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Showing 5 results for Fuzzy Logic

S. M. Fatemiaghda, V. Bagheri, M Mahdavifar,
Volume 7, Issue 1 (8-2013)
Abstract

In this research, one of the new methods for seismic landslides hazard zonation (CAMEL) to predict the behavior of these types of landslides have been discussed.  It is also tried to eveluate this method with the proposed Mahdavifar method.  For achieving this result, the influence of  Sarein earthquake (1997), have been selected as a case study. In order to apply seismic hazard zonation, the methodology of Computing with Words (CW), an approach using fuzzy logic systems in which words are used in place of numbers for computing and reasoning is employed. First, the required information which includes disturbance distance, ground strength class, moisture content, shake intensity, slope angle, slope height, soil depth, terrain roughness, and vegetation have been collected using air photos, Landsat Satellite images, geological and topographic maps, and site investigation of the studied region. The data is digitized and weighted using Geological Information System (GIS). At the next step, the hazard rate and areal concentrations with respect to landslide types are calculated using CAMEL program and then, landslides hazard map produced by the above mentioned method is compared with landslides occurred as a result of Sarein earthquake. Finally, for evaluating on prediction of the earthquake-induced landslides, empirical comparison have been done between CAMEL and Mahdavifar methods.
Sm Fatemiaghda, V Bagheri, Mr Mahdavi,
Volume 8, Issue 3 (12-2014)
Abstract

In the present study, landslides occurred during 1997 Sarein, Iran earthquake are discussed and evaluated. In order to meet the objectives, the Computing with Words (CW), an approach using fuzzy logic systems in which words are used in place of numbers for computing and reasoning is applied. Firstly, the necessary information which include disturbance distance, ground class, moisture, shaking intensity, slope angle, slope height, soil depth, terrain roughness, and land-use have been collected using air photos, LANDSAT satellite images, geological and topographic maps, and site investigation of the studied region. The data is digitized and weighted using ARCGIS software. At the next step, the hazard rate and predicted areal concentrations of landslides with respect to their types are calculated using CAMEL software (Miles & Keefer, 2007). CAMEL provides an integrated framework for modeling all types of earthquake-induced landslides using geographical information system(GIS). Finally, landslides hazard map is compared to landslides triggered by Sarein earthquake.
Salman Soori, Siamak Baharvand,
Volume 9, Issue 4 (3-2016)
Abstract

Landslide is one of the mass movement processes that occur in Iran and parts of the world every year. It causes huge human loss and economical damages. In order to check the stability of slopes in Kakasheraf basin, in the first step sliding areas were identified using the aerial photography and field surveys and then distribution map of landslide is provided. The impact of each of these factors which included dip, aspect, altitude, lithology, landuse and distance from the road and drainage are assessed through Arc GIS software merging the effective factors on landslide with the landslide distribution map. Then these factors were prioritized using AHP model. In this study, the fuzzy logic and density area method has been used in the Kakasheraf basin in order to identify landslide hazard zonation. The empirical probability index (EPI) has been used to assess and classify the models outputs in the landslide risk estimation.Results show that the fuzzy logic is more applicable method than density area model for mapping the landslide risk in Kakasheraf basin
Bakhtiar Fezizadeh, Meysam Soltani ,
Volume 14, Issue 2 (8-2020)
Abstract

Introduction
Landslide is known as one of major natural hazards. Landslide susceptibility mapping is known as efficient approach to mitigate the future hazard and reduce the impact of landslide hazards. The main objective of this research is to apply GIS spatial decision making systems for landslide hazard mapping in the 5th segment of Ardebil-Mianeh railroad. Evaluation of the landslide criteria mapping and their relevancy for landslide hazard can be also considered. To achieve the research objectives, an integrated approach of Fuzzy-Analytic Hierarchy Process (AHP), Fooler Hierarchical Triangle and Fuzzy logic methods were employed in GIS Environment.
Material and methods
Within this research, we also aimed to apply GIS spatial decision making systems and in particular GIS multi criteria decision analysis which are available in Arc GIS and Idrisi softwares. We have identified 8 casual factors (including: density of vegetation, land use, faults desistance, distance from rivers, distance from roads, slope, aspect, geology) based on literature review. Accordingly, these layers were prepared in GIS dataset by means of applying all GIS ready, editing and topology steps. The criterion weighting was established based F-AHP approach. The criteria weights was derived and rank of each criterion was obtained. Accordingly, the landslide susceptible zones were identified using GIS-MCDA approaches.
Results and discussion
Finally the functionality of each method was validated against known landslide locations. This step was applied to identify most efficient method for landslide mapping. According to the results and based on the values derived from Qs, P, and AUC, the accuracy of fuzzy method was accordingly about 0.33, 0.74 and 0.76, respectively. In context of Fuzz-AHP the accuracy of 1.08, 0.88 and 0.94 were obtained. While, the accuracy of Fooler Hierarchical Triangle were obtained 0.78, 0.84 and 0.91, accordingly.
Conclusion
As results indicated integration of Fuzzy-AHP represented more accurate results. Results of this research are great of important for future research in context of methodological issues for GIScience by means of identifying most efficient methods and techniques for variety of applications such landslide mapping, suitability assessment, site selection and in all for any GIS-MCDA application.

Tahereh Azari, Sakineh Dadashi, Fatemeh Kardel,
Volume 17, Issue 2 (9-2023)
Abstract

Qualitative assessment of coastal waters affected by seawater salinity can be done using the parameter of chloride in groundwater. This research proposes a supervised artificial intelligence committee machine (SAICM) method for accurate prediction of chloride concentration in groundwater of Sari plain. SAICM predicts chloride concentration as the output of the model by non-linear combination of artificial intelligence models. In this research, Principal Component Analysis (PCA) method was used to identify effective hydrochemical parameters related to chloride concentration as input components to artificial intelligence models. Based on the results of PCA, parameters (Na, K, EC, TDS, SAR) were selected as input components of artificial intelligence models. Firstly, four artificial intelligence models, Sogno fuzzy logic, Mamdani fuzzy logic, Larsen fuzzy logic and artificial neural network were designed to predict chloride concentration. Based on the modelling results, all the models showed a good fit with the chloride data in Sari Plain. Then, the combined SAICM model was built, which combines the prediction results of 4 separate AI models using the nonlinear ANN combiner and determines the chloride concentration more accurately. The results show that the proposed SAICM can estimate chloride concentration with much higher accuracy than individual methods.


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