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GIS, remote sensing and MCE approach for identifying groundwater prospective zones in mountainous region of PNG

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Abstract

Water is the most precious resource available for mankind. Due to the rapid increase in population, urbanisation and agricultural expansion place additional demand for water. Groundwater is an alternative source of fresh water available to support a human. However, Papua New Guinea’s (PNG) difficult geography and excessive hard rock formation make it challenging for authorities to carry out groundwater development programs. To combat this complexity, an integrated approach based on remote sensing (RS) and geographic information system (GIS) application through multi-criteria evaluation (MCE) technique was adopted for exploration and delineation of groundwater potential zones. A range of influential factors including geomorphology, lithology, soil, rainfall, drainage, slope, elevation, land use/land cover and inundation were analysed and integrated in ArcGIS10 through weighted overlay index techniques. A detail data evaluation criteria were developed with suitability rankings and weightings applied to each parameter. Weightage were assigned based on probability influence, i.e., geomorphology—16%, soil—16%, lithology—15%, drainage—15%, rainfall—10%, topographic slope—9%, elevation—9%, land use/land cover—5% and inundation—5%. After weighted overlay operations, a groundwater prospective zonal map layer was generated and classified in terms of very poor, poor, moderate, good and very good. The area occupied are poor—2.38%, very poor—11.21%, moderate—60.75%, good—16.47% and very good—9.19% respectively.

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Authors and Affiliations

  1. Department of Surveying and Land Studies, The PNG University of Technology, Lae, Morobe, Papua New Guinea

    Nebare Poi

  2. GIS Section, Department of Surveying and Land Studies, The PNG University of Technology, Lae, Morobe, Papua New Guinea

    Sailesh Samanta

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  1. Nebare Poi
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  2. Sailesh Samanta
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Correspondence to Sailesh Samanta.

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Poi, N., Samanta, S. GIS, remote sensing and MCE approach for identifying groundwater prospective zones in mountainous region of PNG. Appl Geomat 11, 317–330 (2019). https://doi.org/10.1007/s12518-019-00259-6

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  • DOI: https://doi.org/10.1007/s12518-019-00259-6

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