Regional-scale landslide susceptibility assessment for the hilly state of Uttarakhand, NW Himalaya, India

Gupta, Vikram; Kumar, Sandeep; Kaur, Ramandeep; Tandon, Ruchika Sharma

doi:10.1007/s12040-021-01746-4

Cited by 25 publications

(4 citation statements)

References 40 publications

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“…The area has a relief of 7654 meters and altitudes ranging from 123 to 7777 meters. (Gupta et al, 2022) Fig 1 . Study Area Map of Uttarakhand.…”

Section: Study Areamentioning

confidence: 99%

A Comparative Assessment of Landslide Prediction Capability of Machine Learning Methods using Frequency Ratio (FR), Shannon Entropy (SE), and Analytical Hierarchy (AHP) Techniques: A Case Study of Uttarakhand, India

Alam,

Siddiqui,

Shamim

et al. 2024

Preprint

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Mapping landslide susceptibility is crucial for defining high-risk zones and preventing property and human casualties. The Uttarakhand provision, which comes under the Himalayan region, has a high potential for landslide occurrence. A landslide susceptibility map was created using satellite imagery, in-depth field research, and aerial photos. The historical landslide inventory of the state's 14698 total landslides was randomly bifurcated into 70% (10289) for training purposes and 30% (4409) for data validation. Eleven landslide-causative factors (Slope, Aspect, Curvature, Topographic Position Index (TPI), Topographic Wetness Index (TWI), Geology, Normalized Difference Vegetation Index (NDVI), Distance to Road, Distance to Stream, Distance to Fault, and Rainfall) were selected for susceptibility assessment. The landslide susceptibility zonation was created using the Shannon Entropy (SE), Frequency Ratio (FR), and Analytical Hierarchy Process (AHP) techniques, along with the causative factors. The AHP method is effectively utilized in LSM to prioritize and weigh the importance of different causative factors contributing to landslide occurrence, while Shannon Entropy uses the method of discrete probability distribution to quantify the uncertainty or variability associated with different causative factors. The FR, AHP, and SE models were validated using the AUC curve, yielding 92%, 89%, and 81% success rates and predictive rates of 90%, 87%, and 77%, respectively. The FR model is most suitable, more efficient, and valuable for future planning in the study area.

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“…The area has a relief of 7654 meters and altitudes ranging from 123 to 7777 meters. (Gupta et al, 2022) Fig 1 . Study Area Map of Uttarakhand.…”

Section: Study Areamentioning

confidence: 99%

A Comparative Assessment of Landslide Prediction Capability of Machine Learning Methods using Frequency Ratio (FR), Shannon Entropy (SE), and Analytical Hierarchy (AHP) Techniques: A Case Study of Uttarakhand, India

Alam,

Siddiqui,

Shamim

et al. 2024

Preprint

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“…It helps to rank variables on the basis of their importance. The IV is calculated using the following formula (Borgonovo et al, 2021;Gupta et al, 2022;Verma, 2020;Wang et al, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

“…WoE assigns a numerical score to each category of independent variables that describe the strength of its relationship with the target variable (Batar & Watanabe, 2021;Lund, 2021;Parsai & Kumar, 2021). For selecting the most informative and relevant independent variable in predicting the target variable, Information Value (IV) can be used (Borgonovo, Hazen, Jose, & Plischke, 2021;Gupta, Kumar, Kaur, & Tandon, 2022;Verma, 2020;Wang et al, 2020). In this article, the author will examine the determination of the weighting MAUT using WoE and IV methods to rank several universities in Medan.…”

Section: Introductionmentioning

confidence: 99%

Ranking Universities in Medan Using WoE and IV in Weighting of MAUT

Fadilah

Perdana

Farhana

2023

SinkrOn

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Throughout Indonesia, including Medan, the popularity of a university can be specified by the ranking of a university. There are five assessment components which important for ranking universities under the Ministry of Education, Culture, Research, Technology and Higher Education, such as the Quality of Human Resources, Institutional Quality, Quality of Student Activities, Quality of Research and Community Service, and Quality of Innovation. Multi Attribute Utility Theory (MAUT) is one of the decision support system (DSS) methods that can be used to calculate campus rankings. However, the researcher were determining the weight of MAUT method based on their preferences and it was subjective. Weight of Evidence (WoE) can be used to assign a numerical score to each category of independent variables that describes the strength of its relationship to the target variable. In selecting the independent variable that is most informative and relevant in predicting the target variable, Information Value (IV) can be used. Based on the results, college B is the most popular university out of ten universities in Medan, with the highest evaluation value 0.796296296 using MAUT method and 0.923794719 using MAUT method with WoE & IV. The last position is J college with the lowest evaluation value 0.1666666667 for MAUT method and 0.02540176 for MAUT method with WoE & IV. The weighting of MAUT method with WoE and IV produces more optimal evaluation value than the the original MAUT method.

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“…[22]. According to various research that has thoroughly detailed the formulation of the Weights-of-Evidence technique, The weights in each class for each parameter are obtained according to the occurrence/non-occurrence of floods in an area (2), (3),[23]-[25]. in Gending District by Comparison Frequency Ratio and Weight of Evidence for Mitigation Strategy https://dx.doi.org/10.30737/ukarst.v7i1.3999…”

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confidence: 99%

Flood Susceptibility Mapping in Gending District by Comparison Frequency Ratio and Weight of Evidence for Mitigation Strategy

Maulana,

Hidayah,

Halik

2023

U Karst

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Floods are natural disasters that occur all over the world. Gending District in Probolinggo Regency, East Java, is an area that often experiences floods and causes various losses. A flood susceptibility map needed to prepare appropriate mitigation actions. Choosing the right method will produce a more accurate flood susceptibility map. The research aims to make a flood susceptibility map in Gending District by comparing the Frequency Ratio (FR) and Weight of Evidence (WofE) methods and providing appropriate mitigation recommendations. Six data factors that cause flooding are used: slope, elevation, land use, normalized difference vegetation index (NDVI), curvature, and rainfall. The data obtained were processed using the FR and WofE methods, which were then validated using the Receiver Operating Characteristics (ROC) method. The validation value is calculated using the ROC chart's Area Under Curve (AUC). The higher the AUC value, the better. The study's results revealed that the correct method for making a flood susceptibility map in Gending District was FR with an AUC value of 92.8%, while the WofE method was 90.4%. The flood susceptibility map illustrates that 14% of the area is in very high and high flood-prone zones, 23% is in the moderate zone, and 63% is in the safe zone. The appropriate mitigation strategy based on the highest FR value is creating drainage networks, and green open spaces, normalizing rivers in residential areas, and implementing selective logging and reforestation programs. The results of this study are used to reduce the impact and risk of future flood disasters.

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Regional-scale landslide susceptibility assessment for the hilly state of Uttarakhand, NW Himalaya, India

Cited by 25 publications

References 40 publications

A Comparative Assessment of Landslide Prediction Capability of Machine Learning Methods using Frequency Ratio (FR), Shannon Entropy (SE), and Analytical Hierarchy (AHP) Techniques: A Case Study of Uttarakhand, India

A Comparative Assessment of Landslide Prediction Capability of Machine Learning Methods using Frequency Ratio (FR), Shannon Entropy (SE), and Analytical Hierarchy (AHP) Techniques: A Case Study of Uttarakhand, India

Ranking Universities in Medan Using WoE and IV in Weighting of MAUT

Flood Susceptibility Mapping in Gending District by Comparison Frequency Ratio and Weight of Evidence for Mitigation Strategy

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