Sinkhole development is a typical geological disaster found in areas of carbonate bedrock. Compar... more Sinkhole development is a typical geological disaster found in areas of carbonate bedrock. Compared with other geological disasters, sinkholes are considerably smaller and scattered according to scale and spatial distribution. Nevertheless, detecting and investigating sinkholes have become increasingly challenging. This study proposes a novel method by applying case-based reasoning (CBR) combined with object-based image analysis and genetic algorithms (GAs) to detect the sinkholes using high-resolution aerial images. This case study was performed in Paitan Town, Guangdong Province, China. The method comprises three major steps: (1) multi-image segmentation, (2) GA-based feature selection, and (3) application of CBR techniques. The detected sinkholes were categorized into three classes: buried, collapse type I, and collapse type II. The experiment demonstrated that the proposed method can obtain higher accuracy compared with the traditional supervised maximum likelihood classifier (MLC). The overall accuracy of CBR classification and MLC for the collapse area was 0.88 and 0.71, respectively. In addition, the kappa coefficient for CBR classification (0.81) was higher than that for MLC (0.5). A similar case library was also applied to another trial area for validation, the satisfactory results of which suggested that CBR is applicable for independently detecting sinkholes.
This paper proposes an automatic method for detecting landslides by using an integrated approach ... more This paper proposes an automatic method for detecting landslides by using an integrated approach comprising object-oriented image analysis (OOIA), a genetic algorithm (GA), and a case-based reasoning (CBR) technique. It consists of three main phases: (1) image processing and multi-image segmentation; (2) feature optimization; and (3) detecting landslides. The proposed approach was employed in a fast-growing urban region, the Pearl River Delta in South China. The results of detection were validated with the help of field surveys. The experimental results indicated that the proposed OOIA-GA-CBR (0.87) demonstrates higher classification performance than the stand-alone OOIA (0.75) method for detecting landslides. The area under curve (AUC) value was also higher than that of the simple OOIA, indicating the high efficiency of the proposed landslide detection approach. The case library created using the integrated model can be reused for time-independent analysis, thus rendering our approach superior in comparison to other traditional methods, such as the maximum likelihood classifier. The results of this study thus facilitate fast generation of accurate landslide inventory maps, which will eventually extend our understanding of the evolution of landscapes shaped by landslide processes.
This paper assesses the potentiality of certainty factor models (CF) for the best suitable causat... more This paper assesses the potentiality of certainty factor models (CF) for the best suitable causative factors extraction for landslide susceptibility mapping in the Sado Island, Niigata Prefecture, Japan. To test the applicability of CF, a landslide inventory map provided by National Research Institute for Earth Science and Disaster Prevention (NIED) was split into two subsets: (i) 70% of the landslides in the inventory to be used for building the CF based model; (ii) 30% of the landslides to be used for the validation purpose. A spatial database with fifteen landslide causative factors was then constructed by processing ALOS satellite images, aerial photos, topographical and geological maps. CF model was then applied to select the best subset from the fifteen factors. Using all fifteen factors and the best subset factors, landslide susceptibility maps were produced using statistical index (SI) and logistic regression (LR) models. The susceptibility maps were validated and compared using landslide locations in the validation data. The prediction performance of two susceptibility maps was estimated using the Receiver Operating Characteristics (ROC). The result shows that the area under the ROC curve (AUC) for the LR model (AUC = 0.817) is slightly higher than those obtained from the SI model (AUC = 0.801). Further, it is noted that the SI and LR models using the best subset outperform the models using the fifteen original factors. Therefore, we conclude that the optimized factor model using CF is more accurate in predicting landslide susceptibility and obtaining a more homogeneous classification map. Our findings acknowledge that in the mountainous regions suffering from data scarcity, it is possible to select key factors related to landslide occurrence based on the CF models in a GIS platform. Hence, the development of a scenario for future planning of risk mitigation is achieved in an efficient manner.
