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  • Prediction of industrial land use using linear regression and mola techniques: A Case Study of Siltara Industrial belt
    59-70
    Views:
    255

    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.

  • Connection between the potential wind energy and the windy days
    6-24
    Views:
    32

    Preliminary wind climate information are required for the selection of the sites of energetic wind
    measurements. Optimal locations of wind energy projects, where the amount of utilizable wind
    energy can be forecasted with a good approach, can be determined using GIS and statistical methods.
    Anyhow, it is necessary to elaborate methods what make posible to gain data for the wind potential of
    a given location on the base of measured data. Monthly number of windy days can be such predictor
    if its basic statistical parameters and its connection to the monthly mean wind power can be
    determined. This latter one can be substituted by the area under the curve of the function fitted to the
    hourly averages of the cubes of the wind speeds. A regression modell is fitted to the monthly number
    of windy days and areas under the curve, on the base of time series of 7 Hungarian weather stations
    and the error of the modell is determined. On this base, the modell is extrapolated to a 35 years long
    period. The area under the curve proportional to the monthly mean wind power calculated on the base
    of the monthly number of windy days show a significant decreasing trend in 4 Hungarian weather
    stations.