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      On the Diversity-Based Weighting Method for Risk Assessment and Decision-Making about Natural Hazards

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          Abstract

          The entropy-weighting method (EWM) and variation coefficient method (VCM) are two typical diversity-based weighting methods, which are widely used in risk assessment and decision-making for natural hazards. However, for the attributes with a specific range of values (RV), the weights calculated by EWM and VCM (abbreviated as W E and W V) may be irrational. To solve this problem, a new indicator representing the dipartite degree is proposed, which is called the coefficient of dipartite degree (CDD), and the corresponding weighting method is called the dipartite coefficient method (DCM). Firstly, based on a large amount of statistical data, a comparison between the EWM and VCM is carried out. It is found that there is a strong correlation between the weights calculated by the EWM and VCM (abbreviated as W E and W V); however, in some cases the difference between W E and W V is big. Especially when the diversity of attributes is high, W E may be much larger than W V. Then, a comparison of the DCM, EWM and VCM is carried out based on two case studies. The results indicate that DCM is preferred for determining the weights of the attributes with a specific RV, and if the values of attributes are large enough, the EWM and VCM are both available. The EWM is more suitable for distinguishing the alternatives, but prudence is required when the diversity of an attribute is high. Finally, the applications of the diversity-based weighting method in natural hazards are discussed.

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          Most cited references36

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          Applying population-based evolutionary algorithms and a neuro-fuzzy system for modeling landslide susceptibility

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            Novel hybrid artificial intelligence approach of bivariate statistical-methods-based kernel logistic regression classifier for landslide susceptibility modeling

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              Landslide Susceptibility Modeling Based on GIS and Novel Bagging-Based Kernel Logistic Regression

              Landslides cause a considerable amount of damage around the world every year. Landslide susceptibility assessments are useful for the mitigation of the associated potential risks to local economic development, land use planning, and decision makers. The main aim of this study was to present a novel hybrid approach of bagging (B)-based kernel logistic regression (KLR), named the BKLR model, for spatial prediction of landslides in the Shangnan County, China. We first selected 15 conditioning factors for landslide susceptibility modeling. Then, the prediction capability of all conditioning factors was evaluated using the least square support vector machine method. Model validation and comparison were performed based on the area under the receiver operating characteristic curve and several statistical-based indexes, including positive predictive rate, negative predictive rate, sensitivity, specificity, kappa index, and root mean square error. Results indicated that the BKLR ensemble model outperformed and outclassed the KLR and the benchmark support vector machine model. Our findings overall confirmed that a combination of the meta model with a decision tree classifier based on a functional algorithm can decrease the over-fitting and variance problems of data, which could enhance the prediction power of the landslide model. The resultant susceptibility maps could be useful for hazard mitigation in the study area and other similar landslide-prone areas.
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                Author and article information

                Journal
                Entropy (Basel)
                Entropy (Basel)
                entropy
                Entropy
                MDPI
                1099-4300
                11 March 2019
                March 2019
                : 21
                : 3
                : 269
                Affiliations
                School of Geography & Resource Science, Neijiang Normal University, Neijiang 641100, China; chenpengyu@ 123456njtc.edu.cn ; Tel.: +86-0832-2340771
                Article
                entropy-21-00269
                10.3390/e21030269
                7514749
                33266984
                8853743d-bce4-4f76-ab44-ea2de3d7fab6
                © 2019 by the author.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 29 January 2019
                : 08 March 2019
                Categories
                Article

                diversity-based weighting method,entropy-weighting method,variation coefficient method,risk assessment,decision-making,natural hazards

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