Inge Revhaug

Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS

The objective of this study is to investigate a potential application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Geographic Information System (GIS) as a relatively new approach for landslide susceptibility mapping in the Hoa Binh province of Vietnam. Firstly, a landslide inventory map with a total of 118 landslide locations was constructed from various sources. Then the landslide inventory was randomly split into a testing dataset 70% (82 landslide locations) for training the models and the remaining 30% (36 landslides locations) was used for validation purpose. Ten landslide conditioning factors such as slope, aspect, curvature, lithology, land use, soil type, rainfall, distance to roads, distance to rivers, and distance to faults were considered in the analysis. The hybrid learning algorithm and six different membership functions (Gaussmf, Gauss2mf, Gbellmf, Sigmf, Dsigmf, Psigmf) were applied to generate the landslide susceptibility maps. The validation dataset, which was not considered in the ANFIS modeling process, was used to validate the landslide susceptibility maps using the prediction rate method. The validation results showed that the area under the curve (AUC) for six ANFIS models vary from 0.739 to 0.848. It indicates that the prediction capability depends on the membership functions used in the ANFIS. The models with Sigmf (0.848) and Gaussmf (0.825) have shown the highest prediction capability. The results of this study show that landslide susceptibility mapping in the Hoa Binh province of Vietnam using the ANFIS approach is viable. As far as the performance of the ANFIS approach is concerned, the results appeared to be quite satisfactory, the zones determined on the map being zones of relative susceptibility.

Landslide Susceptibility Assessment in Vietnam Using Support Vector Machines, Decision Tree, and Naïve Bayes Models

The objective of this study is to investigate and compare the results of three data mining approaches, the support vector machines (SVM), decision tree (DT), and Naive Bayes (NB) models for spatial prediction of landslide hazards in the Hoa Binh province (Vietnam). First, a landslide inventory map showing the locations of 118 landslides was constructed from various sources. The landslide inventory was then randomly partitioned into 70% for training the models and 30% for the model validation. Second, ten landslide conditioning factors were selected (i.e., slope angle, slope aspect, relief amplitude, lithology, soil type, land use, distance to roads, distance to rivers, distance to faults, and rainfall). Using these factors, landslide susceptibility indexes were calculated using SVM, DT, and NB models. Finally, landslide locations that were not used in the training phase were used to validate and compare the landslide susceptibility maps. The validation results show that the models derived using SVM have the highest prediction capability. The model derived using DT has the lowest prediction capability. Compared to the logistic regression model, the prediction capability of the SVM models is slightly better. The prediction capability of the DT and NB models is lower.

Optimization of causative factors for landslide susceptibility evaluation using remote sensing and GIS data in parts of Niigata, Japan

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.

A novel hybrid evidential belief function-based fuzzy logic model in spatial prediction of rainfall-induced shallow landslides in the Lang Son city area (Vietnam)

The main objective of this study is to investigate potential application of an integrated evidential belief function (EBF)-based fuzzy logic model for spatial prediction of rainfall-induced shallow landslides in the Lang Son city area (Vietnam). First, a landslide inventory map was constructed from various sources. Then the landslide inventory map was randomly partitioned as a ratio of 70/30 for training and validation of the models, respectively. Second, six landslide conditioning factors (slope angle, slope aspect, lithology, distance to faults, soil type, land use) were prepared and fuzzy membership values for these factors classes were estimated using the EBF. Subsequently, fuzzy operators were used to generate landslide susceptibility maps. Finally, the susceptibility maps were validated and compared using the validation dataset. The results show that the lowest prediction capability is the fuzzy SUM (76.6%). The prediction capability is almost the same for the fuzzy PRODUCT and fuzzy GAMMA models (79.6%). Compared to the frequency-ratio based fuzzy logic models, the EBF-based fuzzy logic models showed better result in both the success rate and prediction rate. The results from this study may be useful for local planner in areas prone to landslides. The modelling approach can be applied for other areas.

A Comparative Assessment Between the Application of Fuzzy Unordered Rules Induction Algorithm and J48 Decision Tree Models in Spatial Prediction of Shallow Landslides at Lang Son City, Vietnam

The main objective of this study is to investigate potential application of the Fuzzy Unordered Rules Induction Algorithm (FURIA) and the Bagging (an ensemble technique) in comparison with Decision Tree model for spatial prediction of shallow landslides in the Lang Son city area (Vietnam). First, a landslide inventory map was constructed from various sources. Then, the landslide inventory was randomly partitioned into 70 % for training the models and 30 % for the model validation. Second, six landslide conditioning factors (slope, aspect, lithology, land use, soil type, and distance to faults) were prepared. Using these factors and the training dataset, landslide susceptibility indexes were calculated using the FURIA, the FURIA with Bagging, the Decision Tree, and the Decision Tree with Bagging. Finally, prediction performances of these susceptibility maps were carried out using the Receiver Operating Characteristic (ROC) technique. The results show that area under the ROC curve (AUC) using training dataset has the largest for the Decision Tree with Bagging (0.925) and the FURIA with Bagging (0.913), followed by the Decision Tree (0.908) and the FURIA (0.878). The prediction capability of these models was estimated using the validation dataset. The highest prediction was achieved using the FURIA with Bagging (AUC = 0.802), followed by the Decision Tree (AUC = 0.783), the Decision Tree with Bagging (AUC = 0.777), and the FURIA (AUC = 0.773). We conclude that the FURIA with Bagging is the best model in this study.

