A novel integrated approach of relevance vector machine optimized by imperialist competitive algorithm for spatial modeling of shallow landslides

This research aims at proposing a new artificial intelligence approach (namely RVM-ICA) which is based on the Relevance Vector Machine (RVM) and the Imperialist Competitive Algorithm (ICA) optimization for landslide susceptibility modeling. A Geographic Information System (GIS) spatial database was generated from Lang Son city in Lang Son province (Vietnam). This GIS database includes a landslide inventory map and fourteen landslide conditioning factors. The suitability of these factors for landslide susceptibility modeling in the study area was verified by the Information Gain Ratio (IGR) technique. A landslide susceptibility prediction model based on RVM-ICA and the GIS database was established by training and prediction phases. The predictive capability of the new approach was evaluated by calculations of sensitivity, specificity, accuracy, and the area under the Receiver Operating Characteristic curve (AUC). In addition, to assess the applicability of the proposed model, two state-of-the-art soft computing techniques including the support vector machine (SVM) and logistic regression (LR) were used as benchmark methods. The results of this study show that RVM-ICA with AUC = 0.92 achieved a high goodness-of-fit based on both the training and testing datasets. The predictive capability of RVM-ICA outperformed those of SVM with AUC = 0.91 and LR with AUC = 0.87. The experimental results confirm that the newly proposed model is a very promising alternative to assist planners and decision makers in the task of managing landslide prone areas

Predicting uncertainty of machine learning models for modelling nitrate pollution of groundwater using quantile regression and UNNEC methods

Although estimating the uncertainty of models used for modelling nitrate contamination of groundwater is essential in groundwater management, it has been generally ignored. This issue motivates this research to explore the predictive uncertainty of machine-learning (ML) models in this field of study using two different residuals uncertainty methods: quantile regression (QR) and uncertainty estimation based on local errors and clustering (UNEEC). Prediction-interval coverage probability (PICP), the most important of the statistical measures of uncertainty, was used to evaluate uncertainty. Additionally, three state-of-the-art ML models including support vector machine (SVM), random forest (RF), and k-nearest neighbor (kNN) were selected to spatially model groundwater nitrate concentrations. The models were calibrated with nitrate concentrations from 80 wells (70% of the data) and then validated with nitrate concentrations from 34 wells (30% of the data). Both uncertainty and predictive performance criteria should be considered when comparing and selecting the best model. Results highlight that the kNN model is the best model because not only did it have the lowest uncertainty based on the PICP statistic in both the QR (0.94) and the UNEEC (in all clusters, 0.85–0.91) methods, but it also had predictive performance statistics (RMSE = 10.63, R2 = 0.71) that were relatively similar to RF (RMSE = 10.41, R2 = 0.72) and higher than SVM (RMSE = 13.28, R2 = 0.58). Determining the uncertainty of ML models used for spatially modelling groundwater-nitrate pollution enables managers to achieve better risk-based decision making and consequently increases the reliability and credibility of groundwater-nitrate predictions

Application of GIS and remote sensing techniques in assessment of natural hazards in the Central Zab Basin, Northwest of Iran

This research was based on a complete understanding of the central Zab basin (particularly in the neighboring Sardasht county in west Azerbaijan in northwest of Iran) and identify bottlenecks and instability, natural environmental hazards are identified and appropriate strategies should be presented in order to confront and control them. This study demonstrates the synergistic use of medium resolution of SPOT-5 Satellite, for prepare of landslide-inventory map and Landsat ETM+ satellite for prepare of Land use map. After making of TIN and DEM data from the limit of study area from topography maps, aerial photos and satellite images, and have been used GIS techniques and analysis of relevant factors. Methods In this study, based on field studies, library, quantitative and morphometric study was to prepare maps and GIS techniques and analysis of relevant factors have been used. The results indicate a dominance of geomorphologic natural hazards and human hazards. As a result, using the logical and scientific approaches can greatly reduce the morphodynamics factors and make balance between Morphogenesis and pedogenic phenomena and can be achieved stable environment with crisis management

Landslide susceptibility mapping using image satellite and GIS technology

Landslides are among the great destructive factors which cause lots of fatalities and financial losses all over the world every year. The aim of the research was landslide susceptibility mapping by remote sensing data processing and GIS spatial analysis. The area study in research is central Zab basin in west Azerbaijan province, Iran. In this research, through geological maps and field studies,we primarily prepared a map for landslide distributions in Zab basin. Then, applying other information sources such as the existing thematic maps, we studied and defined the 8 factors such as, lithology, slope, slope aspect, annual rainfall, land use, distance to waterway, distance to the fault, and distance to road. That affect occurrence of the landslides. To get more precision,speed and facility in our analysis all descriptive and spatial information was entered into GIS system. After preparation of the needed information layers by influential parameters on landslides, we drew the zoning maps of landslide hazard via information coming from satellite image classification (Quickbird, Ikonos), and then evaluated and compared them. According to the obtained index, and the comparison of landslide distribution map and zoning map of landslide hazard prepared by each of the methods in GIS environment. This model gives also indications about the relevant factors influencing slope instability

