Establishment of the watershed health indicators and health check of reservoirs

Tsai, Yie-Wen; Lin, Jen‐Yang; Chen, Ying-Chu

doi:10.1016/j.ecolind.2021.107779

Cited by 11 publications

(2 citation statements)

References 30 publications

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“…Typical water quality indicators include water temperature (WT), pH, suspended solids (SS), dissolved oxygen (DO), and ammonia-nitrogen (NH 3 -N) (Fortuna et al, 2023). Both the river pollution index (RPI) and the water quality index use the concentrations of NH 3 -N and DO as the primary indicators of river water quality (Fortuna et al, 2023;Tsai et al, 2021). NH 3 -N pollution mainly comes from anthropogenic sources such as the use of agricultural fertilizers on land (Dutra et al, 2016;Waring et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Forecasting fish mortality from water and air quality data using deep learning models

Ting,

Chen

2024

J of Env Quality

Self Cite

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High rate of aquatic mortality incidents recorded in Taiwan and worldwide is creating an urgent demand for more accurate fish mortality prediction. Present study innovatively integrated air and water quality data to measure water quality degradation, and utilized deep learning methods to predict accidental fish mortality from the data. Keras library was used to build multilayer perceptron and long short‐term memory models for training purposes, and the models’ accuracies in fish mortality prediction were compared with that of the naïve Bayesian classifier. Environmental data from the 5 days before a fish mortality event proved to be the most important data for effective model training. Multilayer perceptron model reached an accuracy of 93.4%, with a loss function of 0.01, when meteorological and water quality data were jointly considered. It was found that meteorological conditions were not the sole contributors to fish mortality. Predicted fish mortality rate of 4.7% closely corresponded to the true number of fish mortality events during the study period, that is, four. A significant surge in fish mortality, from 20% to 50%, was noted when the river pollution index increased from 5.36 to 6.5. Moreover, the probability of fish mortality increased when the concentration of dissolved oxygen dropped below 2 mg/L. To mitigate fish mortality, ammonia nitrogen concentrations should be capped at 5 mg/L. Dissolved oxygen concentration was found to be the paramount factor influencing fish mortality, followed by the river pollution index and meteorological data. Results of the present study are expected to aid progress toward achieving the Sustainable Development Goals and to increase the profitability of water resources.

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Section: Introductionmentioning

confidence: 99%

Forecasting fish mortality from water and air quality data using deep learning models

Ting,

Chen

2024

J of Env Quality

Self Cite

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“…Such a change in the watershed environment and the inefficient utilization of water resources seriously distort the hydrologic health of watershed. Increased surface flow, decreased groundwater flow, increased flooding, soil erosion, water pollution, and urban desertification are all side effects of this crisis [8]. Accordingly, to maintain a sustainable and healthy water cycle, it is desirable to accurately identify the overall water cycle systems of each watershed and develop an analysis technology that enables the water availability to be sustained [9].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Land-Use Changes Impact on Watershed Health Using Probabilistic Approaches

Lee

So-Young

Lee

et al. 2021

Water

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This study evaluated watershed health (WH) change using reference values for environmental changes at various times. Land use in 1985 was defined as the reference value under the most natural conditions, and the WH for the years 1995 to 2019 was calculated in comparison to 1985. The proposed method was used to assess the WH of 78 standard subbasins in South Korea’s Geum River Basin (GRB), where complex land-use change has occurred since 1995. For evaluating hydrology and water quality (WQ) health index, Soil and Water Assessment Tool (SWAT) and four land-use maps (1985, 1995, 2008, and 2019) were used to simulate the hydrology and WQ. A multivariate normal distribution (MND) from poor (0) to good (1) was used to assess WH based on SWAT modeling results. Based on the reference value, the WQ health from 1995 to 2019 changed to within 0.1, while the range of changes in the hydrology index was analyzed over 0.18. As a result of WH changes from 1985 to 2019, hydrological health deteriorated in high-density urbanized subbasins, while WQ health deteriorated in upland-cultivation-increased subbasins. This study provides useful information for recognizing potential WH issues related to long-term environmental changes.

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Spatial difference in phoD-harboring bacterial landscape between soils and sediments along the Yangtze River

Yang

Gadd

et al. 2023

Ecological Indicators

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Establishment of the watershed health indicators and health check of reservoirs

Cited by 11 publications

References 30 publications

Forecasting fish mortality from water and air quality data using deep learning models

Forecasting fish mortality from water and air quality data using deep learning models

Evaluation of Land-Use Changes Impact on Watershed Health Using Probabilistic Approaches

Spatial difference in phoD-harboring bacterial landscape between soils and sediments along the Yangtze River

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