The objective of this study was to select the maximum number of correlated factors with landslide... more The objective of this study was to select the maximum number of correlated factors with landslide occurrence for slope-instability mapping and assess landslide susceptibility on Osado Island, Niigata Prefecture, Central Japan, integrating two techniques, namely certainty factor (CF) and artificial neural network (ANN), in a geographic information system (GIS) environment. The landslide inventory data of the National Research Institute for Earth Science and Disaster Prevention (NIED) and a 10-m digital elevation model (DEM) from the Geographical Survey of Institute, Japan, were analyzed. Our study identified fourteen possible landslide-conditioning factors. Considering the spatial autocorrelation and factor redundancy, we applied the CF approach to optimize these set of variables. We hypothesize that if the thematic factor layers of the CF values are positive, it implies that these conditioning factors have a correlation with the landslide occurrence. Therefore, based on this assumption and because of their positive CF values, six conditioning factors including slope angle (0.04), slope aspect (0.02), drainage density network (0.34), distance to the geologic boundaries (0.37), distance to fault (0.35), and lithology (0.31) have been selected as landslide-conditioning factors for further analysis. We partitioned the data into two groups: 70 % (520 landslide locations) for model training and the remaining 30 % (220 landslide locations) for validation. Then, a common ANN model, namely the back-propagation neural network (BPNN), was employed to produce the landslide susceptibility maps. The receiver operating characteristics including the area under the curve (AUC) were used to assess the model accuracy. The validation results indicate that the values of the AUC at optimized and non-optimized BPNN were 0.82 and 0.73, respectively. Hence, it is concluded that the optimized factor model can provide superior accuracy in the prediction of landslide susceptibility in the study area. In this context, we propose a method to select the factors with landslide occurrence. This work is fundamental for further study of the landslide susceptibility evaluation and prediction.
Sinkhole development is a typical geological disaster found in areas of carbonate
bedrock. Compar... more Sinkhole development is a typical geological disaster found in areas of carbonate bedrock. Compared with other geological disasters, sinkholes are considerably smaller and scattered according to scale and spatial distribution. Nevertheless, detecting and investigating sinkholes have become increasingly challenging. This study proposes a novel method by applying case-based reasoning (CBR) combined with object-based image analysis and genetic algorithms (GAs) to detect the sinkholes using high-resolution aerial images. This case study was performed in Paitan Town, Guangdong Province, China. The method comprises three major steps: (1) multi-image segmentation, (2) GA-based feature selection, and (3) application of CBR techniques. The detected sinkholes were categorized into three classes: buried, collapse type I, and collapse type II. The experiment demonstrated that the proposed method can obtain higher accuracy compared with the traditional supervised maximum likelihood classifier (MLC). The overall accuracy of CBR classification and MLC for the collapse area was 0.88 and 0.71, respectively. In addition, the kappa coefficient for CBR classification (0.81) was higher than that for MLC (0.5). A similar case library was also applied to another trial area for validation, the satisfactory results of which suggested that CBR is applicable for independently detecting sinkholes.
The uplift of the Arabian Shield and the opening of the Red Sea led to the development of steep d... more The uplift of the Arabian Shield and the opening of the Red Sea led to the development of steep drainage systems in the Western Arabian Peninsula. Although the Peninsula has been studied from a geological perspective, in relation to oil production, plate tectonics and eolian systems such as sand dunes, the steep mountainous drainage basins have received much less attention. This paper aims to assess the characteristics and development of 36 drainage basins in the Western Arabian Peninsula, using a digital elevation model (DEM), principal component analysis (PCA), and hierarchical cluster analysis (CA). Three major principal components (PC1 to PC3) are found to explain 73% of total variance. CA divided the basins into two or four groups. The division by CA strongly reflects PC1, showing that the two analyses give comparable results. PC1 strongly reflects basin dimensions and drainage texture, and their positive correlations indicate the significant effect of basin relief and slope on mass wasting and limited stream incision in small basins under an arid climate. PC2 mainly reflects the effect of bedrock geology, suggesting that volcanic rocks tend to produce more elongated and less eroded immature basins than crystalline rocks do. PC3 mainly reflects the basin relief and slope and the length of each stream segment, which may also reflect the effect of mass wasting on stream development.
