Landslide hazard zoning along Himalayan Kaghan Valley of Pakistan—by integration of GPS, GIS, and remote sensing technology

Akbar, Tahir Ali; Ha, Sung-Ryong

doi:10.1007/s10346-011-0260-1

Cited by 66 publications

(32 citation statements)

References 18 publications

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“…When the distance was larger than 4000 m, no landslide was found in field survey, the influence degree dropped to the minimize value, 0. In mountainous area, road may artificially cut towards the steep slopes which might cause high landslide occurrence in the near distances from the road [41].…”

Section: In Terrain Cutting Groupmentioning

confidence: 99%

Landslide Susceptibility Mapping Based on Selected Optimal Combination of Landslide Predisposing Factors in a Large Catchment

Wang

Huang

et al. 2015

Sustainability

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Landslides are usually initiated under complex geological conditions. It is of great significance to find out the optimal combination of predisposing factors and create an accurate landslide susceptibility map based on them. In this paper, the Information Value Model was modified to make the Modified Information Value (MIV) Model, and together with GIS (Geographical Information System) and AUC (Area Under Receiver Operating Characteristic Curve) test, 32 factor combinations were evaluated separately, and factor combination group with members Slope, Lithology, Drainage network, Annual precipitation, Faults, Road and Vegetation was selected as the optimal combination group with an accuracy of 95.0%. Based on this group, a landslide susceptibility zonation map was drawn, where the study area was reclassified into five classes, presenting an accurate description of different levels of landslide susceptibility, with 79.41% and 13.67% of the validating field survey landslides falling in the Very High and High zones, respectively, mainly distributed in the south and southeast of the catchment. It showed that MIV model can tackle the problem of "no data in subclass" well, generate the true information value and show real running trend, which performs well in showing the relationship between predisposing factors and landslide occurrence and can be used for preliminary landslide susceptibility assessment in the study area.

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Section: In Terrain Cutting Groupmentioning

confidence: 99%

Landslide Susceptibility Mapping Based on Selected Optimal Combination of Landslide Predisposing Factors in a Large Catchment

Wang

Huang

et al. 2015

Sustainability

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“…It was also observed that the two wells (20 and 26) where acifluorfen was not detected showed rank 1st, which represents the greatest leaching. There could be two possibilities for the fact that acifluorfen was not detected in these four wells (15,20,26, and 42) despite higher leaching ranks given to these wells by ArcPRZM-3. Firstly, some other pesticide (like bentazon) might have been applied on these crops instead of acifluorfen.…”

Section: Sensitivity Analysis For Arcprzm-3 Input Parametersmentioning

confidence: 98%

“…The major crops in the study area are soybeans, rice, and sorghum. The percentages of areas utilized in Randolph, Independence, and Woodruff counties for growth of (i) soybeans are 16,20, and 36%, (ii) rice are 12, 6, and 22%, and (iii) sorghum are 2, 2, and 16%, respectively [38].…”

Section: Study Areamentioning

confidence: 99%

“…Groundwater vulnerability assessment can be obtained by analyzing the pesticides leaching toward groundwater using the simulation models, e.g., depth to water, net recharge, aquifer media, soil media, topography, impact of vadose zone, and hydraulic conductivity (DRASTIC) [9], groundwater loading effects of agricultural management systems (GLEAMS) [10], HYDRUS-1D [11], leaching estimation and chemistry model (LEACHM) [12], PRZM [13], root zone water quality model (RZWQM) [14], hierarchical cluster analysis (HCA) [15], and groundwater modeling tool for grass (GMTG) [16]. Pesticide simulation results obtained from these models can be presented in the form groundwater vulnerability risk maps (e.g., [17][18][19]) using geographic information system (GIS) as it is a useful geospatial technology in risk identification [20]. These risk maps are effective tools for the management of groundwater sources [18].…”

Section: Introductionmentioning

confidence: 99%

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Pesticide Health Risk Mapping and Sensitivity Analysis of Parameters in Groundwater Vulnerability Assessment

