A Technical Note Landslide Susceptibility Mapping
The 'Digital journal of geotechnical anatomist' has published a higher impact research in neuro-scientific landslide susceptibility mapping. This informative article has a great clinical value with significant contribution to the data. The purpose of current article, as a complex note, is to examine some right elements of the stated landslide susceptibility mapping research with additional talk on the used technique. This informative article tries to suggest alternative methodologies to support/validate the found 'landslide susceptibility index' formula from the mentioned published research. Writer of this paper increases the overview of the mentioned article from some other angle, alternative computation and formulation methods.
Yanli Wu and Wenping Li (2015) in their article, A GIS Centered Landslide Susceptibility Mapping Using Multi-Criteria Decision Examination Model at a Regional Level, define landslide susceptibility mapping as a "qualitative methods or quantitative methods, and immediate risk mapping techniques or indirect mapping techniques." In addition they declare that "the consistency of landslide susceptibility maps mainly is determined by the total amount and quality of available data, the working level, and selecting strategy of modeling." Within the books review by Yanli and Wenping (2015) they found many publicized studies on GIS established landslide susceptibility analysis, and lots of used probabilistic models released studies in the mentioned area. They named a few of statistical models; including the bivariate models and logistic regressions. Additionally, various other methods such as analytical hierarchy process, certainty factor, statistical index, index of entropy and weights of information, are popularly used for landslide susceptibility mapping studies also. Various other new methods such as data mining tools specifically fuzzy logic, neuro-fuzzy , artificial neural network and support vector machine are also practiced for landslide susceptibility mappings (Yanli and Wenping,2015). Yanli and Wenping (2015) suggested analytical hierarchy process (AHP) way for Landslide Susceptibility mapping of the Qianyang Region area, Shaanxi Province, China, which sits within 106 longitude?56?E to 107?23?Latitude and e 34?33? to 35?57?N. Yanli Wu and Wenping (2015) used AHP because this method has never or rarely been employed by other scholars in the mentioned area. They have got considered fourteen landslide conditioning factors because of their landslide susceptibility mapping. The primary reason for their article was to suggest a landslide susceptibility map of the analyzed area in China.
To be able to create a landslide susceptibility map, fourteen landslide fitness factors of slope aspect (SAs), altitude(A), slope angle(SAn), plan curvature(PlC), geomorphology(G), account curvature(PrC), rainfall (R), topographic wetness index (TWI), sediment transfer index (STI), stream electric power index (SPI), distance to waterways (DRi), distance to faults (DF), Normalized Difference Vegetation Index (NDVI) and distance to highways (DRo) have been considered, and to be able to formulate the landslide susceptibility map, the LSI (landslide susceptibility index) was developed partly of the results of the analysis using AHP method by Yanli Wu and Wenping Li (2015). The next solution (1) is the designed LSI:
Within the technique portion of this post later, AHP will be reviewed critically. The main reason for the existing article is to consider alternative options for the practiced landslide susceptibility mapping; also to go over potential further studies to validate the talked about published study.
DISCUSSION ON METHODOLOGY
As you of categories under multi-criteria decision making, multi-alternative decision making methods use alternatives and decision multi-criteria (Sorooshian and Dodangeh, 2013). The Analytic Hierarchy Process (AHP) method is one of the very most popular multi-alternative decision making methods (Richartd et al, 2015).
AHP is a numerical established method with matrix-based computations (Anvari et al, 2014). "This strategy was made to optimize procedures through prioritization of parameters in making sophisticated decisions in particular when one is met with a variety of quantitative, qualitative and occasionally problems involving differing factors. Quite simply, the method targets prioritizing selection criteria, and distinguishing the greater important conditions from the less important ones" (Richard et al, 2015). Some studies use traditional AHP, plus some other studies use Fuzzy based mostly AHP, where additional fuzzy considers the likelihood distributions (Sorooshian and Azizi, 2013). Old classic AHP or Fuzzy AHP needs advanced knowledge of mathematics (Sorooshian, 2015), which is the negative point for AHP non-mathematic founded users.
Multi-alternative decision making isn't only AHP, but also various other choice methods including:
- Found (Simple Additive Weighting)
- DEMATEl (decision Trial and analysis laboratory)
- TOPSIS (Way of Order Inclination by Similarity to the perfect Solution)
- VIKOR method
- AIRM (Aggregated Indices Randomization Method)
- ANP (Analytic network process)
- Value evaluation and Value engineering
- Factor ranking and WPM (Weighted product model) and etc.
A few of above unveiled multi-alternative decision making methods in strategy section, can be viewed as with its different functions, like mapping the parameters interrelationships (Sorooshian and Suziyana (2013); Falatoonitoosi et al (2014)) but almost all of above method are rank based; merely to ranking options for decision creators. AHP method is a ranking method. Alternatively, in the same band of multi-criterial diction making there are many methods with potential to develop power functions, such as SEM (structural formula modeling) or Regression.
The "A GIS Established Landslide Susceptibility Mapping Using Multi-Criteria Decision Evaluation Model at a Regional Size" research by Yanli Wu and Wenping Li (2015) is a higher impact study, ideal for geotechnical executive and sciences. Usage of AHP within the methodology of the study has a higher contribution to decision making science, and boosts use of multi-attribute decision making methods in sciences and anatomist. Since in resulted equation (1) from mentioned study the coefficient of every variables (landslide conditioning factors) were predicated on AHP calculations, suggestion of the article, predicated on above methodological discussion, is perfect for future researchers to keep Yanli and Wenping's study (2015) to turn out with yet another prove of validity to aid the LSI formula.
Authors wish to acknowledge the put in time by editors and reviewers of the electronic digital journal of geotechnical anatomist for critiquing and editing this information before publication. We also give thanks to researcher Yanli Wu for his put in time for researching this short article before mailing for the journal.
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