Landslide susceptibility mapping in central Zab basin in GISs-based models, Northwest of Iran

There are several practical methods in landslide susceptibility of which the logistic regression is used as the statistical model in central Zab basin in the southwest mountainsides of West-Azerbaijan province in Iran to predict landslide susceptibility with two independent and dependant variables. This part of Zab basin is landslide-prone given its geological structure and geomorphology. We studied and defined the factors (slope, aspect, elevation, distance to road, distance to drainage network, and distance to fault, land use, precipitation, and geological factors) that affect occurrence of the landslides. To get more precision, speed and facility in our analysis, all descriptive and spatial information was entered into GIS system. The applied statistical approach is appropriate to landslide prediction. It employs the landslide events as dependant variable and data layers as independent variable, and makes use of the correlation between these two factors in landslide susceptibility. Given the employed model and the variables, signification tests were implemented on each independent variable, and the degree of fitness of susceptibility mapping was estimated; finally the map was classified into five categories: very low, low, moderate, high and very high risk. The categories cover an area of 95.46km2, 100.46km2, 46.1km2, 158.38km2 and 120.96km2, respectively

Landslide susceptibility mapping using GIS-based statistical models and Remote sensing data in tropical environment

This research presents the results of the GIS-based statistical models for generation of landslide susceptibility mapping using geographic information system (GIS) and remote-sensing data for Cameron Highlands area in Malaysia. Ten factors including slope, aspect, soil, lithology, NDVI, land cover, distance to drainage, precipitation, distance to fault, and distance to road were extracted from SAR data, SPOT 5 and WorldView-1 images. The relationships between the detected landslide locations and these ten related factors were identified by using GIS-based statistical models including analytical hierarchy process (AHP), weighted linear combination (WLC) and spatial multi-criteria evaluation (SMCE) models. The landslide inventory map which has a total of 92 landslide locations was created based on numerous resources such as digital aerial photographs, AIRSAR data, WorldView-1 images, and field surveys. Then, 80% of the landslide inventory was used for training the statistical models and the remaining 20% was used for validation purpose. The validation results using the Relative landslide density index (R-index) and Receiver operating characteristic (ROC) demonstrated that the SMCE model (accuracy is 96%) is better in prediction than AHP (accuracy is 91%) and WLC (accuracy is 89%) models. These landslide susceptibility maps would be useful for hazard mitigation purpose and regional plannin

Application of Geographical Information system (GIS) in urban water of Amol in Iran at time of natural disaster

For the management of urban disaster risk, periodic updating of geo- databases of urban water is crucial, particularly in developing countries where urbanisation rates are very high. However, collecting information on the characteristics of buildings and lifelines through full ground surveys can be very costly and time-consuming. this article has done operationally in Amol city which is located in Mazandaran Province of Iran and it tries to represent by using rules and data of collected from different maps, urban designing and capabilities of Geographical Information system (GIS) in urban water management at the time of natural disasters. Structure of this article is like that in first we established a comprehensive data base related to water utilities by collecting, entering, saving and data management, then by modeling water utilities we had practically considered its operational aspects related to water utilities problems in urban regions

Land cover mapping using a novel combination model of satellite imageries: case study of a part of the Cameron Highlands, Pahang, Malaysia

Information about land covers is essential for a variety of purposes, such as environmental studies, sustainable development, and regional managements. This study aims to use a novel combination model to generate a land cover map in a part of the Cameron Highlands, Malaysia, where there are different kind of land covers, including tea plantation, florification and forest. Because of the high similarity in land covers of the study area, only through satellite imageries with a high spatial and spectral resolution the land covers can be differentiated. We have combined satellite imageries of Sentinel-1 (S1A, GRD, IW) and Landsat-8 (Operational land imager) for the year 2017 as well as different algorithms of Maximum Likelihood (ML), Minimum Distance (MD), Support Vector Machine (SVM), Spectral Angle Mapper (SAM) and Artificial Neural Network (ANN). The results showed that the combination model is an applicable technique for extracting land covers in areas with high similarities in land covers. The overall accuracy of the confusion matrix and the Kappa Coefficient are 98.1984% and 0.9579, respectively, which indicate that it is a robust model for extracting land covers in areas like the Cameron Highlands. The obtained results can be useful for different purposes, including urban and environmental management, change detection, agriculture and many more purposes