Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Ensemble Deep Learning Approach for Turbidity Prediction of Dooskal Lake Using Remote Sensing Data Subject The topic of the resource Deep learning models; Ensemble learning; Remote sensing data; Turbidity; Water resources Description An account of the resource The summer season in India is marked by a severe shortage of water, which poses significant challenges for daily usage and agricultural practices. With unpredictable weather patterns and irregular rainfall, it is crucial to monitor and maintain water bodies such as domestic ponds and lakes in urban areas to ensure they provide clean and safe water for regular use, free from industrial pollutants. In this research paper, we propose an innovative ensemble deep learning approach (e-DLA) that leverages deep learning models to predict the turbidity of Dooskal Lake, located in Telangana, India, using remote sensing data. The proposed approach utilizes various deep learning models, including bagging, boosting, and stacking, to analyze the complex relationships between remote sensing data and turbidity levels in the lake. The study aims to provide accurate and efficient predictions of turbidity levels, which can aid in the management and conservation of water resources in the region. Hyperparameter tuning is employed, and dynamic climatic features are extracted and integrated with the ensemble learning global protective intelligent algorithm to reveal the complex relationship between in situ and measured values of turbidity during the measuring timeline. The proposed approach provides accurate predictions of turbidity levels, enabling the implementation of effective control measures to maintain water quality standards. Experimental results demonstrate that the proposed approach significantly reduces prediction errors compared to existing deep learning models. Overall, this research highlights the potential of machine learning techniques in monitoring and maintaining water resources, particularly in urban areas, to support sustainable water management and usage, and addresses an urgent and pressing issue in India and around the world. 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG. Creator An entity primarily responsible for making the resource Ramesh J.V.N.; Patibandla P.R.; Shanbhog M.; Ambala S.; Ashraf M.; Kiran A. Source A related resource from which the described resource is derived Remote Sensing in Earth Systems Sciences, Vol-6, No. 45750, pp. 146-155. Publisher An entity responsible for making the resource available Springer Nature Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s41976-023-00098-5" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s41976-023-00098-5</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176768320&amp;doi=10.1007%2Fs41976-023-00098-5&amp;partnerID=40&amp;md5=44738c19356f40f3370dbb3b8a237c31" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176768320&amp;doi=10.1007%2fs41976-023-00098-5&amp;partnerID=40&amp;md5=44738c19356f40f3370dbb3b8a237c31</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 25208195 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Ramesh J.V.N., Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur Dist, Andhra Pradesh, Vaddeswaram, 522502, India; Patibandla P.R., Department of Electronics &amp; Communication Engineering, Dhanekula Institute of Engineering &amp; Technology, Ganguru, Vijayawada, 521139, India; Shanbhog M., School of Sciences, CHRIST (Deemed to be University), Ghaziabad, Delhi NCR, 201003, India; Ambala S., Computer Engineering Department, Pimpri Chinchwad College of Engineering, Maharastra, Pune, India; Ashraf M., Computer Science &amp; Engineering, School of Technology, Maulana Azad National Urdu University, TS, Hyderabad, India; Kiran A., Department of Computer Science and Engineering, MLR Institute of Technology, Dundigal, Telangana, Hyderabad, 500043, India