Optimized object-based image classification: development of landslide knowledge decision support system

Wan, Shiuan; Lei, Tsu Chiang; Chou, Tien-Yin

doi:10.1007/s12517-013-0952-z

Cited by 6 publications

(2 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Shannon's entropy is another important bivariate algorithm used to analyze landslide susceptibility [26]. Other statistical methods for landslide susceptibility mapping (zoning) are probabilistic models, certainty factors, information values, modified bayesian estimation, weighting variables, weighted linear combinations of instability variables, landside nominal risk variables [27][28][29][30], spatial multicriteria evaluation, index of entropy, and Dempster-Shafer models [31][32][33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Shannon Entropy and Weights of Evidence Models in Landslide Susceptibility Mapping for the Pithoragarh District of Uttarakhand State, India

Dam

Amiri

Al‐Ansari

et al. 2022

Advances in Civil Engineering

View full text Add to dashboard Cite

Landslide susceptibility mapping is considered a useful tool for planning, disaster management, and natural hazard mitigation of a region. Although there are different methods for predicting landslide susceptibility, the bivariate statistical analysis method is considered to be simple and popular. In this study, the main aim is to evaluate the performance of Shannon entropy (SE) and weights of evidence (WOE) statistical models in landslide susceptibility mapping of Pithoragarh district of Uttarakhand state, India. For this purpose, ten landslide affecting factors, namely, slope degree, aspect, curvature, elevation, land cover, slope forming materials, geomorphology (landforms), distance to rivers, distance to roads, and overburden depth were used for the development of landslide susceptibility maps using the SE and WOE methods. Data extracted from the Google Earth images, Aster Digital Elevation Model, and Geological Survey of India report were used for the construction and evaluation of landslide susceptibility models and maps. The landslide data of 91 locations were randomly divided into two parts in the ratio of 70 : 30 using GIS software that is 70% data was used for training the models and 30% data was used for testing and validating the models. Performance of the applied models was evaluated using area under the AUC (area under the curve) ROC (receiver operating characteristics) curve. Results indicated that the WOE model is having better accuracy (AUCWOE = 68.75%) than the SE model (AUCSE = 52.17%) in the development of landslide susceptibility maps. Hence, WOE model can be used for the development of accurate landslide susceptibility maps which can provide useful information to decision maker and policy planner in better development of landslide prone areas.

show abstract

Section: Introductionmentioning

confidence: 99%

Evaluation of Shannon Entropy and Weights of Evidence Models in Landslide Susceptibility Mapping for the Pithoragarh District of Uttarakhand State, India

Dam

Amiri

Al‐Ansari

et al. 2022

Advances in Civil Engineering

View full text Add to dashboard Cite

show abstract

“…Furthermore, few other algorithms with optimum performance and a higher degree of accuracy used in the landslide susceptibility mapping include Discriminant Analysis, Generalized Additive Models, Evidential Belief Functions, Weighted Linear Combinations, and Weights of Evidence [16][17][18][19][20]. Also, Certainty Factors, Probabilistic Models, Modified Bayesian Estimation, Information Values [21][22][23][24], Dempster-Shafer models, and Index of Entropy are also widely used as the statistical quantitative techniques to map the landslide susceptible areas [25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

GIS-Based Soft Computing Models for Landslide Susceptibility Mapping: A Case Study of Pithoragarh District, Uttarakhand State, India

Tran

Dam

Jalal

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The main objective of the study was to investigate performance of three soft computing models: Naïve Bayes (NB), Multilayer Perceptron (MLP) neural network classifier, and Alternating Decision Tree (ADT) in landslide susceptibility mapping of Pithoragarh District of Uttarakhand State, India. For this purpose, data of 91 past landslide locations and ten landslide influencing factors, namely, slope degree, curvature, aspect, land cover, slope forming materials (SFM), elevation, distance to rivers, geomorphology, overburden depth, and distance to roads were considered in the models study. Thematic maps of the Geological Survey of India (GSI), Google Earth images, and Aster Digital Elevation Model (DEM) were used for the development of landslide susceptibility maps in the Geographic Information System (GIS) environment. Landslide locations data was divided into a 70 : 30 ratio for the training (70%) and testing/validation (30%) of the three models. Standard statistical measures, namely, Positive Predicted Values (PPV), Negative Predicted Values (NPV), Sensitivity, Specificity, Mean Absolute Error (MAE), Root Mean Squire Error (RMSE), and Area under the ROC Curve (AUC) were used for the evaluation of the models. All the three soft computing models used in this study have shown good performance in the accurate development of landslide susceptibility maps, but performance of the ADT and MLP is better than NB. Therefore, these models can be used for the construction of accurate landslide susceptibility maps in other landslide-prone areas also.

show abstract

Mapping and monitoring geological hazards using optical, LiDAR, and synthetic aperture RADAR image data

et al. 2014

View full text Add to dashboard Cite

Geological hazards and their effects are often geographically widespread. Consequently, their effective mapping and monitoring is best conducted using satellite and airborne imaging platforms to obtain broad scale, synoptic coverage. With a multitude of hazards and effects, potential data types, and processing techniques, it can be challenging to determine the best approach for mapping and monitoring. It is therefore critical to understand the spatial and temporal effects of any particular hazard on the environment before selecting the most appropriate data type/s and processing techniques to apply. This review is designed to assist the decision-making and selection process when embarking on a hazard mapping or monitoring exercise. It focuses on the application of optical, LiDAR, and synthetic aperture RADAR technologies for the assessment of pre-event risk and post-event damage. Geological hazards of global interest summarized here are landslides and erosion; seismic and tectonic hazards; ground subsidence; and flooding and tsunami

show abstract

Optimized object-based image classification: development of landslide knowledge decision support system

Cited by 6 publications

References 28 publications

Evaluation of Shannon Entropy and Weights of Evidence Models in Landslide Susceptibility Mapping for the Pithoragarh District of Uttarakhand State, India

Evaluation of Shannon Entropy and Weights of Evidence Models in Landslide Susceptibility Mapping for the Pithoragarh District of Uttarakhand State, India

GIS-Based Soft Computing Models for Landslide Susceptibility Mapping: A Case Study of Pithoragarh District, Uttarakhand State, India

Mapping and monitoring geological hazards using optical, LiDAR, and synthetic aperture RADAR image data

Contact Info

Product

Resources

About