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 Mapping of groundwater availability in dry areas of rural and urban regions in India using IOT assisted deep learning classification model Subject The topic of the resource Classification; Deep learning; Groundwater availability; Internet of things; Water quality index Description An account of the resource Groundwater is a crucial resource for fulfilling the water requirements of India's rural and urban areas. The heterogeneous nature of geological, hydrological, and climatic factors results in substantial variability in the accessibility of groundwater across disparate regions. The present investigation centers on the cartography of groundwater accessibility in arid zones of rural and urban Indian areas using a Deep Learning Classification Model (DL-GWCM) supported by the Internet of Things (IoT). The introductory section underscores the importance of groundwater in India, where groundwater sources cater to around 80% of rural and 50% of urban water demands. The text highlights statistical data derived from surveys that indicate a notable decrease in groundwater levels. This underscores the pressing necessity for implementing effective monitoring and management strategies. The DL-GWCM is a proposed solution that aims to enhance the precision and effectiveness of groundwater availability mapping by incorporating IoT technology and Deep Learning Classification. The DL-GWCM comprises multiple constituent elements, such as Groundwater Prediction, Water Quality Index, and Conventional Neural Network- Bidirectional Long Short-Term Memory (CNNBi LSTM) classification. The process of Groundwater Prediction involves the utilization of past data and environmental factors to make precise forecasts of groundwater levels. The Water Quality Index evaluates the quality of subsurface water resources, guaranteeing their secure and enduring utilization. The Deep Learning Classification Model with IoT technology was implemented for groundwater accessibility mapping in Indian arid zones. It integrates Groundwater Prediction, Water Quality Index, and CNNBi LSTM classification. The model makes precise forecasts using past data and environmental factors, ensuring secure water quality. Using the CNNBi LSTM classification model improves the precision of groundwater availability mapping due to its resilient classification capabilities. These findings suggest that the DL-GWCM outperforms conventional approaches. The mean values of all five metrics for the proposed method are presented as follows: The performance metrics of the model are as follows: Root Mean Square Error (RMSE) of 0.77%, Mean Absolute Error (MAE) of 2.13%, Relative Absolute Error (RAE) of 8.72%, Root Relative Squared Error (RRSE) of 0.92%, and Correlation Coefficient (CC) of 0.92. The results of the proposed methodology facilitate the discernment of regions with abundant or scarce groundwater accessibility, thereby supporting the sustainable management and planning of groundwater resources. 2024 Elsevier B.V. Creator An entity primarily responsible for making the resource S S.; Basi Reddy A.; Alphy A.; Velusamy J.; J I.; Rajagopal M. Source A related resource from which the described resource is derived Groundwater for Sustainable Development, Vol-25 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.2024.101098" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.gsd.2024.101098</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184016737&amp;doi=10.1016%2Fj.gsd.2024.101098&amp;partnerID=40&amp;md5=5013a42789dcf53a158b7432bea6725b" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184016737&amp;doi=10.1016%2fj.gsd.2024.101098&amp;partnerID=40&amp;md5=5013a42789dcf53a158b7432bea6725b</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 S S., Department of Management Studies, Skyline University Nigeria, Nigeria; Basi Reddy A., Department of Computer Science and Engineering, School of Computing, Mohan Babu University, Andhra Pradesh, Tirupati, India; Alphy A., Department of Computer Science and Engineering, SRM IST Delhi NCR Campus, Ghaziabad, India; Velusamy J., Department of Electronics and Communication Engineering, Nehru Institute of Engineering and Technology, India; J I., Department of Electronics and Communication Engineering, KPR Institute of Engineering and Technology, Arasur, Coimbatore, 641 407, India; Rajagopal M., Lean Operations and System, School of Business and Management, CHRIST (Deemed to Be University), Bangalore, India