Please use this identifier to cite or link to this item: https://repository.iimb.ac.in/handle/2074/11246
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dc.contributor.authorGupta, Aparana
dc.contributor.authorGarg, Anshul
dc.contributor.authorRawat, Namrata
dc.contributor.authorChigurupati, Sandeep
dc.contributor.authorDinesh Kumar, U
dc.date.accessioned2020-04-01T13:36:54Z-
dc.date.available2020-04-01T13:36:54Z-
dc.date.issued2017
dc.identifier.isbn9781450352437
dc.identifier.urihttps://repository.iimb.ac.in/handle/2074/11246-
dc.description.abstractWater is at the heart of 'Sustainable Development Goals (SDGs)' set by United Nations - with an objective to balance the three dimensions of sustainable development: Environment, Social and Economic - and is indirectly associated with the success of all the other Goals. But, with changing climatic patterns, untimely rains, prolonged dry spells, depleting ground water and drought making every drop of water extremely precious, the need of the hour is to gauge and work towards the major aspects of water harvesting - 'Catchment'. Water Harvesting must be a key element of any strategy to bring an end to India's perennial swings between drought and flood and to meet the following SDGs for sustained development. This study presents a structured and meticulous approach, wielding 'Geospatial Analytics' to identify the prospective locations for Water Harvesting in arid and semi-arid parts of the country for sustainable development. This paper is structured as follows. Section 1 describes the background and motivation for this idea. Section 2 details out the objective. In section 3 we present the 'Literature Survey' on the work that has already been carried out in this field. While section 4 discerns our area of study, Section 5 provides process flow starting from Data gathering, Data extraction, Data pre-processing, Model selection and Multi Criteria Decision Making (Model Application). In Section 6, we present and validate our experimental results achieved using the proposed methodology. Section 7 concludes our study followed by Section 8 on Recommendations for future enhancements and next steps.
dc.publisherAssociation for Computing Machinery
dc.subjectAhp
dc.subjectAnalytical Hierarchy Process
dc.subjectDigital Elevation Model
dc.subjectFlood Fill Model
dc.subjectGeospatial Analytics
dc.subjectGis
dc.subjectImage Processing
dc.subjectLandsat-8
dc.subjectRain Water Harvesting
dc.subjectRwh Optimum Location Selection
dc.subjectSliding Window Algorithm
dc.subjectSmart Water
dc.subjectWater Tanks
dc.titleEvery drop counts: unleashing the prospective locations for water harvesting using geospatial analytics
dc.typePresentation
dc.relation.conferenceIML 2017: International Conference on Internet of Things and Machine Learning: 17-18 October , 2017, Liverpool, United Kingdom
dc.relation.publicationACM International Conference Proceeding Series-
dc.identifier.doi10.1145/3109761.3158394
dc.pages13p.
Appears in Collections:2010-2019 P
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