{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T16:41:19Z","timestamp":1781973679697,"version":"3.54.5"},"reference-count":41,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2019,3,22]],"date-time":"2019-03-22T00:00:00Z","timestamp":1553212800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key R &amp; D Project of Hainan Province, China","award":["ZDYF2018015"],"award-info":[{"award-number":["ZDYF2018015"]}]},{"name":"Hainan Province Natural Science Foundation of China","award":["617033"],"award-info":[{"award-number":["617033"]}]},{"name":"Oriented Project of State Key Laboratory of Marine Resource Utilization in South China Sea","award":["DX2017012"],"award-info":[{"award-number":["DX2017012"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>An accurate prediction of cage-cultured water quality is a hot topic in smart mariculture. Since the mariculturing environment is always open to its surroundings, the changes in water quality parameters are normally nonlinear, dynamic, changeable, and complex. However, traditional forecasting methods have lots of problems, such as low accuracy, poor generalization, and high time complexity. In order to solve these shortcomings, a novel water quality prediction method based on the deep LSTM (long short-term memory) learning network is proposed to predict pH and water temperature. Firstly, linear interpolation, smoothing, and moving average filtering techniques are used to repair, correct, and de-noise water quality data, respectively. Secondly, Pearson\u2019s correlation coefficient is used to obtain the correlation priors between pH, water temperature, and other water quality parameters. Finally, a water quality prediction model based on LSTM is constructed using the preprocessed data and its correlation information. Experimental results show that, in the short-term prediction, the prediction accuracy of pH and water temperature can reach 98.56% and 98.97%, and the time cost of the predictions is 0.273 s and 0.257 s, respectively. In the long-term prediction, the prediction accuracy of pH and water temperature can reach 95.76% and 96.88%, respectively.<\/jats:p>","DOI":"10.3390\/s19061420","type":"journal-article","created":{"date-parts":[[2019,3,25]],"date-time":"2019-03-25T06:56:52Z","timestamp":1553497012000},"page":"1420","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":182,"title":["A Water Quality Prediction Method Based on the Deep LSTM Network Considering Correlation in Smart Mariculture"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6837-9024","authenticated-orcid":false,"given":"Zhuhua","family":"Hu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Marine Resource Utilization in South China Sea, College of Information Science &amp; Technology, Hainan University, No.58, Renmin Avenue, Haikou 570228, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiran","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Software &amp; Microelectronics, Peking University, No.24, Jinyuan Road, Daxing District, Beijing 102600, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaochi","family":"Zhao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Marine Resource Utilization in South China Sea, College of Information Science &amp; Technology, Hainan University, No.58, Renmin Avenue, Haikou 570228, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingshan","family":"Xie","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Marine Resource Utilization in South China Sea, College of Information Science &amp; Technology, Hainan University, No.58, Renmin Avenue, Haikou 570228, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiezhuo","family":"Zhong","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Marine Resource Utilization in South China Sea, College of Information Science &amp; Technology, Hainan University, No.58, Renmin Avenue, Haikou 570228, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhigang","family":"Tu","sequence":"additional","affiliation":[{"name":"Hainan Academy of Ocean and Fisheries Sciences, No.12, Baiju Avenue, Haikou 571126, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juntao","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Marine Resource Utilization in South China Sea, College of Information Science &amp; Technology, Hainan University, No.58, Renmin Avenue, Haikou 570228, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,3,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Liao, X., Huang, H., Dai, M., and Lin, Q. 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