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Gottumukkala SB, Thotakura VN, Gvr SR, Chinta DP, Park R. Balancing aquaculture and estuarine ecosystems: machine learning-based water quality indices for effective management. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024:10.1007/s11356-024-34134-8. [PMID: 38963626 DOI: 10.1007/s11356-024-34134-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Accepted: 06/22/2024] [Indexed: 07/05/2024]
Abstract
This study delves into the environmental impact of inland aquaculture on estuarine ecosystems by examining the water quality of four estuarine streams within the key inland aquaculture zone of South India. In this region, extensive and intensive aquaculture practices are common, posing potential challenges to estuarine health. The research explores the predictive capabilities of the Gaussian elimination method (GEM) and machine learning techniques, specifically multi-linear regression (MLR) and support vector regressor (SVR), in forecasting the water quality index of these streams. Through comprehensive evaluation using performance metrics such as coefficient of determination (R2) and average mean absolute percentage error (MAPE), MLR and SVR demonstrate higher prediction efficiency. Notably, employing key water parameters as inputs in machine learning models is also more effective. Biochemical oxygen demand (BOD) emerges as a critical water parameter, identified by both MLR and SVR, exhibiting high specificity in predicting water quality. This suggests that MLR and SVR, incorporating key water parameters, should be prioritized for future water quality monitoring in intensive aquaculture zones, facilitating timely warnings and interventions to safeguard water quality.
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Affiliation(s)
- Sri Bala Gottumukkala
- Department of Civil Engineering, S.R.K.R Engineering College, Bhimavaram, India
- Centre for Clean and Sustainable Environment (CCSE), S.R.K.R Engineering College, Bhimavaram, India
| | - Vamsi Nagaraju Thotakura
- Department of Civil Engineering, S.R.K.R Engineering College, Bhimavaram, India.
- Centre for Clean and Sustainable Environment (CCSE), S.R.K.R Engineering College, Bhimavaram, India.
| | - Srinivasa Rao Gvr
- Department of Civil Engineering, Andhra University, Visakhapatnam, India
| | - Durga Prasad Chinta
- Department of Electrical and Electronics Engineering, S.R.K.R Engineering College, Bhimavaram, India
| | - Raju Park
- Department of Civil Engineering, S.R.K.R Engineering College, Bhimavaram, India
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Chen Y, Li J, Zhang Z, Jiao J, Wang N, Bai L, Liang Y, Xu Q, Zhang S. Modeling soil loss under rainfall events using machine learning algorithms. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 352:120004. [PMID: 38218170 DOI: 10.1016/j.jenvman.2023.120004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Revised: 12/12/2023] [Accepted: 12/30/2023] [Indexed: 01/15/2024]
Abstract
Soil loss is an environmental concern of global importance. Accurate simulation of soil loss in small watersheds is crucial for protecting the environment and implementing soil and water conservation measures. However, predicting soil loss while meeting the criteria of high precision, efficiency, and generalizability remains a challenge. Therefore, this study first used three machine learning (ML) algorithms, namely, random forest (RF), support vector machine (SVM), and artificial neural network (ANN) to develop soil loss models and predict soil loss rates (SLRs). These soil loss models were constructed using field observation data with an average SLR of 1756.48 t/km2 from rainfall events and small watersheds in the hilly-gully region of the Loess Plateau, China. During training, testing and generalizability stages, the average coefficients of determination from the RF, SVM, and ANN models were 0.903, 0.860, and 0.836, respectively. Similarly, the average Nash-Sutcliffe coefficients of efficiency from the RF, SVM and ANN models were 0.893, 0.791 and 0.814, respectively. These results indicated that MLs have superior predictive performance and generalizability, and broad prospects for predicting SLRs. This study also demonstrated that the RF model outperformed better than the SVM and ANN models. Therefore, the RF model was used to simulate the SLR of each small watershed in the Chabagou watershed. Our results showed the four-year (2017-2020) average annual SLR of the small watersheds ranged from 0.73 to 1.63 × 104 t/(km2∙a) in the Chabagou watershed. Additionally, the results also indicated the SLR of small watersheds under the rainstorm event with a 100-year recurrence interval was 4.4-51.3 times that of other rainfall events.Furthermore, this study confirmed that bare land was the predominant source of soil loss in the Chabagou watershed, followed by cropland land and grassland. This study helps to provide the theoretical basis for deploying soil and water conservation measures to realize the sustainable utilization of soil resources in the future.
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Affiliation(s)
- Yulan Chen
- The Research Center of Soil and Water Conservation and Ecological Environment, Chinese Academy of Sciences and Ministry of Education, Yangling, Shaanxi, 712100, China; Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling, Shaanxi, 712100, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jianjun Li
- Institute of Soil and Water Conservation, Northwest A& F University, Yangling, Shaanxi, 712100, China
| | - Ziqi Zhang
- Institute of Soil and Water Conservation, Northwest A& F University, Yangling, Shaanxi, 712100, China
| | - Juying Jiao
- The Research Center of Soil and Water Conservation and Ecological Environment, Chinese Academy of Sciences and Ministry of Education, Yangling, Shaanxi, 712100, China; Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling, Shaanxi, 712100, China; University of Chinese Academy of Sciences, Beijing, 100049, China; Institute of Soil and Water Conservation, Northwest A& F University, Yangling, Shaanxi, 712100, China.
| | - Nan Wang
- The Research Center of Soil and Water Conservation and Ecological Environment, Chinese Academy of Sciences and Ministry of Education, Yangling, Shaanxi, 712100, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Leichao Bai
- Institute of Soil and Water Conservation, Northwest A& F University, Yangling, Shaanxi, 712100, China; School of Geographical Sciences, China West Normal University, Nanchong, 637009, China
| | - Yue Liang
- University of Chinese Academy of Sciences, Beijing, 100049, China; State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing, 100048, China
| | - Qian Xu
- Institute of Soil and Water Conservation, Northwest A& F University, Yangling, Shaanxi, 712100, China
| | - Shijie Zhang
- Institute of Soil and Water Conservation, Northwest A& F University, Yangling, Shaanxi, 712100, China; Anhui and Huaihe River Institute of Hydraulic Research, Hefei, 230088, China
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