GIS-based MCDA for Gully Vulnerability Mapping Using AHP Techniques

2020 ◽  
Vol 4 (2) ◽  
pp. 45-63
Author(s):  
Ishaku Bashir ◽  
Rachel Sallau ◽  
Abubakar Sheikh ◽  
Zuni Aminu ◽  
Shu’aib Hassan
Keyword(s):  
Vulnerability Index ◽  
Drainage Density ◽  
Disaster Mitigation ◽  
Vulnerability Mapping ◽  
Slope Aspect ◽  
Land Area ◽  
Stream Power ◽  
Topographic Wetness Index ◽  
Vulnerability Class ◽  
Stream Power Index

This paper explores the potentiality of GIS-based Multi-Criteria Decision Analysis (MCDA) and Analytical Hierarchy Process (AHP) for gully vulnerability mapping. Multilayer information of basin characteristics, such as drainage density, Topographic Wetness Index (TWI), Stream Power Index (SPI), slope aspect and land use land cover (LULC), were used in this study to develop a Gully Vulnerability Index (GVI). A weighted approach was implemented on each criterion relative to their inferred influence on gully vulnerability and validated by determining the Consistency Ratio (CR). Findings show a varying magnitude of gully vulnerability across the study area. The low to medium gully vulnerability class was dominant covering a land area of 6557ha (21.25%), and mostly confined to developed areas. Still, it is noteworthy to observe that the severe gully vulnerability class covers a substantial land area of 5825ha (18.88%), which presents a great risk to infrastructural development and human settlements in the study area. The study has a model predictive capability with accuracy rate of 84.62%. The integration of the MCDA and AHP into GIS workflow is an effective approach critical to minimize the limitations associated with gully occurrence analysis, using a singular basin characteristic. The results obtained in the study will equally be important in determining gully risk zones, circumspect urban development, tracking and proper infrastructure construction plans for long-term gully disaster mitigation.

scholarly journals Harris Hawks Optimization: A Novel Swarm Intelligence Technique for Spatial Assessment of Landslide Susceptibility

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3590 ◽  
Author(s):  
Bui ◽  
Moayedi ◽  
Kalantar ◽  
Osouli ◽  
Gör ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Characteristic Curve ◽  
Absolute Error ◽  
Slope Aspect ◽  
Stream Power ◽  
Topographic Wetness Index ◽  
Conditioning Factors ◽  
Performance Error ◽  
Stream Power Index ◽  
Artificial Neural Network Ann

In this research, the novel metaheuristic algorithm Harris hawks optimization (HHO) is applied to landslide susceptibility analysis in Western Iran. To this end, the HHO is synthesized with an artificial neural network (ANN) to optimize its performance. A spatial database comprising 208 historical landslides, as well as 14 landslide conditioning factors—elevation, slope aspect, plan curvature, profile curvature, soil type, lithology, distance to the river, distance to the road, distance to the fault, land cover, slope degree, stream power index (SPI), topographic wetness index (TWI), and rainfall—is prepared to develop the ANN and HHO–ANN predictive tools. Mean square error and mean absolute error criteria are defined to measure the performance error of the models, and area under the receiving operating characteristic curve (AUROC) is used to evaluate the accuracy of the generated susceptibility maps. The findings showed that the HHO algorithm effectively improved the performance of ANN in both recognizing (AUROCANN = 0.731 and AUROCHHO–ANN = 0.777) and predicting (AUROCANN = 0.720 and AUROCHHO–ANN = 0.773) the landslide pattern.

scholarly journals Digital elevation models (DEM) used to assess soil erosion risks: a case study in Boyaca, Colombia

2016 ◽  
Vol 34 (2) ◽  
pp. 239-249 ◽  
Author(s):  
Jeiner Yobany Buitrago E. ◽  
Luis Joel Martínez M.
Keyword(s):  
Slope Aspect ◽  
Mountainous Area ◽  
Stream Power ◽  
Topographic Wetness Index ◽  
Alos Palsar ◽  
Erosion Risk ◽  
Digital Elevation ◽  
Stream Power Index ◽  
Logic Functions ◽  
Soil Surveys

The objective of this research was to develop a model for assessing the risk of erosion, exploring the potential of DEMs from SRTM, ASTER, ALOS PALSAR and one made with interpolation of a 1:25,000 contour map to calculate the variables of the relief that have greater impact on erosion. Several geomorphometric parameters, such as slope, aspect, profile and plan curvature, topographic wetness index, stream power index, and sediment transport capacity were computed from the DEM's elevation, some fuzzy logic functions proposed to evaluate the incidence of each parameter on erosion risk in a mountainous area of Colombia. The results showed that the use of DEM data is a relatively easy, uncostly method to identify, in a qualitative way, the risk of erosion and contribute to the enhancement of erosion information that is obtained with conventional general soil surveys.

