{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T21:10:54Z","timestamp":1766178654588,"version":"build-2065373602"},"reference-count":22,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,6,27]],"date-time":"2023-06-27T00:00:00Z","timestamp":1687824000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The present study links monitoring and simulation models to predict water quality distribution in lakes using an optimized neural network and remote sensing data processing. Two data driven models were developed. First, a monitoring model was established that is able to convert spectral images to TDS distribution. Moreover, a simulation model was developed to generate a TDS distribution map for unseen scenarios for which no spectral images are available. Outputs of the monitoring model were applied as the observations for training the simulation model. The Nash\u2013Sutcliffe model efficiency coefficient (NSE) was utilized in the system performance measurement of the models. Based on the results in the case study, the monitoring model was sufficiently robust to convert the operational land imager spectral bands of Landsat 8 to the TDS distribution map. The NSE was more than 0.6 for the monitoring model, which confirms the predictive skills of the model. Furthermore, the simulation model was highly reliable in generating the TDS distribution map of the lakes. Three tests were carried out to demonstrate the reliability of the model. When comparing the results of the monitoring model and simulation model, an NSE of more than 0.6 was found for all the tests. It is recommendable to apply the proposed method instead of conventional hydrodynamic models that might be highly time consuming for simulating water quality parameters distribution in lakes. Low computational complexity is the main advantage of the proposed method.<\/jats:p>","DOI":"10.3390\/rs15133302","type":"journal-article","created":{"date-parts":[[2023,6,28]],"date-time":"2023-06-28T00:45:11Z","timestamp":1687913111000},"page":"3302","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Predicting Water Quality Distribution of Lakes through Linking Remote Sensing\u2013Based Monitoring and Machine Learning Simulation"],"prefix":"10.3390","volume":"15","author":[{"given":"Mahdi","family":"Sedighkia","sequence":"first","affiliation":[{"name":"Civil and Environmental Engineering Department, College of Science and Engineering, James Cook University, Townsville, QLD 4811, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2535-8685","authenticated-orcid":false,"given":"Bithin","family":"Datta","sequence":"additional","affiliation":[{"name":"Civil and Environmental Engineering Department, College of Science and Engineering, James Cook University, Townsville, QLD 4811, Australia"}]},{"given":"Parisa","family":"Saeedipour","sequence":"additional","affiliation":[{"name":"Computer Engineering Department, Engineering Faculty, Shahid Chamran University, Ahvaz 61357, Iran"}]},{"given":"Asghar","family":"Abdoli","sequence":"additional","affiliation":[{"name":"Environmental Science Research Institute, Tehran 19839, Iran"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,27]]},"reference":[{"key":"ref_1","unstructured":"Ritchie, H., Roser, M., and Environmental Impacts of Food Production (2023, May 25). 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