The tsunami waves generated during the Sumatra-Andaman earthquake of 26 December 2004 devastated ... more The tsunami waves generated during the Sumatra-Andaman earthquake of 26 December 2004 devastated the coastal area along Trinkat Island, causing sudden changes to the morphology of the landforms. This study uses a series of satellite images to record the short-term morphological response and shoreline changes as well as the recovery of coastal land after its destruction. Results indicate that the island experienced substantial erosion and a significant reduction in land area. Shoreline erosion is more prevalent than accretion at an average linear regression rate of ∼−9 m per year between 2004 and 2013. The major morphological changes at Trinkat Island were observed in coastal inlets, beaches, and bay head-lands. Straight beaches had almost recovered eight years after the tsunami; however, erosion is continually observed in other areas. Our study will help understanding the response and recovery of shorelines in Indian Ocean regions after the 2004 tsunami.
International Journal of Environmental Research, May 27, 2013
This study presents an empirical relationship of suspended sediment concentrations (SSCs) in the ... more This study presents an empirical relationship of suspended sediment concentrations (SSCs) in the coastal waters, which is derived from the conventional methods, to radiometer remote sensing reflectance values (Rrs) and satellite data in coastal waters of Kerala. An algorithm is then developed to utilize both in situ SSCs measured from conventional methods and Rrs values. This algorithm is validated with the SSCs retrieved from Oceansat-2 Ocean Colour Monitor (OCM) data. A significant coefficient of determination (R2=0.62) is observed between the SSCs measured in situ and satellite derived SSCs. Reflectance values from more than two spectral wavelength bands are also employed to test the accuracy of results. Satellite derived SSCs range from 1- 40 mg/L in the coastal waters off Cochin, southwest India. The regional algorithm developed for the study area gives better results than Tassan’s algorithm, and this algorithm can be used in estimation of SSC for coastal waters of western India.
Sinkhole development is a typical geological disaster found in areas of carbonate bedrock. Compar... more Sinkhole development is a typical geological disaster found in areas of carbonate bedrock. Compared with other geological disasters, sinkholes are considerably smaller and scattered according to scale and spatial distribution. Nevertheless, detecting and investigating sinkholes have become increasingly challenging. This study proposes a novel method by applying case-based reasoning (CBR) combined with object-based image analysis and genetic algorithms (GAs) to detect the sinkholes using high-resolution aerial images. This case study was performed in Paitan Town, Guangdong Province, China. The method comprises three major steps: (1) multi-image segmentation, (2) GA-based feature selection, and (3) application of CBR techniques. The detected sinkholes were categorized into three classes: buried, collapse type I, and collapse type II. The experiment demonstrated that the proposed method can obtain higher accuracy compared with the traditional supervised maximum likelihood classifier (MLC). The overall accuracy of CBR classification and MLC for the collapse area was 0.88 and 0.71, respectively. In addition, the kappa coefficient for CBR classification (0.81) was higher than that for MLC (0.5). A similar case library was also applied to another trial area for validation, the satisfactory results of which suggested that CBR is applicable for independently detecting sinkholes.
This paper proposes an automatic method for detecting landslides by using an integrated approach ... more This paper proposes an automatic method for detecting landslides by using an integrated approach comprising object-oriented image analysis (OOIA), a genetic algorithm (GA), and a case-based reasoning (CBR) technique. It consists of three main phases: (1) image processing and multi-image segmentation; (2) feature optimization; and (3) detecting landslides. The proposed approach was employed in a fast-growing urban region, the Pearl River Delta in South China. The results of detection were validated with the help of field surveys. The experimental results indicated that the proposed OOIA-GA-CBR (0.87) demonstrates higher classification performance than the stand-alone OOIA (0.75) method for detecting landslides. The area under curve (AUC) value was also higher than that of the simple OOIA, indicating the high efficiency of the proposed landslide detection approach. The case library created using the integrated model can be reused for time-independent analysis, thus rendering our approach superior in comparison to other traditional methods, such as the maximum likelihood classifier. The results of this study thus facilitate fast generation of accurate landslide inventory maps, which will eventually extend our understanding of the evolution of landscapes shaped by landslide processes.