Quantitative Hazard Analysis for Landslides in Hulu Kelang area, Malaysia

2015 ◽  
Vol 73 (1) ◽  
Author(s):  
Nader Saadatkhah ◽  
Azman Kassim ◽  
Lee Min Lee ◽  
Gambo Haruna Yunusa
Keyword(s):  
Landslide Susceptibility ◽  
Frequency Ratio ◽  
Hazard Analysis ◽  
Drainage Density ◽  
Weighting Factor ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Operating Characteristics ◽  
Stream Power ◽  
Topographic Wetness Index

Hulu Kelang is a region in Malaysia which is very susceptible to landslides. From 1990 to 2011, a total of 28 major landslide events had been reported in this area. This paper evaluates and compares the probability-frequency ratio (FR), statistical index (Wi), and weighting factor (Wf), used for assessing landslide susceptibility in the study area. Eleven landslide influencing factors were considered in the analyses. These factors included lithology, land cover, curvature, slope inclination, slope aspect, drainage density, elevation, distance to lake and stream, distance to road and trenches and two indices (the stream power index (SPI) and the topographic wetness index (TWI)) found in the area. The accuracy of the maps produced from the three models was verified using a receiver operating characteristics (ROC). The verification results indicated that the probability-frequency ratio (FR) model which was developed quantitatively based on probabilistic analysis of spatial distribution of historical landslide events was capable of producing a more reliable landslide susceptibility map in this study area compared to its other counterparts. About 89% of the landslide locations have been predicted accurately by using the FR map. 

scholarly journals Spatial Modelling of Gully Erosion Using GIS and R Programing: A Comparison among Three Data Mining Algorithms

2018 ◽  
Vol 8 (8) ◽  
pp. 1369 ◽  
Author(s):  
Alireza Arabameri ◽  
Biswajeet Pradhan ◽  
Hamid Reza Pourghasemi ◽  
Khalil Rezaei ◽  
Norman Kerle
Keyword(s):  
Data Mining ◽  
Spatial Relationship ◽  
Area Under The Curve ◽  
Regression Tree ◽  
Drainage Density ◽  
Gully Erosion ◽  
Slope Aspect ◽  
Topographic Wetness Index ◽  
Boosted Regression Tree ◽  
Area Index

Gully erosion triggers land degradation and restricts the use of land. This study assesses the spatial relationship between gully erosion (GE) and geo-environmental variables (GEVs) using Weights-of-Evidence (WoE) Bayes theory, and then applies three data mining methods—Random Forest (RF), boosted regression tree (BRT), and multivariate adaptive regression spline (MARS)—for gully erosion susceptibility mapping (GESM) in the Shahroud watershed, Iran. Gully locations were identified by extensive field surveys, and a total of 172 GE locations were mapped. Twelve gully-related GEVs: Elevation, slope degree, slope aspect, plan curvature, convergence index, topographic wetness index (TWI), lithology, land use/land cover (LU/LC), distance from rivers, distance from roads, drainage density, and NDVI were selected to model GE. The results of variables importance by RF and BRT models indicated that distance from road, elevation, and lithology had the highest effect on GE occurrence. The area under the curve (AUC) and seed cell area index (SCAI) methods were used to validate the three GE maps. The results showed that AUC for the three models varies from 0.911 to 0.927, whereas the RF model had a prediction accuracy of 0.927 as per SCAI values, when compared to the other models. The findings will be of help for planning and developing the studied region.

scholarly journals Landform classification using GIS techniques. The case of Kimi municipality area, Euboea Island, Greece.

2016 ◽  
Vol 47 (1) ◽  
pp. 264 ◽  
Author(s):  
I. Ilia ◽  
D. Rozos ◽  
I. Koumantakis
Keyword(s):  
Spatial Analysis ◽  
Power Index ◽  
Stream Power ◽  
Topographic Wetness Index ◽  
Wetness Index ◽  
Topographic Position Index ◽  
Landform Classification ◽  
Essential Parameter ◽  
Position Index ◽  
Stream Power Index

The main objective of this paper is to classify landforms in Kimi municipality area of Euboea Island, Greece using advanced spatial techniques. Landform categories were determined by conducting morphometric analysis through the use of advanced GIS functions. In particular, the process of classifying the landscape into landform categories was based on Topographic Position Index (TPI). The main topographic elements such as slope inclination, aspect, slope shape (curvature), topographic wetness index and stream power index were obtained from the DEM file of the study area. Landform classification was obtained using TPI grids and the classes were related with the geological pattern and the land cover by sophisticated spatial analysis function. The knowledge obtained from the present study could be useful in identifying areas prone to land degradation and instability problems in which landforms are identified as an essential parameter

scholarly journals GIS-Based Site Selection for Check Dams in Watersheds: Considering Geomorphometric and Topo-Hydrological Factors

