دانلود رایگان مقاله انگلیسی GIS یک رویکرد تصمیم گیری چند معیاری برای طبقه بندی مناطق ساحلی هند - الزویر 2018

ABSTRACT

Coastal area classification in India is a challenge for decision makers due to unclear directions in implementation of coastal regulations and lack of scientific rational about existing classification methods. To improve the objectivity of the coastal area classification is the aim of the present work. A Geographical Information System (GIS) coupled Multi-criteria Decision Making (MCDM) approach is developed in this work to provide scientific rational for classifying coastal areas. Utility functions are used to transform the physical coastal features into quantitative membership values. Different weighting schemes for coastal features are applied to derive Coastal Area Index (CAI) which classifies the coastal areas in distinct categories. Mumbai, the coastal megacity of India, is used as case study for demonstration of proposed approach. Results of application of GIS-MCDM approach showed the clear demarcation of coastal areas based on CAI is possible which provides a better decision support for developmental and planning authorities to classify coastal areas. Finally, uncertainty analysis using Monte Carlo approach to validate the sensitivity of CAI under different scenarios was carried out.

4. Conclusions Coastal area classification presents numerous challenges that have been elaborated in the current work. The lack of scientific rational in Indian coastal zone classification policy has been identified in this work. A novel GIS-MCDM based CAI approach to classify urban coastal areas has been proposed to serve as scientific rational for coastal area classification.

The developed approach is transparent where physical characteristics of coastal areas are transformed into quantitative measurements. Utility based membership functions are developed and applied to each of the coastal features. Weights are applied to each of the layers and results are aggregated into one index, called CAI. The applicability of the CAI is demonstrated using Mumbai city case study. The robustness of the results are validated using rigorous sensitivity analysis. The results of sensitivity analysis showed that the weights and membership functions used to derive CAI presented lower uncertainty than compared to the equal weights and random weight scenarios.

The methodology used is based on decision science where criteria relies on physical utility of coastal features rather than the subjective interest of stakeholders. This study provides the decision making framework for location specific coastal area classification. Classification of coastal areas based on proposed CAI will facilitate planning based on scientific principles. CAI generates the distinct categories of coastal areas where most effective urban coastal management options can be proposed. The CAI computation method is modular and scalable in nature and can be applied to different locations and datasets easily. Thus the CAI approach can further be used for developing integrated framework for sustainable coastal management.

The CAI results showed the usefulness to serve as decision support approach for urban planners and ULBs for classifying urban areas. We hope that CAI will be taken up by the researchers and the practitioners and will be widely used for classifying urban coastal areas by policy makers, coastal managers, coastal communities and other associated stakeholders.