This paper assesses the potentiality of certainty factor models (CF) for the best suitable causat... more This paper assesses the potentiality of certainty factor models (CF) for the best suitable causative factors extraction for landslide susceptibility mapping in the Sado Island, Niigata Prefecture, Japan. To test the applicability of CF, a landslide inventory map provided by National Research Institute for Earth Science and Disaster Prevention (NIED) was split into two subsets: (i) 70% of the landslides in the inventory to be used for building the CF based model; (ii) 30% of the landslides to be used for the validation purpose. A spatial database with fifteen landslide causative factors was then constructed by processing ALOS satellite images, aerial photos, topographical and geological maps. CF model was then applied to select the best subset from the fifteen factors. Using all fifteen factors and the best subset factors, landslide susceptibility maps were produced using statistical index (SI) and logistic regression (LR) models. The susceptibility maps were validated and compared using landslide locations in the validation data. The prediction performance of two susceptibility maps was estimated using the Receiver Operating Characteristics (ROC). The result shows that the area under the ROC curve (AUC) for the LR model (AUC = 0.817) is slightly higher than those obtained from the SI model (AUC = 0.801). Further, it is noted that the SI and LR models using the best subset outperform the models using the fifteen original factors. Therefore, we conclude that the optimized factor model using CF is more accurate in predicting landslide susceptibility and obtaining a more homogeneous classification map. Our findings acknowledge that in the mountainous regions suffering from data scarcity, it is possible to select key factors related to landslide occurrence based on the CF models in a GIS platform. Hence, the development of a scenario for future planning of risk mitigation is achieved in an efficient manner.
The objective of this study was to select the maximum number of correlated factors with landslide... more The objective of this study was to select the maximum number of correlated factors with landslide occurrence for slope-instability mapping and assess landslide susceptibility on Osado Island, Niigata Prefecture, Central Japan, integrating two techniques, namely certainty factor (CF) and artificial neural network (ANN), in a geographic information system (GIS) environment. The landslide inventory data of the National Research Institute for Earth Science and Disaster Prevention (NIED) and a 10-m digital elevation model (DEM) from the Geographical Survey of Institute, Japan, were analyzed. Our study identified fourteen possible landslide-conditioning factors. Considering the spatial autocorrelation and factor redundancy, we applied the CF approach to optimize these set of variables. We hypothesize that if the thematic factor layers of the CF values are positive, it implies that these conditioning factors have a correlation with the landslide occurrence. Therefore, based on this assumption and because of their positive CF values, six conditioning factors including slope angle (0.04), slope aspect (0.02), drainage density network (0.34), distance to the geologic boundaries (0.37), distance to fault (0.35), and lithology (0.31) have been selected as landslide-conditioning factors for further analysis. We partitioned the data into two groups: 70 % (520 landslide locations) for model training and the remaining 30 % (220 landslide locations) for validation. Then, a common ANN model, namely the back-propagation neural network (BPNN), was employed to produce the landslide susceptibility maps. The receiver operating characteristics including the area under the curve (AUC) were used to assess the model accuracy. The validation results indicate that the values of the AUC at optimized and non-optimized BPNN were 0.82 and 0.73, respectively. Hence, it is concluded that the optimized factor model can provide superior accuracy in the prediction of landslide susceptibility in the study area. In this context, we propose a method to select the factors with landslide occurrence. This work is fundamental for further study of the landslide susceptibility evaluation and prediction.
