{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T05:51:09Z","timestamp":1771048269108,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,4,24]],"date-time":"2024-04-24T00:00:00Z","timestamp":1713916800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["U1906217"],"award-info":[{"award-number":["U1906217"]}]},{"name":"National Natural Science Foundation of China","award":["62071491"],"award-info":[{"award-number":["62071491"]}]},{"name":"National Natural Science Foundation of China","award":["22CX01004A-5"],"award-info":[{"award-number":["22CX01004A-5"]}]},{"name":"National Natural Science Foundation of China","award":["22CX01004A-4"],"award-info":[{"award-number":["22CX01004A-4"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["U1906217"],"award-info":[{"award-number":["U1906217"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["62071491"],"award-info":[{"award-number":["62071491"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["22CX01004A-5"],"award-info":[{"award-number":["22CX01004A-5"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["22CX01004A-4"],"award-info":[{"award-number":["22CX01004A-4"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Chlorophyll-a (Chl-a) concentration monitoring is very important for managing water resources and ensuring the stability of marine ecosystems. Due to their high operating efficiency and high prediction accuracy, backpropagation (BP) neural networks are widely used in Chl-a concentration inversion. However, BP neural networks tend to become stuck in local optima, and their prediction accuracy fluctuates significantly, thus posing restrictions to their accuracy and stability in the inversion process. Studies have found that metaheuristic optimization algorithms can significantly improve these shortcomings by optimizing the initial parameters (weights and biases) of BP neural networks. In this paper, the adaptive nonlinear weight coefficient, the path search strategy \u201cLevy flight\u201d and the dynamic crossover mechanism are introduced to optimize the three main steps of the Artificial Ecosystem Optimization (AEO) algorithm to overcome the algorithm\u2019s limitation in solving complex problems, improve its global search capability, and thereby improve its performance in optimizing BP neural networks. Relying on Google Earth Engine and Google Colaboratory (Colab), a model for the inversion of Chl-a concentration in the coastal waters of Hong Kong was built to verify the performance of the improved AEO algorithm in optimizing BP neural networks, and the improved AEO algorithm proposed herein was compared with 17 different metaheuristic optimization algorithms. The results show that the Chl-a concentration inversion model based on a BP neural network optimized using the improved AEO algorithm is significantly superior to other models in terms of prediction accuracy and stability, and the results obtained via the model through inversion with respect to Chl-a concentration in the coastal waters of Hong Kong during heavy precipitation events and red tides are highly consistent with the measured values of Chl-a concentration in both time and space domains. These conclusions can provide a new method for Chl-a concentration monitoring and water quality management for coastal waters.<\/jats:p>","DOI":"10.3390\/rs16091503","type":"journal-article","created":{"date-parts":[[2024,4,24]],"date-time":"2024-04-24T10:18:36Z","timestamp":1713953916000},"page":"1503","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A Chlorophyll-a Concentration Inversion Model Based on Backpropagation Neural Network Optimized by an Improved Metaheuristic Algorithm"],"prefix":"10.3390","volume":"16","author":[{"given":"Xichen","family":"Wang","sequence":"first","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"}]},{"given":"Jianyong","family":"Cui","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"}]},{"given":"Mingming","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gholizadeh, M.H., Melesse, A.M., and Reddi, L. 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