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 Predicting nitrous oxide contaminants in Cauvery basin using region-based convolutional neural network Subject The topic of the resource Cauvery river beds; Deep learning; Environmental management; Nitrous oxide; Region-based convolutional neural network (RCNN) Description An account of the resource Nitrous oxide (N2O) in riverbeds affects hydrological processes by contributing to the greenhouse effect, indicating poor water quality, disrupting biogeochemical cycling, and linking to eutrophication. Elevated N2O levels signal environmental issues, impacting aquatic life and necessitating precise forecasting for effective environmental management and reduced greenhouse gas emissions. Precisely forecasting nitrous oxide (N2O) emissions from riverbeds is paramount for effective environmental management, given its significant potency as a greenhouse gas. This study focuses on the difficulties related to spatial feature extraction and modeling accuracy in predicting N2O in riverbeds in Tamil Nadu. To address the obstacles, the research suggests utilizing the Deep Learning Based Prediction of Nitrous Oxide Contaminants (DL-PNOC), which studies the N2O contaminants in water using Region-based Convolutional Neural Network (RCNN) for spatial feature extraction, to predict nitrous oxide contaminants. The study is centered on the Cauvery River Basin located in Tamil Nadu, where the emission of N2O is a matter of environment. The outcomes encompass the specialized N2O contaminant model for riverbeds and the implementation of RCNN achieves precise N2O forecasting. The DL-PNOC approach combines a contaminant model with RCNN deep learning techniques to capture spatial characteristics and predict N2O pollutants accurately. Furthermore, using the River Bed Dynamics Simulator reinforces the dependability of the findings. The DL-PNOC approach has exhibited encouraging results, as evidenced by the following metrics: a high IoU of 88.66%, precision of 88.96%, recall of 90.03%, F1 score of 89.22%, and low RMSE and MAE values of 9.14% and 7.59%, respectively. The findings highlight the efficacy of the DL-PNOC approach in precisely forecasting N2O pollutants in river sediments. 2024 Elsevier B.V. Creator An entity primarily responsible for making the resource Poluru R.K.; Sundararajan S.; S V.; Balakrishnan S.; V S.; Rajagopal M. Source A related resource from which the described resource is derived Groundwater for Sustainable Development, Vol-26 Publisher An entity responsible for making the resource available Elsevier B.V. Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.gsd.2024.101194" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.gsd.2024.101194</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192501593&amp;doi=10.1016%2Fj.gsd.2024.101194&amp;partnerID=40&amp;md5=c44a3d358a2e75beaa4c0c8b3cd5eb50" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192501593&amp;doi=10.1016%2fj.gsd.2024.101194&amp;partnerID=40&amp;md5=c44a3d358a2e75beaa4c0c8b3cd5eb50</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 2352801X 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 Poluru R.K., Department of Information Technology, Institute of Aeronautical Engineering, Hyderabad, India; Sundararajan S., Business Management, Skyline University Nigeria, Kano State, Kano City, Nigeria; S V., Department of CSE, Rajalakshmi Engineering College, Chennai, India; Balakrishnan S., Department of Computer Science and Engineering, Aarupadai Veedu Institute of Technology, Vinayaka Mission's Research Foundation (Deemed to be University), Paiyanoor, Tamilnadu, Chennai, India; V S., Department of Computer Science, Periyar University, Salem, 11, India; Rajagopal M., Lean Operations and Systems, School of Business and Management, CHRIST (Deemed to be University), Bangalore, India