Sinkhole development is a typical geological disaster found in areas of carbonate
bedrock. Compar... more Sinkhole development is a typical geological disaster found in areas of carbonate bedrock. Compared with other geological disasters, sinkholes are considerably smaller and scattered according to scale and spatial distribution. Nevertheless, detecting and investigating sinkholes have become increasingly challenging. This study proposes a novel method by applying case-based reasoning (CBR) combined with object-based image analysis and genetic algorithms (GAs) to detect the sinkholes using high-resolution aerial images. This case study was performed in Paitan Town, Guangdong Province, China. The method comprises three major steps: (1) multi-image segmentation, (2) GA-based feature selection, and (3) application of CBR techniques. The detected sinkholes were categorized into three classes: buried, collapse type I, and collapse type II. The experiment demonstrated that the proposed method can obtain higher accuracy compared with the traditional supervised maximum likelihood classifier (MLC). The overall accuracy of CBR classification and MLC for the collapse area was 0.88 and 0.71, respectively. In addition, the kappa coefficient for CBR classification (0.81) was higher than that for MLC (0.5). A similar case library was also applied to another trial area for validation, the satisfactory results of which suggested that CBR is applicable for independently detecting sinkholes.
The uplift of the Arabian Shield and the opening of the Red Sea led to the development of steep d... more The uplift of the Arabian Shield and the opening of the Red Sea led to the development of steep drainage systems in the Western Arabian Peninsula. Although the Peninsula has been studied from a geological perspective, in relation to oil production, plate tectonics and eolian systems such as sand dunes, the steep mountainous drainage basins have received much less attention. This paper aims to assess the characteristics and development of 36 drainage basins in the Western Arabian Peninsula, using a digital elevation model (DEM), principal component analysis (PCA), and hierarchical cluster analysis (CA). Three major principal components (PC1 to PC3) are found to explain 73% of total variance. CA divided the basins into two or four groups. The division by CA strongly reflects PC1, showing that the two analyses give comparable results. PC1 strongly reflects basin dimensions and drainage texture, and their positive correlations indicate the significant effect of basin relief and slope on mass wasting and limited stream incision in small basins under an arid climate. PC2 mainly reflects the effect of bedrock geology, suggesting that volcanic rocks tend to produce more elongated and less eroded immature basins than crystalline rocks do. PC3 mainly reflects the basin relief and slope and the length of each stream segment, which may also reflect the effect of mass wasting on stream development.
The tsunami waves generated during the Sumatra-Andaman earthquake of 26 December 2004 devastated ... more The tsunami waves generated during the Sumatra-Andaman earthquake of 26 December 2004 devastated the coastal area along Trinkat Island, causing sudden changes to the morphology of the landforms. This study uses a series of satellite images to record the short-term morphological response and shoreline changes as well as the recovery of coastal land after its destruction. Results indicate that the island experienced substantial erosion and a significant reduction in land area. Shoreline erosion is more prevalent than accretion at an average linear regression rate of ∼−9 m per year between 2004 and 2013. The major morphological changes at Trinkat Island were observed in coastal inlets, beaches, and bay head-lands. Straight beaches had almost recovered eight years after the tsunami; however, erosion is continually observed in other areas. Our study will help understanding the response and recovery of shorelines in Indian Ocean regions after the 2004 tsunami.
International Journal of Environmental Research, May 27, 2013
This study presents an empirical relationship of suspended sediment concentrations (SSCs) in the ... more This study presents an empirical relationship of suspended sediment concentrations (SSCs) in the coastal waters, which is derived from the conventional methods, to radiometer remote sensing reflectance values (Rrs) and satellite data in coastal waters of Kerala. An algorithm is then developed to utilize both in situ SSCs measured from conventional methods and Rrs values. This algorithm is validated with the SSCs retrieved from Oceansat-2 Ocean Colour Monitor (OCM) data. A significant coefficient of determination (R2=0.62) is observed between the SSCs measured in situ and satellite derived SSCs. Reflectance values from more than two spectral wavelength bands are also employed to test the accuracy of results. Satellite derived SSCs range from 1- 40 mg/L in the coastal waters off Cochin, southwest India. The regional algorithm developed for the study area gives better results than Tassan’s algorithm, and this algorithm can be used in estimation of SSC for coastal waters of western India.