2019 ◽  
Vol 11 (20) ◽  
pp. 5639 ◽  
Author(s):  
Omid Rahmati ◽  
Zahra Kalantari ◽  
Mahmood Samadi ◽  
Evelyn Uuemaai ◽  
Davoud Davoudi Moghaddam ◽  
...  
Keyword(s):  
Sediment Transport ◽  
Spatial Data ◽  
Site Selection ◽  
Drainage Density ◽  
Stream Power ◽  
Topographic Wetness Index ◽  
Stream Network ◽  
Flood Peak ◽  
Check Dams ◽  
The World

Check dams are widely used watershed management measures for reducing flood peak discharge and sediment transport, and increasing lag time and groundwater recharge throughout the world. However, identifying the best suitable sites for check dams within the stream networks of various watersheds remains challenging. This study aimed to develop an open-source software with user-friendly interface for screening the stream network possibilities and identifying and guiding the selection of suitable sites for check dams within watersheds. In this developed site selection software (SSS), multi-criteria decision analysis (MCDA) was integrated into geographic information systems (GIS), which allowed for numerous spatial data of the multiple criteria to be relatively simply and visually processed. Different geomorphometric and topo-hydrological factors were considered and accounted for to enhance the SSS identification of the best locations for check dams. The factors included topographic wetness index (TWI), terrain ruggedness index (TRI), topographic position index (TPI), sediment transport index (STI), stream power index (SPI), slope, drainage density (DD), and stream order (SO). The site identification performance of the SSS was assessed using the receiver operating characteristic (ROC) curve method, with results for the case study example of the Poldokhtar watershed in Iran showing excellent performance and identifying 327 potential sites for efficient check dam construction in this watershed. The SSS tool is not site-specific but is rather general, adaptive, and comprehensive, such that it can and should be further applied and tested across different watersheds and parts of the world.

An illustrated introduction to general geomorphometry

2017 ◽  
Vol 41 (6) ◽  
pp. 723-752 ◽  
Author(s):  
Igor V Florinsky
Keyword(s):  
Analytical Description ◽  
Shape Index ◽  
Shuttle Radar Topography Mission ◽  
Slope Aspect ◽  
Triaxial Ellipsoid ◽  
Stream Power ◽  
Mathematical Concepts ◽  
Non Local ◽  
Stream Power Index ◽  
Horizontal Curvature

Geomorphometry is widely used to solve various multiscale geoscientific problems. For the successful application of geomorphometric methods, a researcher should know the basic mathematical concepts of geomorphometry and be aware of the system of morphometric variables, as well as understand their physical, mathematical and geographical meanings. This paper reviews the basic mathematical concepts of general geomorphometry. First, we discuss the notion of the topographic surface and its limitations. Second, we present definitions, formulae and meanings for four main groups of morphometric variables, such as local, non-local, two-field specific and combined topographic attributes, and we review the following 29 fundamental morphometric variables: slope, aspect, northwardness, eastwardness, plan curvature, horizontal curvature, vertical curvature, difference curvature, horizontal excess curvature, vertical excess curvature, accumulation curvature, ring curvature, minimal curvature, maximal curvature, mean curvature, Gaussian curvature, unsphericity curvature, rotor, Laplacian, shape index, curvedness, horizontal curvature deflection, vertical curvature deflection, catchment area, dispersive area, reflectance, insolation, topographic index and stream power index. For illustrations, we use a digital elevation model (DEM) of Mount Ararat, extracted from the Shuttle Radar Topography Mission (SRTM) 1-arc-second DEM. The DEM was treated by a spectral analytical method. Finally, we briefly discuss the main paradox of general geomorphometry associated with the smoothness of the topographic surface and the non-smoothness of the real topography; application of morphometric variables; statistical aspects of geomorphometric modelling, including relationships between morphometric variables and roughness indices; and some pending problems of general geomorphometry (i.e. analysis of inner surfaces of caves, analytical description of non-local attributes and structural lines, as well as modelling on a triaxial ellipsoid). The paper can be used as a reference guide on general geomorphometry.

scholarly journals Landslide susceptibility analysis using decision tree method, Phidim, Eastern Nepal

2013 ◽  
Vol 15 ◽  
pp. 69-76 ◽  
Author(s):  
Chandra Prakash Poudyal
Keyword(s):  
Decision Tree ◽  
Landslide Susceptibility ◽  
Power Index ◽  
Geographical Information ◽  
Susceptibility Map ◽  
Effective Parameters ◽  
Stream Power ◽  
Topographic Wetness Index ◽  
Landslide Susceptibility Map ◽  
Stream Power Index