Landslide Susceptibility Mapping Along the National Road 32 of Vietnam Using GIS-Based J48 Decision Tree Classifier and Its Ensembles

The main objective of this study is to compare the results of decision tree classifier and its ensembles for landslide susceptibility assessment along the National Road 32 of Vietnam. First, a landslide inventory map with 262 landslide locations was constructed using data from various sources that accounts for landslides that occurred during the last 20 years. Second, ten landslide conditioning factors (slope, aspect, relief amplitude, topographic wetness index, toposhape, distance to roads, distance to rivers, distance to faults, lithology, and rainfall) were prepared. Third, using decision tree and two ensemble techniques i.e. Bagging and AdaBoost, landslide susceptibility maps were constructed. Finally, the resultant landslide susceptibility maps were validated and compared using a validation dataset not used during the model building. The results show that the decision tree with Bagging ensemble technique have the highest prediction capability (90.6 %), followed by the decision tree (87.8 %) and the decision tree with AdaBoost (86.2 %).

Tropical Forest Fire Susceptibility Mapping at the Cat Ba National Park Area, Hai Phong City, Vietnam, Using GIS-Based Kernel Logistic Regression

The Cat Ba National Park area (Vietnam) with its tropical forest is recognized as being part of the world biodiversity conservation by the United Nations Educational, Scientific and Cultural Organization (UNESCO) and is a well-known destination for tourists, with around 500,000 travelers per year. This area has been the site for many research projects; however, no project has been carried out for forest fire susceptibility assessment. Thus, protection of the forest including fire prevention is one of the main concerns of the local authorities. This work aims to produce a tropical forest fire susceptibility map for the Cat Ba National Park area, which may be helpful for the local authorities in forest fire protection management. To obtain this purpose, first, historical forest fires and related factors were collected from various sources to construct a GIS database. Then, a forest fire susceptibility model was developed using Kernel logistic regression. The quality of the model was assessed using the Receiver Operating Characteristic (ROC) curve, area under the ROC curve (AUC), and five statistical evaluation measures. The usability of the resulting model is further compared with a benchmark model, the support vector machine (SVM). The results show that the Kernel logistic regression model has a high level of performance in both the training and validation dataset, with a prediction capability of 92.2%. Since the Kernel logistic regression model outperforms the benchmark model, we conclude that the proposed model is a promising alternative tool that should also be considered for forest fire susceptibility mapping in other areas. The results of this study are useful for the local authorities in forest planning and management.

A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran

Floods are one of the most damaging natural hazards causing huge loss of property, infrastructure and lives. Prediction of occurrence of flash flood locations is very difficult due to sudden change in climatic condition and manmade factors. However, prior identification of flood susceptible areas can be done with the help of machine learning techniques for proper timely management of flood hazards. In this study, we tested four decision trees based machine learning models namely Logistic Model Trees (LMT), Reduced Error Pruning Trees (REPT), Naïve Bayes Trees (NBT), and Alternating Decision Trees (ADT) for flash flood susceptibility mapping at the Haraz Watershed in the northern part of Iran. For this, a spatial database was constructed with 201 present and past flood locations and eleven flood-influencing factors namely ground slope, altitude, curvature, Stream Power Index (SPI), Topographic Wetness Index (TWI), land use, rainfall, river density, distance from river, lithology, and Normalized Difference Vegetation Index (NDVI). Statistical evaluation measures, the Receiver Operating Characteristic (ROC) curve, and Freidman and Wilcoxon signed-rank tests were used to validate and compare the prediction capability of the models. Results show that the ADT model has the highest prediction capability for flash flood susceptibility assessment, followed by the NBT, the LMT, and the REPT, respectively. These techniques have proven successful in quickly determining flood susceptible areas.

Spatial Prediction of Landslide Hazard at the Yihuang Area (China): A Comparative Study on the Predictive Ability of Backpropagation Multi-layer Perceptron Neural Networks and Radial Basic Function Neural Networks

The aim of this study is to investigate potential applications of multi-layer perceptron neural networks (MLP Neural Nets) and radial basis function neural networks (RBF Neural Nets) for landslide susceptibility mapping in the Yihuang area (China). First, a landslide inventory map with 187 landslide locations was generated, and then the map was randomly partitioned into a ratio of 70/30 for training and validating models. Second, 14 landslide conditioning factors (slope, altitude, aspect, topographic wetness, sediment transport index (STI), stream power index (SPI), plan curvature, distance to rivers, distance to faults, distance to roads, land use, normalized difference vegetation index (NDVI), lithology, rainfall) were prepared. Using MLP Neural Nets and RBF Neural Nets, two landslide susceptibility models were constructed and two landslide susceptibility maps were generated. Finally, the two resulting landslide susceptibility maps were validated using the landslide locations and the receiver operating characteristic (ROC) method. The validation results showed that the areas under the ROC curve (AUC) for the two landslide models produced by MLP Neural Nets and RBF Neural Nets are 0.932 and 0.765 for success rate curve and 0.757 and 0.725 for prediction rate curve, respectively. The results showed that the MLP Neural Nets model is better than the RBF Neural Nets model in this study. The results may be useful for general land use planning and hazard mitigation purposes.

iGeoTrans – a novel iOS application for GPS positioning in geosciences

The iGeoTrans is an iOS application designed for navigation purposes for iPhone, iPad and iPod Touch. This application uses Global Positioning System (GPS), Assisted GPS system, GLONASS, Wi-Fi and Cellular Network for positioning. The iGeoTrans has included datum transformations and map projections that enable users to convert the collected data between different coordinate systems for almost all areas in the world. In addition, other features are also included such as distance, area measurements, and GPS results could be exported to the AutoCad dxf format for GIS softwares. The average horizontal and vertical root mean square errors (RMSEs) for the static test are around 4.11 and 3.51 m, respectively. The horizontal RMSE for the dynamic outdoor test is around 2.72 m. The iGeoTrans application can be used to support surveying, mapping and geosciences fieldworks for any area in the world.