{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:06:34Z","timestamp":1777705594809,"version":"3.51.4"},"reference-count":44,"publisher":"SAGE Publications","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2022,2,2]]},"abstract":"<jats:p>In recent years, lakes pollution has become increasingly serious, so water quality monitoring is becoming increasingly important. The concentration of total organic carbon (TOC) in lakes is an important indicator for monitoring the emission of organic pollutants. Therefore, it is of great significance to determine the TOC concentration in lakes. In this paper, the water quality dataset of the middle and lower reaches of the Yangtze River is obtained, and then the temperature, transparency, pH value, dissolved oxygen, conductivity, chlorophyll and ammonia nitrogen content are taken as the impact factors, and the stacking of different epochs\u2019 deep neural networks (SDE-DNN) model is constructed to predict the TOC concentration in water. Five deep neural networks and linear regression are integrated into a strong prediction model by the stacking ensemble method. The experimental results show the prediction performance, the Nash-Sutcliffe efficiency coefficient (NSE) is 0.5312, the mean absolute error (MAE) is 0.2108\u200amg\/L, the symmetric mean absolute percentage error (SMAPE) is 43.92%, and the root mean squared error (RMSE) is 0.3064\u200amg\/L. The model has good prediction performance for the TOC concentration in water. Compared with the common machine learning models, traditional ensemble learning models and existing TOC prediction methods, the prediction error of this model is lower, and it is more suitable for predicting the TOC concentration. The model can use a wireless sensor network to obtain water quality data, thus predicting the TOC concentration of lakes in real time, reducing the cost of manual testing, and improving the detection efficiency.<\/jats:p>","DOI":"10.3233\/jifs-210708","type":"journal-article","created":{"date-parts":[[2021,12,7]],"date-time":"2021-12-07T14:09:44Z","timestamp":1638886184000},"page":"1455-1482","source":"Crossref","is-referenced-by-count":3,"title":["An ensemble deep neural network approach for predicting TOC concentration in lakes along the middle-lower reaches of Yangtze River"],"prefix":"10.1177","volume":"42","author":[{"given":"Hai","family":"Guo","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Dalian Minzu University, Dalian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yifan","family":"Song","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Dalian Minzu University, Dalian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haoran","family":"Tang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Dalian Minzu University, Dalian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingying","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Dalian Minzu University, Dalian, China"},{"name":"Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"4","key":"10.3233\/JIFS-210708_ref1","doi-asserted-by":"crossref","first-page":"5319","DOI":"10.3233\/JIFS-189017","article-title":"Water remote sensing eutrophication inversion algorithm based on multilayer convolutional neural network","volume":"39","author":"Lei","year":"2020","journal-title":"Journal of Intelligent & Fuzzy Systems"},{"issue":"1","key":"10.3233\/JIFS-210708_ref2","doi-asserted-by":"crossref","first-page":"381","DOI":"10.3233\/JIFS-191828","article-title":"Emergency water supply decision-making of transboundary river basin considering government\u2013public perceived satisfaction","volume":"40","author":"Wang","year":"2021","journal-title":"Journal of Intelligent & Fuzzy Systems"},{"key":"10.3233\/JIFS-210708_ref3","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1016\/j.scitotenv.2019.03.138","article-title":"Detection, provenance and associated environmental risks of water quality pollutants during anomaly events in River Atoyac, Central Mexico: A real-time monitoring approach","volume":"669","author":"Hernandez-Ramirez","year":"2019","journal-title":"Science of the Total Environment"},{"key":"10.3233\/JIFS-210708_ref4","doi-asserted-by":"crossref","first-page":"134825","DOI":"10.1016\/j.scitotenv.2019.134825","article-title":"Where does land use matter most? 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