Prediction of industrial land use using linear regression and mola techniques: A Case Study of Siltara Industrial belt

Abstract

The Siltara Industrial belt is an important industrial pocket of Chattisgarh state located in the northern part of the Raipur city, which is rapidly growing. In this process spatial, cultural, political and administrative factors are controlling its rate, direction and pattern. The Simple Linear Regression (SLR) and Multi-Objective Land Allocation (MOLA) techniques, which are embedded in SPSS and Idrisi Kilimanjaro software respectively, and have been used for the estimation of future scenario of the industrial growth. In this model, a suitable platform has been prepared in which future industrialization has been estimated by integrating physical, social, cultural factors and land acquisition policy. In this article, results have revealed that industrialization has occurred very fast during last one decade. The industrial land was 6.15 km2 in 2001 and 18.725 km2 in 2011 and estimated as 31.30 km2 in 2021 and 43.87 km2 in 2031 using SLR. The rapid industrial growth is very critical issues for agrarian society and fresh environment. This model very accurately estimating (overall accuracy=95.39%, Kno=97.24%, agreement=98.63 %) the future growth of industrial land. This work will be useful to the planners and policy makers of private and government sectors to regulate the sustainable planning practices and smart decision-making.

Estimation of Surface Runoff from Semi-arid Ungauged Agricultural Watershed Using SCS-CN Method and Earth Observation Data Sets

“…Many studies have proved the use of EO data namely groundwater [27,46,48,51,52], river water quality [55], coastal water [22], lake and wetlands [5,47,53,59,60], land use/land cover mapping [45,46,48,50,51], land use change trajectories [56], land use/land cover modeling [28,49], urban land use dynamics [4], hydrological modeling [31], forest mapping [44], cyclone tracking [16], soil characterization [37], climate change [54], slope estimation [57], landscape ecology [47,53], ocean studies [35,36] and watershed management [67], watershed prioritization [68]. GIS processing has become a critical step in hydrologic modeling since it contributes to generate model parameters in a spatially distributed manner.…”

Case study for estimation of the amount of contaminants stored in soil in an industrial area

“…Contaminants accumulating in the soil due to industrial activities is a serious environmental issue which needs to be managed by society (MacCormack, 2005;Wycisk et al, 2009;Marshall et al, 2020). Uncontrolled emissions of contaminants lasting for decades leads to severe environmental pollution which can only be managed by expensive remediation measures (Ahmed 2009;Mustak et al, 2015;Fischer, 2008, Szabó et al, 2016. The amount of contaminants occurring under the surface in various states of matter can nowadays be determined with several geoinformatics software programmes, which can be used to identify the sources and the spread of contaminants in order to facilitate the remediation process (Yang et al 2004;Kátai 2008;Hürkamp et al 2009).…”

Spatial Distribution of Heavy Metals around the Gold Mine Ore Tailings of Hatti, Karnataka State, India

“…In developing countries like India, a substantial quantity of mining resulted waste were dropped publicly open space with none contraceptives Harish and David, 2015) that results in overburden of the ore tailings (Rao and Reddy 2005), discharge of tailing leachate into in and around the mining sites, additionally to those government owing activities like mineral effluents implicate as fertilizers (Kumar, 2013) are the foremost supply of environmental pollution. Moreover, pollutants carried out by mining activities in developing countries remains controversial, since it may impact hugely on the cultural, physical and socio-economic status of the local people (Verma et al 2012;Harish and David 2015;Mustak et al 2015).…”