A two-step fusion process for multi-criteria decision applied to natural hazards in mountains

Mountain river torrents and snow avalanches generate human and material damages with dramatic consequences. Knowledge about natural phenomenona is often lacking and expertise is required for decision and risk management purposes using multi-disciplinary quantitative or qualitative approaches. Expertise is considered as a decision process based on imperfect information coming from more or less reliable and conflicting sources. A methodology mixing the Analytic Hierarchy Process (AHP), a multi-criteria aid-decision method, and information fusion using Belief Function Theory is described. Fuzzy Sets and Possibilities theories allow to transform quantitative and qualitative criteria into a common frame of discernment for decision in Dempster-Shafer Theory (DST ) and Dezert-Smarandache Theory (DSmT) contexts. Main issues consist in basic belief assignments elicitation, conflict identification and management, fusion rule choices, results validation but also in specific needs to make a difference between importance and reliability and uncertainty in the fusion process

Applying new uncertainty related theories and multicriteria decision analysis methods to snow avalanche risk management

International audienceMaking the best decision in the event of a snow avalanche encounters problems in the assessment and management process because of the lack of information and knowledge on natural phenomena and the heterogeneity and reliability of the information sources available (historical data, field measurements, and expert assessments). One major goal today is therefore to aid decision making by improving the quality, quantity, and reliability of the available information. This article presents a new method called evidential reasoning and multicriteria decision analysis (ER-MCDA) to help decision making by considering information imperfections arising from several more or less reliable and possibly conflicting sources of information. First, the principles of the existing methods are reviewed. Classical methods of multicriteria decision making and existing theories attempting to represent and propagate information imperfections are described. In a second point, we describe the principle of the ER-MCDA method combining multicriteria decision analysis (MCDA) to model the decision-making process and fuzzy sets theory, possibility theory, and evidence theory to represent, fuse and propagate information imperfections. Experts, considered more or less reliable, provide imprecise and uncertain evaluations of quantitative and qualitative criteria that are combined through information fusion. The method is applied to a simplified version of an existing system aiming to evaluate the sensitivity of avalanche sites. This new method makes it possible to consider both the importance of the information available and reliability in the decision process. It also contributes to improving traceability. Other developments designed to handle other assessment problems such as avalanche triggering conditions or data quality are in progress

AHP and uncertainty theories for decision making using the ER-MCDA methodology

International audienceIn this paper, we present the ER-MCDA methodology for multi-criteria decision-making based on imperfect information coming from more or less reliable and conflicting sources. The Analytic Hierarchy Process (AHP), Fuzzy Sets, Possibility and Belief Functions theories are combined to take a decision based on imprecise and uncertain evaluations of quantitative, qualitative criteria. Classical aggregation of criteria is replaced by a two-step fusion process using advanced fusion rules based on the Dezert-Smarandache Theory (DSmT) that allows to make a difference between importance, reliability and uncertainty of information sources and contents

Seafloor characterization using airborne hyperspectral co-registration procedures independent from attitude and positioning sensors

The advance of remote-sensing technology and data-storage capabilities has progressed in the last decade to commercial multi-sensor data collection. There is a constant need to characterize, quantify and monitor the coastal areas for habitat research and coastal management. In this paper, we present work on seafloor characterization that uses hyperspectral imagery (HSI). The HSI data allows the operator to extend seafloor characterization from multibeam backscatter towards land and thus creates a seamless ocean-to-land characterization of the littoral zone

Evaluation of Efficiency of Torrential Protective Structures With New BF-TOPSIS Methods

Decision-Aid Methods (DAMs) such as the CostBenefit Analysis (CBA) and the Analytical Hierarchy Process (AHP) help decision-makers to rank alternatives or to choose the best one among several potential ones

A Hybrid Clustering-Fusion Methodology for Land Subsidence Estimation

A hybrid clustering-fusion methodology is developed in this study that employs Genetic Algorithm (GA) optimization method, k-means method, and several soft computing (SC) models to better estimate land subsidence. Estimation of land subsidence is important in planning and management of groundwater resources to prevent associated catastrophic damages. Methods such as the Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) can be used to estimate the subsidence rate, but PS-InSAR does not offer the required efficiency and accuracy in noisy pixels (obtained from remote sensing). Alternatively, a fusion-based methodology can be used to estimate subsidence rate, which offers a superior accuracy as opposed to the traditionally used methods. In the proposed methodology, five SC methods are employed with hydrogeological forcing of frequency and thickness of fine-grained sediments, groundwater depth, water level decline, transmissivity and storage coefficient, and output of land subsidence rate. Results of individual SC models are then fused to render more accurate land subsidence rate in noisy pixels, for which PS-InSAR cannot be effective. We first extract 14,392 different input-output patterns from PS-InSAR technique for our study area in Tehran province, Iran. Then, k-means method is used to divide the study area to homogenous zones with similar features. The five SC models include Adaptive Neuro Fuzzy Inference System (ANFIS), Support Vector Regression (SVR), Multi-Layer Perceptron (MLP) neural network and two optimized models, namely, Radial Basis Function (RBF) and Generalized Regression Neural Network (GRNN). To fuse individual SC models, three methods including Genetic Algorithm (GA), K-Nearest Neighbors (KNN) and Ordered Weighted Average (OWA) based on ORNESS method and ORLIKE method, are developed and evaluated. Results show that the fusion-based method is significantly superior to each of the employed individual methods in predicting land subsidence rate

Automatic landslide detection using Dempster–Shafer theory from LiDAR-derived data and orthophotos

© 2017 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. A good landslide inventory map is a prerequisite for landslide hazard and risk analysis. In tropical countries, such as Malaysia, preparation of the landslide inventory is a challenging task because of the rapid growth of vegetation. Thus, it is crucial to use rapid and accurate technique and effective parameters. For this purpose, Dempster Shafer theory (DST) was applied in fusing high resolution LiDAR derived data products and Greenness index derived from orthophoto imagery. Two sites were selected, for the implementation and evaluation of the DST model; site “A” for DST implementation and site “B” for the comparison. For model implementation, vegetation index, slope and height were used as effective parameters for identifying automatic landslide detection. Two type of DST based fusions were evaluated; (greenness and height) and (greenness and slope). Furthermore, validation techniques were used to validate the accuracy are confusion matrix and area under the curve. The overall accuracy of the first and second evaluated fusions were (73.4% and 84.33%), and area under the curve were (0.76 and 0.81) respectively. Additionally, the result was compared with Random Forest (RF) based detection approach. The results showed that DST does not require a priori knowledge

Reliability assessment of open-source multiscale landslide susceptibility maps and effects of their fusion

Several landslide susceptibility (LS) maps at various scales of analysis have been performed with specific zoning purposes and techniques. Supervised machine learning algorithms (ML) have become one of the most diffused techniques for landslide prediction, whose reliability is firmly based on the quality of input data. Site-specific landslide inventories are often more accurate and complete than national or worldwide databases. For these reasons, detailed landslide inventory and predisposing variables must be collected to derive reliable LS products. However, high-quality data are often rare, and risk managers must consider lower-resolution available products with no more than informative purposes. In this work, we compared different ML models to select the most accurate for large-scale LS assessment within the Municipality of Rome. The ExtraTreesClassifier outperformed the others reaching an average F1-score of 0.896. Thereafter, we addressed the reliability of open-source LS maps at different scales of analysis (global to regional) by means of statistical and spatial analysis. The obtained results shed light on the difference in hazard zoning depending on the scale and mapping unit. An approach for low-resolution LS data fusion was attempted, assessing the importance of the adopted criteria, which increased the ability to detect occurred landslides while maintaining precision