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Papers by Yunus Ali
Prefecture, Japan. To test the applicability of CF, a landslide inventory map provided by National Research Institute for Earth Science and Disaster Prevention (NIED) was split into
two subsets: (i) 70% of the landslides in the inventory to be used for building the CF based model; (ii) 30% of the landslides to be used for the validation purpose. A spatial database
with fifteen landslide causative factors was then constructed by processing ALOS satellite images, aerial photos, topographical and geological maps. CF model was then applied to select the best subset from the fifteen factors. Using all fifteen factors and the best subset factors, landslide susceptibility maps were produced using statistical index (SI) and logistic regression (LR) models. The susceptibility maps were validated and compared using landslide locations in the validation data. The prediction performance of two susceptibility maps was estimated using the Receiver Operating Characteristics (ROC). The result shows that the area under the ROC curve (AUC) for the LR model (AUC = 0.817) is slightly higher than those obtained from the SI model (AUC = 0.801). Further, it is noted that the SI and LR models
using the best subset outperform the models using the fifteen original factors. Therefore, we conclude that the optimized factor model using CF is more accurate in predicting landslide
susceptibility and obtaining a more homogeneous classification map. Our findings acknowledge that in the mountainous regions suffering from data scarcity, it is possible to
select key factors related to landslide occurrence based on the CF models in a GIS platform. Hence, the development of a scenario for future planning of risk mitigation is achieved in an
efficient manner.
on Osado Island, Niigata Prefecture, Central Japan, integrating two techniques, namely certainty factor (CF) and artificial neural network (ANN), in a geographic information system (GIS) environment. The landslide inventory data of the National Research Institute for Earth Science and Disaster Prevention (NIED) and a 10-m digital elevation model (DEM) from the Geographical Survey of Institute, Japan, were analyzed. Our study identified fourteen possible landslide-conditioning factors. Considering the spatial autocorrelation and factor redundancy, we applied the CF approach to optimize these set of variables. We hypothesize that if the thematic factor layers of the CF values are positive, it implies that these conditioning factors have a correlation with the landslide occurrence.
Therefore, based on this assumption and because of their positive CF values, six conditioning factors including slope angle (0.04), slope aspect (0.02), drainage density network
(0.34), distance to the geologic boundaries (0.37), distance to fault (0.35), and lithology (0.31) have been selected as landslide-conditioning factors for further analysis. We partitioned
the data into two groups: 70 % (520 landslide locations) for model training and the remaining 30 % (220 landslide locations) for validation. Then, a common ANN model, namely the back-propagation neural network (BPNN), was employed to produce the landslide susceptibility maps. The receiver operating characteristics including the area under the curve (AUC) were used to assess the model accuracy. The validation results
indicate that the values of the AUC at optimized and non-optimized BPNN were 0.82 and 0.73, respectively. Hence, it is concluded that the optimized factor model can provide
superior accuracy in the prediction of landslide susceptibility in the study area. In this context, we propose a method to select the factors with landslide occurrence. This work is
fundamental for further study of the landslide susceptibility evaluation and prediction.
bedrock. Compared with other geological disasters, sinkholes are considerably
smaller and scattered according to scale and spatial distribution. Nevertheless, detecting
and investigating sinkholes have become increasingly challenging. This study proposes a
novel method by applying case-based reasoning (CBR) combined with object-based image
analysis and genetic algorithms (GAs) to detect the sinkholes using high-resolution aerial
images. This case study was performed in Paitan Town, Guangdong Province, China. The
method comprises three major steps: (1) multi-image segmentation, (2) GA-based feature
selection, and (3) application of CBR techniques. The detected sinkholes were categorized
into three classes: buried, collapse type I, and collapse type II. The experiment demonstrated
that the proposed method can obtain higher accuracy compared with the traditional
supervised maximum likelihood classifier (MLC). The overall accuracy of CBR classification
and MLC for the collapse area was 0.88 and 0.71, respectively. In addition, the
kappa coefficient for CBR classification (0.81) was higher than that for MLC (0.5). A
similar case library was also applied to another trial area for validation, the satisfactory
results of which suggested that CBR is applicable for independently detecting sinkholes.
