Identification of Landslide-prone Zones Using a GIS-based Multi-Criteria Decision Analysis and Region Growing Algorithm in Uncertain Conditions

Landslides are considered to be one of the most significant natural hazards. Detection of landslide-prone zones is an important phase in landslide hazard assessment and mitigation of landslide-related losses. AHP as one of the most effective methods for GIS-based multi-criteria decision analysis is increasingly being used in susceptibility mapping. However, its weights have some degree of uncertainty that interval comparison matrix (ICM) method can be used to deal with this problem. The importance of this study is to propose an interval number distance-based region growing (IDRG) method based on ICM for the identification of landslide-prone zones in the Urmia lake basin, Iran. To assess the capability of the proposed IDRG method, a landslide susceptibility map was produced using common AHP, too. To generate the maps, the weights of nine conditioning factors were determined using both traditional pairwise comparison matrices (PCM) of the AHP method and ICM. The accuracy of the produced maps was assessed through ROC (receiver operating curve) and using a dataset of known landslide occurrences. The results indicate an improvement in accuracy of about 11% by identifying the landslide-prone zones using the IDRG method. This improvement was achieved by minimizing the uncertainty associated with criteria ranking/weighting in a traditional AHP and identifying the prone zones as areas instead of pixels.

A NFT verification method for distributed AI system

This paper proposes a method invention, namely an efficient NFT data inspection method with minimum granularity and probability comparison. The invention establishes a fast comparison method of AI model and data, that is, the direct comparison of small files priority and the maximum-minimum interval comparison. The invention takes the substantial identity inside the NFT data and the processing method of NFT data coincidence into account, so that the data content outside the token of the NFT publicly shared by the AI distributed system can also be unique on the Internet. Therefore, it can avoid the problem of incremental packaging and repeated packaging, and can successfully balance the efficiency and security of the comparison process. portions given in this document

Interval Information Content of Fuzzy Relation and the Application in the Fuzzy Implication Operators

In rule optimization, some rule characteristics were extracted to describe the uncertainty correlations of fuzzy relations, but the concrete numbers cannot express correlations with uncertainty, such as “at least 0.1 and up to 0.5.” To solve this problem, a novel definition concerning interval information content of fuzzy relation has been proposed in this manuscript to realize the fuzziness measurement of the fuzzy relation. Also, its definition and expressions have also been constructed. Meanwhile based on the interval information content, the issues of fuzzy implication ranking and clustering were analyzed. Finally, utilizing the combination of possibility’s interval comparison equations and interval value’s similarity measure, the classifications of implication operators were proved to be realizable. The achievements in the presented work will provide a reasonable index to measure the fuzzy implication operators and lay a solid foundation for further research.

PRIORITIZATION OF ALTERNATIVES BASED ON ANALYTIC HIERARCHY PROCESS USING INTERVAL TYPE-2 FUZZY SETS AND PROBABILITY-THEORETICAL INTERVAL COMPARISON

The Analytic Hierarchy Process (AHP) enables decision-makers to prioritize alternatives. However, when an expert expresses judgments using natural language statements (e.g. words or phrases) inherent vagueness of language constructs can cause the interpretation to be imprecise. The fuzzy Analytic Hierarchy Process (FAHP) can be viewed in the context of the classical AHP expansion. While performing pairwise comparisons domain experts are accustomed to operating with verbal terms in their judgments. Most existing FAHP approaches do not consider a human’s confidence in the estimates provided. This paper presents a model that gives weight to the constraints on domains of expert assessments as they are almost always supplied with certain degrees of confidence. Interval type-2 membership functions (IT2MF) along with the probability-theoretical procedure for comparison of intervals can be applied here as suitable modeling options. Empirical comparison of FAHP that makes use of triangular fuzzy numbers and IT2MF-based FAHP is also presented.

Two Approximation Models of Fuzzy Weight Vector from a Comparison Matrix

In this study, our uncertain judgment on multiple items is denoted as a fuzzy weight vector. Its membership function is estimated from more than one interval weight vector. The interval weight vector is obtained from a crisp/interval comparison matrix by Interval Analytic Hierarchy Process (AHP). We redefine it as a closure of the crisp weight vectors which approximate the comparison matrix. The intuitively given comparison matrix is often imperfect so that there could be various approaches to approximate it. We propose two of them: upper and lower approximation models. The former is based on weight possibility and the weight vector with it includes the comparison matrix. The latter is based on comparison possibility and the comparison matrix with it includes the weight vector.