An assessment of the use of an advanced neural network model with five different training strategies for the preparation of landslide susceptibility maps.

Data collection for landslide susceptibility modelling is often an almost inhibitive activity. This has been the reason for quite sometimes landslide was described and modelled on the basis of spatially distributed values of landslide related attributes. This paper presents landslide susceptibility analysis at Selangor area, Malaysia, using artificial neural network model with the aid of remote sensing data and geographic information system (GIS) tools. To meet the objectives, landslide locations were identified in the study area from interpretation of aerial photographs and supported with extensive field surveys. Then, the landslide inventory was grouped into two categories: (1) training data (2) testing data. Further, topographical, geological data and satellite images were collected, processed, and constructed into a spatial database using GIS tools and image processing techniques. Nine landslide occurrence attributes were selected and analyzed using an artificial neural network model to generate the landslide susceptibility maps. Landslide location data (training data) were used for training the neural network and five training sites were selected randomly in this case. The use of five training sites ensemble to investigate the model reliability, including the role of the thematic variables used to construct the model, and the model sensitivity to changes in the selection of the training sites. By studying the variation of the neural network’s susceptibility estimate, the error associated with the model is determined. The results of the neural network analysis are shown on five sets of landslide susceptibility maps. Then the susceptibility maps were validated using ”receiver operating characteristics (ROC)” method as a measure for the model verification. Landslide training data which were not used during the training of the neural network was used for the verification of the maps. The results of the analysis were verified using the landslide location data and compared between five different cases. Qualitatively, the model seems to give reasonable results with accuracy observed was 87%, 83%, 85%, 86% and 82% for five different training sites respectively

A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS.

he purpose of the present study is to compare the prediction performances of three different approaches such as decision tree (DT), support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS) for landslide susceptibility mapping at Penang Hill area, Malaysia. The necessary input parameters for the landslide susceptibility assessments were obtained from various sources. At first, landslide locations were identified by aerial photographs and field surveys and a total of 113 landslide locations were constructed. The study area contains 340,608 pixels while total 8403 pixels include landslides. The landslide inventory was randomly partitioned into two subsets: (1) part 1 that contains 50% (4000 landslide grid cells) was used in the training phase of the models; (2) part 2 is a validation dataset 50% (4000 landslide grid cells) for validation of three models and to confirm its accuracy. The digitally processed images of input parameters were combined in GIS. Finally, landslide susceptibility maps were produced, and the performances were assessed and discussed. Total fifteen landslide susceptibility maps were produced using DT, SVM and ANFIS based models, and the resultant maps were validated using the landslide locations. Prediction performances of these maps were checked by receiver operating characteristics (ROC) by using both success rate curve and prediction rate curve. The validation results showed that, area under the ROC curve for the fifteen models produced using DT, SVM and ANFIS varied from 0.8204 to 0.9421 for success rate curve and 0.7580 to 0.8307 for prediction rate curves, respectively. Moreover, the prediction curves revealed that model 5 of DT has slightly higher prediction performance (83.07), whereas the success rate showed that model 5 of ANFIS has better prediction (94.21) capability among all models. The results of this study showed that landslide susceptibility mapping in the Penang Hill area using the three approaches (e.g., DT, SVM and ANFIS) is viable. As far as the performance of the models are concerned, the results appeared to be quite satisfactory, i.e., the zones determined on the map being zones of relative susceptibility

A100-year maximum flood susceptibility mapping using integrated hydrological and hydrodynamic models: Kelantan River Corridor, Malaysia.

The Kelantan River is located in the northeastern part of Peninsular Malaysia and presents a great challenge in terms of long and recurring floods. The recent floods, in the year 2005, 2006, 2007, 2008 and 2009 due to heavy monsoons rainfall have triggered these events along Kelantan River Basin. This paper summarizes the findings of the flood susceptibility analysis using hydrological and hydrodynamic models with the aid of GIS tools and remote sensing data. Terrain information such as historical flooded areas for the year 2007 was extracted from RADARSAT images. Further, digital elevation model and precipitation information were updated to enable the quantification of flood-associated attributes. For hydrological and hydrodynamic analyses, data obtained from Department of Irrigation and Drainage, Government of Malaysia, has been used corresponding to rain gauge/discharge stations along the Kelantan River. Data on daily and hourly average discharge and peak discharge are modelled for all the stations for different periods. Probability density moisture combined with rainfall simulation models was applied to determine the maximum flood susceptibility map. Results indicate that the flood-prone areas delineated on this map correspond to areas that would be inundated by significant flooding (approximately the 100-year flood)

An integrated DRASTIC model using frequency ratio and two new hybrid methods for groundwater vulnerability assessment

