10.1007/978-981-99-1252-0_59
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