{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T22:45:02Z","timestamp":1776811502650,"version":"3.51.2"},"reference-count":26,"publisher":"European Society of Computational Methods in Sciences and Engineering","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"published-print":{"date-parts":[[2022,7,8]]},"abstract":"<jats:p>In order to accurately predict noctiluca density, a new prediction model PSO-GRNN was constructed according to the relationship between noctiluca density and environmental factors. Particle swarm optimization (PSO) algorithm was used to obtain the optimal smoothing factor of generalized regression neural network (GRNN) in this model, that could reduce human subjective influence and improve the generalization ability and performance of generalized regression neural network. The dissolved oxygen, water temperature, total nitrogen, salinity, phytoplankton density and soluble inorganic phosphorus were taken as the model input, and the noctiluca density was taken as the model output. Finally, the new model and other traditional models were both tested, and the prediction results of the improved model were compared with other traditional models. The experimental results showed that the improved new model had high accuracy in the prediction of noctiluca density, and could indirectly realize the early prediction of red tide.<\/jats:p>","DOI":"10.3233\/jcm-226006","type":"journal-article","created":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T14:06:00Z","timestamp":1648562760000},"page":"1131-1139","source":"Crossref","is-referenced-by-count":1,"title":["An improved GRNN model and prediction of noctiluca density"],"prefix":"10.66113","volume":"22","author":[{"given":"Yan","family":"Kang","sequence":"first","affiliation":[{"name":"School of Mathematics and Information Science and Technology, Hebei Normal University of Science and Technology, Qinhuangdao, Hebei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinling","family":"Song","sequence":"additional","affiliation":[{"name":"School of Mathematics and Information Science and Technology, Hebei Normal University of Science and Technology, Qinhuangdao, Hebei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongyan","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Mathematics and Information Science and Technology, Hebei Normal University of Science and Technology, Qinhuangdao, Hebei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruidong","family":"Li","sequence":"additional","affiliation":[{"name":"Yanching Institute of Technology, Langfang, Hebei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"55691","reference":[{"issue":"3","key":"10.3233\/JCM-226006_ref1","doi-asserted-by":"crossref","first-page":"1789","DOI":"10.1007\/s13762-018-2108-x","article-title":"Harmful algal blooms (red tide): A review of causes, impacts and approaches to monitoring and prediction","volume":"16","author":"Zohdi","year":"2019","journal-title":"International Journal of Environmental Science and Technology."},{"issue":"1","key":"10.3233\/JCM-226006_ref2","first-page":"471","article-title":"A study on red tide risk and basic understanding of fishermen and residents in Bandar Abbas, Hormozgan Province, Iran (Persian Gulf)","volume":"19","author":"Mirza Esmaeili","year":"2020","journal-title":"Iranian Journal of Fisheries Sciences."},{"key":"10.3233\/JCM-226006_ref3","doi-asserted-by":"crossref","unstructured":"He X, Chen C, Lu C, et al. 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