Every drop counts: unleashing the prospective locations for water harvesting using geospatial analytics

Description

Water 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.

Publication Date

1-1-2017

DOI

10.1145/3109761.3158394

ISBN

978-1450352437

Publisher

Association for Computing Machinery

Keywords

Ahp, Analytical Hierarchy Process, Digital Elevation Model, Flood Fill Model, Geospatial Analytics, Gis, Image Processing, Landsat-8, Rain Water Harvesting, Rwh Optimum Location Selection, Sliding Window Algorithm, Smart Water, Water Tanks

Conference

IML 2017: International Conference on Internet of Things and Machine Learning: 17-18 October , 2017, Liverpool, United Kingdom

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