The decision tree is one of the new methods used for the determination of landslide susceptibility in the study area. The Phidim area is selected for the application of this method. The total surface area is 168.07 sq. km, and is located at the eastern part of Nepal. There are total of 10 different data bases used for this study which are; geological formation, elevation, slope, curvature, aspect, stream power index, topographic wetness index, distance from drainage, lineaments, and slope length, and are considered as landslide conditioning factors. Geographical information system (GIS) is used as basic tools and ARC/View is used for the processing data analysis and final map preparation. For the decision tree analysis the PASW 18 (statistical tool) is used to generate values of each factor. According to the results of decision tree, two geological formations; stream power index and slope are found as the most effective parameters on the landslide occurrence in the study area. Using the predicted values, the landslide susceptibility map of the study area is produced. To assess the performance of the produced susceptibility map, the area under curve (AUC) is drawn. The AUC value of the produced landslide susceptibility map has been obtained as 95.9%. According to the results of the AUC evaluation, the produced map has showed a good performance. As to wrap up, the produced map is able to be used for medium scaled and regional planning purposes. DOI: http://dx.doi.org/10.3126/bdg.v15i0.7419 Bulletin of the Department of Geology, Vol. 15, 2012, pp. 69-76

scholarly journals Spatial Landslide Susceptibility Assessment Based on Novel Neural-Metaheuristic Geographic Information System Based Ensembles

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4698 ◽  
Author(s):  
Hossein Moayedi ◽  
Abdolreza Osouli ◽  
Dieu Tien Bui ◽  
Loke Kok Foong
Keyword(s):  
Neural Network ◽  
Spatial Interaction ◽  
Characteristic Curve ◽  
Absolute Error ◽  
Optimization Techniques ◽  
Slope Aspect ◽  
Stream Power ◽  
Topographic Wetness Index ◽  
Learning Capability ◽  
Conditioning Factors

Regular optimization techniques have been widely used in landslide-related problems. This paper outlines two novel optimizations of artificial neural network (ANN) using grey wolf optimization (GWO) and biogeography-based optimization (BBO) metaheuristic algorithms in the Ardabil province, Iran. To this end, these algorithms are synthesized with a multi-layer perceptron (MLP) neural network for optimizing its computational parameters. The used spatial database consists of fourteen landslide conditioning factors, namely elevation, slope aspect, land use, plan curvature, profile curvature, soil type, distance to river, distance to road, distance to fault, rainfall, slope degree, stream power index (SPI), topographic wetness index (TWI) and lithology. 70% of the identified landslides are randomly selected to train the proposed models and the remaining 30% is used to evaluate the accuracy of them. Also, the frequency ratio theory is used to analyze the spatial interaction between the landslide and conditioning factors. Obtained values of area under the receiver operating characteristic curve, as well as mean square error and mean absolute error showed that both GWO and BBO hybrid algorithms could efficiently improve the learning capability of the MLP. Besides, the BBO-based ensemble surpasses other implemented models.

scholarly journals Optimal Combination of Predictors and Algorithms for Forest Above-Ground Biomass Mapping from Sentinel and SRTM Data

2019 ◽  
Vol 11 (4) ◽  
pp. 414 ◽  
Author(s):  
Lin Chen ◽  
Yeqiao Wang ◽  
Chunying Ren ◽  
Bai Zhang ◽  
Zongming Wang
Keyword(s):  
Vegetation Indices ◽  
Absolute Error ◽  
Optimal Combination ◽  
Support Vector ◽  
Above Ground Biomass ◽  
Stream Power ◽  
Topographic Wetness Index ◽  
Ground Biomass ◽  
Shuttle Radar Topographic Mission ◽  
Stream Power Index

Accurate forest above-ground biomass (AGB) mapping is crucial for sustaining forest management and carbon cycle tracking. The Shuttle Radar Topographic Mission (SRTM) and Sentinel satellite series offer opportunities for forest AGB monitoring. In this study, predictors filtered from 121 variables from Sentinel-1 synthetic aperture radar (SAR), Sentinal-2 multispectral instrument (MSI) and SRTM digital elevation model (DEM) data were composed into four groups and evaluated for their effectiveness in prediction of AGB. Five evaluated algorithms include linear regression such as stepwise regression (SWR) and geographically weighted regression (GWR); machine learning (ML) such as artificial neural network (ANN), support vector machine for regression (SVR), and random forest (RF). The results showed that the RF model used predictors from both the Sentinel series and SRTM DEM performed the best, based on the independent validation set. The RF model achieved accuracy with the mean error, mean absolute error, root mean square error, and correlation coefficient in 1.39, 25.48, 61.11 Mg·ha−1 and 0.9769, respectively. Texture characteristics, reflectance, vegetation indices, elevation, stream power index, topographic wetness index and surface roughness were recommended predictors for AGB prediction. Predictor variables were more important than algorithms for improving the accuracy of AGB estimates. The study demonstrated encouraging results in the optimal combination of predictors and algorithms for forest AGB mapping, using openly accessible and fine-resolution data based on RF algorithms.

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