Groundwater management can be effectively implemented by mapping groundwater contamination. Intense agricultural activities and land overexploitation have resulted in groundwater contamination, which is becoming a critical issue, specifically in areas where fertilizers are extensively used on large plantations. The goal of this study was to develop an integrated DRASTIC model with a frequency ratio (FR) as a novel approach. Two new hybrid methods namely single-parameter sensitivity analysis (SPSA) and an analytical hierarchy process (AHP) are also implemented for adjusting feature weights to local settings. The FR is used for DRASTIC model rates, whereas both SPSA and AHP are used for DRASTIC weights. The FR-DRASTIC, FR-SPSA and FR-AHP methods are developed; nitrate samples from the same month in different years are used for analysis and correlation (May 2010 and May 2012). The first nitrate samples are interpolated using the Kriging approach. The Kerman plain is used as an example, which is located in south-eastern part of Iran. Additionally, the new methods are employed in the study area to compare with each other and the original DRASTIC model. The validation results exhibited that using FR approach improved the correlation between vulnerability index and nitrate concentrations compared with original DRASTIC vulnerability correlation which was 0.37. The results indicated that the new hybrid methods exhibited higher correlation 0.75 in the FR-DRASTIC model. Correlations of the FR-SPSA and FR-AHP approaches were 0.77 and 0.80. Hence, the new hybrid methods are more effective and provide reasonably good results. Furthermore, quantitative measures of vulnerability offer an excellent opportunity to effectively prevent as well as reduce contamination

GIS modeling for selection of a transfer station site for residential solid waste separation and recycling

In this study a GIS model was developed and spatial analytical techniques performed to identify and select a suitable location for a waste transfer station in the sprawling suburban town of Petaling Jaya. The lack of a transfer station in urban centres of Malaysia has caused many problems and affects the efficiency of waste collection and disposal. With diminishing space for landfills and the increasing cost of solid waste management, the need for urban solid-waste recycling has become very important. However, finding a place for waste to be efficiently sorted before unwanted waste can be carried to disposal landfills has social and physical constraints. This study applies GIS techniques and analysis for site selection and identifies an acceptable area. In the model, environmental, physical and social constraints were taken into account, resulting in the selection of a potential area that is acceptable to the residents of the area because it is out of range of causing public nuisance and within minimum travelling distance for collection vehicles. The results show that the potential location for the transfer station should be in proximity of the industrial area of Petaling Jaya, allowing for the possible sale of recyclable materials to local industries. The location is also sited near a major highway to allow quick transportation of the rest of the unwanted waste to the landfill

Risk assessment of groundwater pollution with a new methodological framework: application of Dempster-Shafer theory and GIS

Managing natural groundwater resources is challenged by nitrate pollution resulting from agricultural activities. This issue is emerging as an important environmental concern that needs to be addressed through effective groundwater management. Groundwater assessment is an important aspect of groundwater management, particularly in arid and semi-arid regions. This study focused on the Kerman Plain, which is exposed to intensive agricultural activities and land exploitation that result in intense land pollution. The effects of nitrate pollution may be controlled by applying specific measures. Dempster–Shafer theory (DST) was applied in this study to develop a new methodology for assessing pollution risk. Applying this theory as a pioneering approach to assessing groundwater pollution risk is the novel component of this research. This approach provides a major advantage by dealing with varying levels of precision related to information. The spatial association between DRASTIC parameters including D (depth of water), R (net recharge), A (aquifer media), S (soil media), T (topography), I (impact of vadose zone) and C (hydraulic conductivity) and underground nitrate occurrence was evaluated by applying bivariate DST to assign mass functions. Dempster’s rule of combination using GIS was then applied to determine a series of combined mass functions for multiple hydrogeological data layers. The uncertainty of system responses was directly addressed by the proposed methodology. Finally, the modified DRASTIC map with the highest validity and accuracy was selected and combined with the damage map. The comparison between nitrate distribution and vulnerability and the risk maps exhibit high similarity between different vulnerability degrees and nitrate concentrations. Long-term planning of preventive measures and associated developments can be aided by the regions with low and very low risks located in the northeast, northwest, and central regions

Comparison between prediction capabilities of neural network and fuzzy logic techniques for L and slide susceptibility mapping.

Preparation of L and slide susceptibility maps is important for engineering geologists and geomorphologists. However, due to complex nature of L and slides, producing a reliable susceptibility map is not easy. In recent years, various data mining and soft computing techniques are getting popular for the prediction and classification of L and slide susceptibility and hazard mapping. This paper presents a comparative analysis of the prediction capabilities between the neural network and fuzzy logic model for L and slide susceptibility mapping in a geographic information system (GIS) environment. In the first stage, L and slide-related factors such as altitude, slope angle, slope aspect, distance to drainage, distance to road, lithology and normalized difference vegetation index (ndvi) were extracted from topographic and geology and soil maps. Secondly, L and slide locations were identified from the interpretation of aerial photographs, high resolution satellite imageries and extensive field surveys. Then L and slide-susceptibility maps were produced by the application of neural network and fuzzy logic approahc using the aforementioned L and slide related factors. Finally, the results of the analyses were verified using the L and slide location data and compared with the neural network and fuzzy logic models. The validation results showed that the neural network model (accuracy is 88%) is better in prediction than fuzzy logic (accuracy is 84%) models. Results show that "gamma" operator (X = 0.9) showed the best accuracy (84%) while "or" operator showed the worst accuracy (66%)

