Journal Articles

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    Industrial estate planning for Mangalore Taluk in Karnataka, using remote sensing and GIS
    (2006) Navalgund, L.; Shreedhara, V.; Srinikethan, G.
    The present work presents a technique to prepare zoning atlas to classify the environment and risks involved in siting an industry. Based on risks involved in a classified zone, the best-suited industries are recommended. Mangalore city has been taken as the study area has for the present work. Sensitivity of study area has been checked in terms of air pollution, surface water pollution and groundwater pollution. The study relies upon the database procured for this purpose from Central Pollution Control Board (CPCB) and Karnataka State Remote Sensing Technology, Bang lore. The database mainly comprises of topographic maps, thematic maps and groundwater information. Buffering and over-laying of the thematic maps have been carried out as per the guidelines of CPCB. © Enviromedia Printed in India. All rights reserved.
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    Regression analysis and ANN models to predict rock properties from sound levels produced during drilling
    (Elsevier Ltd, 2013) Rajesh Kumar, B.; Vardhan, H.; Govindaraj, M.; Vijay, G.S.
    This study aims to predict rock properties using soft computing techniques such as multiple regression, artificial neural network (MLP and RBF) models, taking drill bit speed, penetration rate, drill bit diameter and equivalent sound level produced during drilling as the input parameters. A database of 448 cases were tested for determination of uniaxial compressive strength (UCS), Schmidt rebound number (SRN), dry density (?), P-wave velocity (Vp), tensile strength (TS), modulus of elasticity (E) and percentage porosity (n) and the prediction capabilities of the models were then analyzed. Results from the analysis demonstrate that neural network approach is efficient when compared to statistical analysis in predicting rock properties from the sound level produced during drilling. © 2012 Elsevier Ltd.