A comparative study for landslide susceptibility mapping using GIS-based multi-criteria decision analysis (MCDA), logistic regression (LR) and association rule mining (ARM)

2016 ◽  
Vol 203 ◽  
pp. 45-55 ◽  
Author(s):  
Arzu Erener ◽  
Alev Mutlu ◽  
H. Sebnem Düzgün
Keyword(s):  
Logistic Regression ◽  
Comparative Study ◽  
Decision Analysis ◽  
Association Rule ◽  
Landslide Susceptibility ◽  
Association Rule Mining ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Rule Mining ◽  
Multi Criteria Decision Analysis

An Improved Approach to Analyze Accidents and Promote Road Safety using Association Rule Mining and Multi-Criteria Decision Analysis Methods

2020 ◽  
Vol 13 (4) ◽  
pp. 731-746
Author(s):  
Farhat Zeinab ◽  
Karouni Ali ◽  
Daya Bassam ◽  
Chauvet Pierre
Keyword(s):  
Decision Making ◽  
Decision Analysis ◽  
Association Rule ◽  
Road Safety ◽  
Association Rule Mining ◽  
Decision Makers ◽  
Road Accidents ◽  
Rule Mining ◽  
Multi Criteria Decision Analysis ◽  
Electre Tri

Background: Road accidents have become a major social and health problem for being dramatically increasing day after day worldwide. Scientists are conducting their studies to define the main attributes that share the severity of road accidents. Finding a new approach to analyze road accidents is of great urgency. Data mining techniques are best fitting to discover useful information out of enormous data which are used to make proactive decisions. Methods: This paper tempts a rule-based machine learning method known as association rule mining, which can identify strong rules discovered in databases using interesting measures. Given a data- set from the Lebanese territory for the years 2016-2017, the application of association rule mining, the Apriori method takes its place. However, its implementation leads to a very large number of rules. The task that is the most difficult is extracting meaningful and non-redundant rules. In order to find out the most interesting and relevant rules out of fatal rules such, ELECTRE TRI and PROMETHEE methods, the most significant methods of decision making, Multi-Criteria Decision Analysis (MCDA) are integrated to resolve the outranking problem. The integration is presented by the use of the same set of weights and the same constant values of Indifference and Preference thresholds used in ELECTRE-TRI method to define the linear preference function needed by PROMETHEE method. Realizing the sensitivity of the final output of alternatives ranking to the changes of the input parameters of the decision-making tool, this proposed integration helps the decision makers to overcome their ambivalence between preference and indifference thresholds and to cope adequately with the issue of the uncertainty of MCDA procedures; it comes up with the complete ranking of rules. Results: The obtained ranked rules declare the most significant attributes or combinations of attributes that influence the severity of road accidents. Four main factors of fatal road accidents are pinpointed: over-speeding mainly leading up to rollover crashes, pedestrians encountering in the context, distracted driving leading to fatal road vehicle collisions with Pedestrian victims; and wet roads particularly in the case of single car accidents. Meanwhile, the importance of ELECRE-TRI and PROMETHEE and their integration in dealing with such complex phenomena and corresponding database with a large number of involved attributes have been validated. Conclusion: This paper studies the phenomenon of road accidents. Association rule mining has been applied to discover all possible relations between the various attributes. The integration of ELECTRE- TRI and PROMETHEE MCDA techniques aims at extracting meaningful information from the big dataset. The obtained results have shown how influencing the behavior of the driver is on the occurrence of fatal road accidents. These findings contribute to supporting decision makers to draw new design conceptions for road infrastructure and develop preventive measures that improve road safety in Lebanon.

scholarly journals The Application of the Hybrid GIS Spatial Multi-Criteria Decision Analysis Best–Worst Methodology for Landslide Susceptibility Mapping

2019 ◽  
Vol 8 (2) ◽  
pp. 79 ◽  
Author(s):  
Ljubomir Gigović ◽  
Siniša Drobnjak ◽  
Dragan Pamučar
Keyword(s):  
Decision Analysis ◽  
Landslide Susceptibility ◽  
Susceptibility Mapping ◽  
Geographical Information ◽  
Weighted Averaging ◽  
Landslide Susceptibility Mapping ◽  
Annual Rainfall ◽  
Slope Aspect ◽  
Multi Criteria Decision Analysis ◽  
Aggregation Methods

The main goal of this article is to produce a landslide susceptibility map by using the hybrid Geographical Information System (GIS) spatial multi-criteria decision analysis best–worst methodology (MCDA-BWM) in the western part of the Republic of Serbia. Initially, a landslide inventory map was prepared using the National Landslide Database, aerial photographs, and also by carrying out field surveys. A total of 1082 landslide locations were detected. This methodology considers the fifteen conditioning factors that are relevant to landslide susceptibility mapping: the elevation, slope, aspect, distance to the road network, distance to the river, distance to faults, lithology, the Normalized Difference Vegetation Index (NDVI), the Topographic Wetness Index (TWI), the Stream Power Index (SPI), the Sediment Transport Index (STI), annual rainfall, the distance to urban areas, and the land use/cover. The expert evaluation takes into account the nature and severity of the observed criteria, and it was tested by using two scenarios: the different aggregation methods of the BWM. The prediction performances of the generated maps were checked by the receiver operating characteristics (ROCs). The validation results confirmed that the areas under the ROC curve for the weighted linear combination (WLC) and the ordered weighted averaging (OWA) aggregation methods of the MCDA-BWM have a very high accuracy. The results of the landslide susceptibility assessment obtained by applying the proposed best–worst method were the first step in the development of landslide risk management and they are expected to be used by local governments for effective management planning purposes.

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