Multi-scenario rockfall hazard assessment using LiDAR data and GIS

Transportation corridors that pass through mountainous or hilly areas are prone to rockfall hazard. Rockfall incidents in such areas can cause human fatalities and damage to properties in addition to transportation interruptions. In Malaysia, the North–South Expressway is the most significant expressway that operates as the backbone of the peninsula. A portion of this expressway in Jelapang was chosen as the site of rockfall hazard assessment in multiple scenarios. Light detection and ranging techniques are indispensable in capturing high-resolution digital elevation models related to geohazard studies. An airborne laser scanner was used to create a high-density point cloud of the study area. The use of 3D rockfall process modeling in combination with geographic information system (GIS) is a beneficial tool in rockfall hazard studies. In this study, a 3D rockfall model integrated into GIS was used to derive rockfall trajectories and velocity associated with them in multiple scenarios based on a range of mechanical parameter values (coefficients of restitution and friction angle). Rockfall characteristics in terms of frequency, height, and energy were determined through raster modeling. Analytic hierarchy process (AHP) was used to compute the weight of each rockfall characteristic raster that affects rockfall hazard. A spatial model that considers rockfall characteristics was conducted to produce a rockfall hazard map. Moreover, a barrier location was proposed to eliminate rockfall hazard. As a result, rockfall trajectories and their characteristics were derived. The result of AHP shows that rockfall hazard was significantly influenced by rockfall energy and then by frequency and height. The areas at risk were delineated and the hazard percentage along the expressway was observed and demonstrated. The result also shows that with increasing mechanical parameter values, the rockfall trajectories and their characteristics, and consequently rockfall hazard, were increased. In addition, the suggested barrier effectively restrained most of the rockfall trajectories and eliminated the hazard along the expressway. This study can serve not only as a guide for a comprehensive investigation of rockfall hazard but also as a reference that decision makers can use in designing a risk mitigation method. Furthermore, this study is applicable in any rockfall study, especially in situations where mechanical parameters have no specific values

Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area.

This paper presents landslide-susceptibility mapping using an adaptive neuro-fuzzy inference system (ANFIS) using a geographic information system (GIS) environment. In the first stage, landslide locations from the study area were identified by interpreting aerial photographs and supported by an extensive field survey. In the second stage, landslide-related conditioning factors such as altitude, slope angle, plan curvature, distance to drainage, distance to road, soil texture and stream power index (SPI) were extracted from the topographic and soil maps. Then, landslide-susceptible areas were analyzed by the ANFIS approach and mapped using landslide-conditioning factors. In particular, various membership functions (MFs) were applied for the landslide-susceptibility mapping and their results were compared with the field-verified landslide locations. Additionally, the receiver operating characteristics (ROC) curve for all landslide susceptibility maps were drawn and the areas under curve values were calculated. The ROC curve technique is based on the plotting of model sensitivity — true positive fraction values calculated for different threshold values, versus model specificity — true negative fraction values, on a graph. Landslide test locations that were not used during the ANFIS modeling purpose were used to validate the landslide susceptibility maps. The validation results revealed that the susceptibility maps constructed by the ANFIS predictive models using triangular, trapezoidal, generalized bell and polynomial MFs produced reasonable results (84.39%), which can be used for preliminary land-use planning. Finally, the authors concluded that ANFIS is a very useful and an effective tool in regional landslide susceptibility assessment

Characterization of Macro- and Micro-Geomorphology of Cave Channel from High-Resolution 3D Laser Scanning Survey: Case Study of Gomantong Cave in Sabah, Malaysia

Three-dimensional documentation of hypogene cave morphology is one of the major applications of laser scanning survey. This chapter presents applications of terrestrial laser scanning (TLS) survey for analyzing endogenic cave passage geomorphologic structure and morphometry using 3D meshing, high-resolution 3D texture modeling for geovisualization, and its potential for cave art documentation. To achieve this, multi-scale resolution 3D models were generated; one using the mesh model for macro-morphological analysis and the other with the full-resolution scan to produce high quality 3D texture model for identification of micro-morphological features. The mesh model of the cave makes it possible to analyze the general shape, distinguish phreatic tube from post-speleogenetic modified conduits and carry out morphometric measurements including the cave volume and channel surface area. The 3D texture model provides true to live visualization of the cave with exceptionally high level of accuracy and details that would be impossible to obtain with direct observation by visiting the site or from the mesh model. The model allows discerning different speleogenetic phases, karstification processes and micro-morphologies such as wall and ceiling seepage, hanging rocks, fractures, scallops, ceiling flush dome, pockets, bell-hole and avens. Also, the texture model permits identifying cave arts and engravings along the passage