Assessment of an air pollution monitoring network to generate urban air pollution maps using Shannon information index, fuzzy overlay, and Dempster-Shafer theory, A case study: Tehran, Iran

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

Bigdeli

2019

Abstract. In this study, a GIS based approach has been proposed for the flood risk zonation based on a multi-criteria spatial group fuzzy AHP decision making analysis and its integration with fuzzy overlay analysis. For this purpose, 10 layers affecting flood occurrence have been used including: the Digital Elevation Model (DEM), Slope, NDVI, Flow Accumulation (Flow Ac.), HOFD, VOFD, Topographic Position Index (TPI), Topographic Wetness Index (TWI), Curve Number (CN), Modified Fournier Index. Each layer was classified into 5 sub-classes and their preference at its layer was weighted by a group of experts using fuzzy analytical hierarchy processes (GFAHP) method. Finally, the risk map of the studied area with the weight of experts and fuzzy overlay method was product and divided into five categories.

Section: Introductionmentioning

confidence: 99%

Flood Risk Zonation Using a Multi-Criteria Spatial Group Fuzzy-Ahp Decision Making and Fuzzy Overlay Analysis

Hasanloo

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

Bigdeli

2019

“…Intelligent transportation systems are an effective approach for better managing traffic and related issues in smart cities (Mohan, Padmanabhan et al 2008;Pahlavani et al, 2019;Moghadam et al, 2017). Moreover, geospatial information systems provides a powerful tools for importing, analyzing, and displaying urban data from a variety of sensors and sources (Delavar, 2004;Pahlavani et al, 2006;Pahlavani et al, 2017;Bahari et al, 2014). Automakers are developing vehicle sensors and their own applications in various fields.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Energy Consumption of Vehicle Sensor Networks Based on the K-Means Clustering Method and Ant Colony Algorithm

Mashari

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

Ebrahimi

2019

With the world's growing population, the number of vehicles has increased, but the capacity of roads and transportation systems has not. The increasing traffic and the pollution that it causes has therefore become a problem all over the world. Wireless Sensor Networks that detect traffic and prevent its pernicious effects have attracted the attention of many. Fast information transfers, easy installation, less repair and maintenance, compression and lower costs make WSNs more common than other network solutions (Nellore and Hancke 2016). Since more traffic congestion wastes time and energy, it is crucial to develop and present approaches to more accurately detect traffic patterns. Clustering is one of the best data analysis methods used for detecting traffic patterns. The approach proposed by this study assumes that all vehicles are equipped with GPS, and that sensors establish connections with each other through radio communication equipment. In this case, clustering is used to gather important traffic information and create intelligent urban transportation systems (ITS) to reduce sensor energy consumption in vehicular sensor networks. The K-means method and the ANT Colony optimization algorithm were used to cluster sensors and investigate their impact on reducing sensor energy consumption. The results show that the K-means algorithm and the ANT Colony algorithm reduce vehicular sensor energy consumption by 41.7 and 76.8 percent, respectively. The investigations showed that the ANT Colony algorithm outperformed the Kmeans algorithm by 84.2%.

“…The procedure of preparing the zonation map is based on the recognition of natural features and quantitative modeling based on the data of the studied area. Geospatial information system and geostatistical analyses have been widely applied in analyzing different field of georelated topics (Delavar, 2004;Pahlavani et al, 2006;Pahlavani et al, 2017;Bahari et al, 2014). In this regard, applying statistical approaches to forecast landslide hazard has generally been seen since the 1990s.…”

Section: Introductionmentioning

confidence: 99%

Landslide Hazard Mapping Using a Radial Basis Function Neural Network Model: A Case Study in Semirom, Isfahan, Iran

Yavari

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

Bigdeli

2019

Abstract. In this paper, Radial Basis Function (RBF) Neural Network and Logistic Regression (LR) models were proposed for hazard prediction of landslides in a part of the Semirom area (Iran) to compare their accuracy and performance. For this purpose, a spatial database of the study area was prepared that consists of 68 landslide locations and 11 influencing information layers including slope, aspect, profile curvature, plan curvature, distance from faults, distance from roads, distance from residential regions, distance from rivers, land use, lithology and rainfall. Landslide hazard maps were prepared for the study area by applying the proposed algorithms. Performance of the models was assessed using the Receiver Operating Characteristic (ROC) curve and area under the ROC curve (AUC). The coefficient of determination (R2), the root mean square error (RMSE), and the Normal Root Mean Square Error (NRMSE) were calculated for proposed methods. The outcomes showed that the RBF Neural Network has the highest R2 (0.8224), in comparison to that of the LR model (0.5365). Also, the ROC plots, RMSEs and NRMSEs showed that the proposed RBF Neural Network is much better than the LR model. Consequently, it can be concluded that the RBF Neural Network is the best regression model in this study and it can be considered as a capable method for landslide hazard mapping in landslide-susceptible areas.