Modeling stream baseflow nitrate concentration in an agricultural watershed using neural network and bootstrap method
The evaluation of water resource vulnerability to nonpoint source pollution in the presence of uncertainty remains a crucial concern. To enhance the accuracy of the assessment and pinpoint the key factors that affect watershed water quality, the integration of an artificial neural network (ANN) into...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Ecological Indicators |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X23012396 |
| _version_ | 1797649693675618304 |
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| author | Ping Lan Li Guo Hailong Sun Yaling Zhang Yanjia Jiang |
| author_facet | Ping Lan Li Guo Hailong Sun Yaling Zhang Yanjia Jiang |
| author_sort | Ping Lan |
| collection | DOAJ |
| description | The evaluation of water resource vulnerability to nonpoint source pollution in the presence of uncertainty remains a crucial concern. To enhance the accuracy of the assessment and pinpoint the key factors that affect watershed water quality, the integration of an artificial neural network (ANN) into the evaluation process is imperative. The research involved the collection of streams baseflow samples from thirty-eight sites between 1994 and 1999 in the Tomorrow-Waupaca River Watershed in central Wisconsin, USA, with a focus on examining the relationship between nitrate concentrations and a range of environmental and land use variables extracted from the watershed GIS database. This study utilized ANN methodology, combined with a bootstrap technique that employed a random resampling approach from a single input dataset, to simulate monthly stream baseflow nitrate concentrations. The effectiveness and predictive ability of the ANN model were assessed by comparing it to conventional multivariate regression methods. Through the use of ANN, more precise outcomes can be achieved while taking into account the uncertainty associated with the analysis. The findings demonstrated that the ANN outperformed both the multivariate linear regression and nonlinear quadratic response surface models in explaining the variance of stream nitrate and in external prediction consistency. This study also highlighted several key variables, such as the areal percentage of agricultural land and grassland, stream order, and the average slope of the groundwater flow path, that significantly impacted the stream baseflow nitrate concentrations in this watershed that was dominated by dairy farming and groundwater. Of these variables, the percentage of agricultural land emerged as the most significant factor. |
| first_indexed | 2024-03-11T15:49:41Z |
| format | Article |
| id | doaj.art-2025d0e6b3694026ae309b305987ea15 |
| institution | Directory Open Access Journal |
| issn | 1470-160X |
| language | English |
| last_indexed | 2024-03-11T15:49:41Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj.art-2025d0e6b3694026ae309b305987ea152023-10-26T04:17:29ZengElsevierEcological Indicators1470-160X2023-12-01156111097Modeling stream baseflow nitrate concentration in an agricultural watershed using neural network and bootstrap methodPing Lan0Li Guo1Hailong Sun2Yaling Zhang3Yanjia Jiang4State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, ChinaState Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, ChinaCorresponding author.; State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, ChinaState Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, ChinaState Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, ChinaThe evaluation of water resource vulnerability to nonpoint source pollution in the presence of uncertainty remains a crucial concern. To enhance the accuracy of the assessment and pinpoint the key factors that affect watershed water quality, the integration of an artificial neural network (ANN) into the evaluation process is imperative. The research involved the collection of streams baseflow samples from thirty-eight sites between 1994 and 1999 in the Tomorrow-Waupaca River Watershed in central Wisconsin, USA, with a focus on examining the relationship between nitrate concentrations and a range of environmental and land use variables extracted from the watershed GIS database. This study utilized ANN methodology, combined with a bootstrap technique that employed a random resampling approach from a single input dataset, to simulate monthly stream baseflow nitrate concentrations. The effectiveness and predictive ability of the ANN model were assessed by comparing it to conventional multivariate regression methods. Through the use of ANN, more precise outcomes can be achieved while taking into account the uncertainty associated with the analysis. The findings demonstrated that the ANN outperformed both the multivariate linear regression and nonlinear quadratic response surface models in explaining the variance of stream nitrate and in external prediction consistency. This study also highlighted several key variables, such as the areal percentage of agricultural land and grassland, stream order, and the average slope of the groundwater flow path, that significantly impacted the stream baseflow nitrate concentrations in this watershed that was dominated by dairy farming and groundwater. Of these variables, the percentage of agricultural land emerged as the most significant factor.http://www.sciencedirect.com/science/article/pii/S1470160X23012396Agricultural land useBootstrap methodNeural networkNitrateNonpoint source pollutionWater quality |
| spellingShingle | Ping Lan Li Guo Hailong Sun Yaling Zhang Yanjia Jiang Modeling stream baseflow nitrate concentration in an agricultural watershed using neural network and bootstrap method Ecological Indicators Agricultural land use Bootstrap method Neural network Nitrate Nonpoint source pollution Water quality |
| title | Modeling stream baseflow nitrate concentration in an agricultural watershed using neural network and bootstrap method |
| title_full | Modeling stream baseflow nitrate concentration in an agricultural watershed using neural network and bootstrap method |
| title_fullStr | Modeling stream baseflow nitrate concentration in an agricultural watershed using neural network and bootstrap method |
| title_full_unstemmed | Modeling stream baseflow nitrate concentration in an agricultural watershed using neural network and bootstrap method |
| title_short | Modeling stream baseflow nitrate concentration in an agricultural watershed using neural network and bootstrap method |
| title_sort | modeling stream baseflow nitrate concentration in an agricultural watershed using neural network and bootstrap method |
| topic | Agricultural land use Bootstrap method Neural network Nitrate Nonpoint source pollution Water quality |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X23012396 |
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