Akbar

2013

CLEAN Soil Air Water

Self Cite

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The extensive use of pesticides for increasing the agricultural production is affecting the quality of groundwater. The objectives of this article are to (i) develop pesticide relative leaching ranks for well sites, (ii) develop maps for human health risks due to pesticide applications, and (iii) identify the most significant parameters in pesticide simulations for groundwater vulnerability assessment. The methods include (i) development of acifluorfen relative leaching ranks for 25 well sites using ArcPRZM-3, (ii) development of health risk maps using model simulated maximum dissolved bentazon concentrations on the basis of USA drinking water quality guidelines, (iii) sensitivity analysis for 14 ArcPRZM-3 input parameters using the Plackett-Burman method. ArcPRZM-3 is a user-friendly system for spatial modeling of pesticide leaching from surface to groundwater. Thirteen acifluorfen relative leaching potential ranks were developed in which the pesticide leaching decrease from 1 to 13. The model predicted ranks for well 34 and well 9 were 2nd and 3rd, respectively, and acifluorfen was detected in both wells during the physical monitoring. The percentages of high health risks in the agricultural areas were 48.38 and 72.72% for Randolph and Independence Counties, respectively. The most significant parameters were thickness of horizon compartment, runoff curve number of antecedent moisture condition II for cropping, soil bulk density, and total application of pesticide. The irrigation, soil permeability, and numerical dispersion could impact the pesticide leaching in soils toward groundwater. The ArcPRZM-3 system could be efficiently applied for spatial modeling and mapping of pesticide concentrations for groundwater vulnerability assessment on a large scale.

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“…Geospatial technologies like the use of GIS, Global Positioning System (GPS), and Remote Sensing (RS) are useful in the hazard assessment, risk identification, and disaster management for landslides. GPS is a space-based global navigation satellite system which provides the information of position and time anywhere in the world in all weather conditions (Akbar and Ha 2011). Previous studies showed the application of GPS for mapping and identifying landslide zones.…”

Section: Introductionmentioning

confidence: 99%

Landslide susceptibility mapping using multi-criteria evaluation techniques in Chittagong Metropolitan Area, Bangladesh

2014

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Landslides are a common hazard in the highly urbanized hilly areas in Chittagong Metropolitan Area (CMA), Bangladesh. The main cause of the landslides is torrential rain in short period of time. This area experiences several landslides each year, resulting in casualties, property damage, and economic loss. Therefore, the primary objective of this research is to produce the Landslide Susceptibility Maps for CMA so that appropriate landslide disaster risk reduction strategies can be developed. In this research, three different Geographic Information Systembased Multi-Criteria Decision Analysis methods-the Artificial Hierarchy Process (AHP), Weighted Linear Combination (WLC), and Ordered Weighted Average (OWA)-were applied to scientifically assess the landslide susceptible areas in CMA. Nine different thematic layers or landslide causative factors were considered. Then, seven different landslide susceptible scenarios were generated based on the three weighted overlay techniques. Later, the performances of the methods were validated using the area under the relative operating characteristic curves. The accuracies of the landslide susceptibility maps produced by the AHP, WLC_1, WLC_2, WLC_3, OWA_1, OWA_2, and OWA_3 methods were found as 89. 80, 83.90, 91.10, 88.50, 90.40, 95.10, and 87.10 %, respectively. The verification results showed satisfactory agreement between the susceptibility maps produced and the existing data on the 20 historical landslide locations.

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Landslide hazard zoning along Himalayan Kaghan Valley of Pakistan—by integration of GPS, GIS, and remote sensing technology

Cited by 66 publications

References 18 publications

Landslide Susceptibility Mapping Based on Selected Optimal Combination of Landslide Predisposing Factors in a Large Catchment

Landslide Susceptibility Mapping Based on Selected Optimal Combination of Landslide Predisposing Factors in a Large Catchment

Pesticide Health Risk Mapping and Sensitivity Analysis of Parameters in Groundwater Vulnerability Assessment

Landslide susceptibility mapping using multi-criteria evaluation techniques in Chittagong Metropolitan Area, Bangladesh

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