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Investigation of Finite Groundwater Potential Zones through Analytical Hierarchy Process and ArcGIS for the Region of Visakhapatnam District, Andhra Pradesh

Kalyani Kunchala, Reddyy Mutukuru Gangireddy Muni

Ecological Engineering & Environmental Technology

2023

Vol. 24, iss. 3

161--175

Due to the indiscriminate use of limited water sources, the requirement for groundwater evaluation in India expanded substantially. Population growth and unequal distribution, poor irrigation systems, rapid urbanization/industrialization, large-scale deforestation, droughts, and inefficient land use practises contribute to groundwater depletion.As a result, the need for water for agriculture, domestic, and industry soars. The study identifies viable zones in Visakhapatnam’s emerging metropolitan metropolis by utilising the Analytical Hierarchy Process (AHP) approach with remote sensing data in ArcGIS software. Thematic layers were created by taking remote sensing data into consideration (drainage density, soil, lineament density, land use land cover, geomorphology, rainfall, slope, and geology). The method is employed to determine the weights of distinct thematic layers by obtaining the normalised weight from a pairwise matrix.To emphasize the groundwater potential zones and create a map with different zones specified, the weights and ranks extrapolated from the AHP approach have been made available in the weighted index overlay analysis tool in ArcGIS.Groundwater availability and recharge are significantly high in the good zone of the present study’s four classifications of good, moderate, low, and very low. The groundwater status, potential locations for water extraction, and best practises for groundwater recharging may all be determined with the use of the acquired information from the indication map.

Classification of rocks radionuclide data using machine learning techniques

Khan A. R., Mir A. A., Saeed S., Rafique M., Asim K. M., Iqbal T., Jabbar A., Rahman S. U.

Acta Geophysica

2018

Vol. 66, no. 5

1073--1079

The aim of this study is to assess the performance of linear discriminate analysis, support vector machines (SVMs) with linear and radial basis, classification and regression trees and random forest (RF) in the classification of radionuclide data obtained from three different types of rocks. Radionuclide data were obtained for metamorphic, sedimentary and igneous rocks using gamma spectroscopic method. A P-type high-purity germanium detector was used for the radiometric study. For analysis purpose, we have determined activity concentrations of 232Th, 226Ra and 40K radionuclides, published elsewhere (Rafique et al. in Russ Geol Geophys 55:1073–1082, 2014), in different rock samples and built the classification model after pre-processing the data using three times tenfold cross-validation. Using this model, we have classified the new samples into known categories of sedimentary, igneous and metamorphic. The statistics depicts that RF and SVM with radial kernel outperform as compared to other classification methods in terms of error rate, area under the curve and with respect to other performance measures.