Prefecture, Japan. To test the applicability of CF, a landslide inventory map provided by National Research Institute for Earth Science and Disaster Prevention (NIED) was split into
two subsets: (i) 70% of the landslides in the inventory to be used for building the CF based model; (ii) 30% of the landslides to be used for the validation purpose. A spatial database
with fifteen landslide causative factors was then constructed by processing ALOS satellite images, aerial photos, topographical and geological maps. CF model was then applied to select the best subset from the fifteen factors. Using all fifteen factors and the best subset factors, landslide susceptibility maps were produced using statistical index (SI) and logistic regression (LR) models. The susceptibility maps were validated and compared using landslide locations in the validation data. The prediction performance of two susceptibility maps was estimated using the Receiver Operating Characteristics (ROC). The result shows that the area under the ROC curve (AUC) for the LR model (AUC = 0.817) is slightly higher than those obtained from the SI model (AUC = 0.801). Further, it is noted that the SI and LR models
using the best subset outperform the models using the fifteen original factors. Therefore, we conclude that the optimized factor model using CF is more accurate in predicting landslide
susceptibility and obtaining a more homogeneous classification map. Our findings acknowledge that in the mountainous regions suffering from data scarcity, it is possible to
select key factors related to landslide occurrence based on the CF models in a GIS platform. Hence, the development of a scenario for future planning of risk mitigation is achieved in an
efficient manner.
on Osado Island, Niigata Prefecture, Central Japan, integrating two techniques, namely certainty factor (CF) and artificial neural network (ANN), in a geographic information system (GIS) environment. The landslide inventory data of the National Research Institute for Earth Science and Disaster Prevention (NIED) and a 10-m digital elevation model (DEM) from the Geographical Survey of Institute, Japan, were analyzed. Our study identified fourteen possible landslide-conditioning factors. Considering the spatial autocorrelation and factor redundancy, we applied the CF approach to optimize these set of variables. We hypothesize that if the thematic factor layers of the CF values are positive, it implies that these conditioning factors have a correlation with the landslide occurrence.
Therefore, based on this assumption and because of their positive CF values, six conditioning factors including slope angle (0.04), slope aspect (0.02), drainage density network
(0.34), distance to the geologic boundaries (0.37), distance to fault (0.35), and lithology (0.31) have been selected as landslide-conditioning factors for further analysis. We partitioned
the data into two groups: 70 % (520 landslide locations) for model training and the remaining 30 % (220 landslide locations) for validation. Then, a common ANN model, namely the back-propagation neural network (BPNN), was employed to produce the landslide susceptibility maps. The receiver operating characteristics including the area under the curve (AUC) were used to assess the model accuracy. The validation results
indicate that the values of the AUC at optimized and non-optimized BPNN were 0.82 and 0.73, respectively. Hence, it is concluded that the optimized factor model can provide
superior accuracy in the prediction of landslide susceptibility in the study area. In this context, we propose a method to select the factors with landslide occurrence. This work is
fundamental for further study of the landslide susceptibility evaluation and prediction.
bedrock. Compared with other geological disasters, sinkholes are considerably
smaller and scattered according to scale and spatial distribution. Nevertheless, detecting
and investigating sinkholes have become increasingly challenging. This study proposes a
novel method by applying case-based reasoning (CBR) combined with object-based image
analysis and genetic algorithms (GAs) to detect the sinkholes using high-resolution aerial
images. This case study was performed in Paitan Town, Guangdong Province, China. The
method comprises three major steps: (1) multi-image segmentation, (2) GA-based feature
selection, and (3) application of CBR techniques. The detected sinkholes were categorized
into three classes: buried, collapse type I, and collapse type II. The experiment demonstrated
that the proposed method can obtain higher accuracy compared with the traditional
supervised maximum likelihood classifier (MLC). The overall accuracy of CBR classification
and MLC for the collapse area was 0.88 and 0.71, respectively. In addition, the
kappa coefficient for CBR classification (0.81) was higher than that for MLC (0.5). A
similar case library was also applied to another trial area for validation, the satisfactory
results of which suggested that CBR is applicable for independently detecting sinkholes.