Spatial prediction of flood susceptible areas using rule based decision tree (DT) and ensemble bivariate and multivariate statistical models in GIS

Flood is one of the natural hazards which occur all over the world and it is critical to be controlled through proper management. Severe flood events in Kelantan, Malaysia cause damage to both life and property every year, and therefore the development of flood model to recognize the susceptible areas in watersheds is important for decision makers. Remote sensing (RS) and geographic information system (GIS) techniques could be useful in hydrological studies while they are able to fulfill all the requirements for comprehensive, rapid and accurate analysis. The aim of the current research is to compare the prediction performances of two different approaches such as rule-based decision tree (DT) and combination of frequency ratio (FR) and logistic regression (LR) statistical methods for flood susceptibility mapping at Kelantan, Malaysia. DT is based on the rules which are created precisely and strongly by considering all the characteristics of the variables which can enhance the performance of the flood susceptibility mapping. On the other hand, LR as multivariate statistical analysis (MSA) has some weak points. For that reason, FR was used to analyze the impact of classes of each variable on flood occurrence and overcome the weakness of LR. At first, flood inventory map with a total of 155 flood locations was extracted from various sources over the part of the Kelantan. Then the flood inventory data was randomly divided into a testing dataset 70% (115 flood locations) for training the models and the remaining 30% (40 flood locations) was used for validation purpose. The spatial database includes digital elevation model (DEM), curvature, geology, river, stream power index (SPI), rainfall, land use/cover (LULC), soil type, topographic wetness index (TWI) and slope. For validation both success and prediction rate curves were performed. The validation results showed that, area under the curve for the results of DT and integrated method of FR and LR was 87% and 90% for success rate and 82% and 83% for prediction rate respectively

A new semiautomated detection mapping of flood extent from TerraSAR-X satellite image using rule-based classification and Taguchi optimization techniques

Floods are among the most destructive natural disasters worldwide. In flood disaster management programs, flood mapping is an initial step. This research proposes an efficient methodology to recognize and map flooded areas by using TerraSAR-X imagery. First, a TerraSAR-X satellite image was captured during a flood event in Kuala Terengganu, Malaysia, to map the inundated areas. Multispectral Landsat imagery was then used to detect water bodies prior to the flooding. In synthetic aperture radar (SAR) imagery, the water bodies and flood locations appear in black; thus, both objects were classified as one. To overcome this drawback, the class of the water bodies was extracted from the Landsat image and then subtracted from that extracted from the TerraSAR-X image. The remaining water bodies represented the flooded locations. Object-oriented classification and Taguchi method were implemented for both images. The Landsat images were categorized into three classes, namely, urban, vegetation, and water bodies. By contrast, only water bodies were extracted from the TerraSAR-X image. The classification results were then evaluated using a confusion matrix. To examine the efficiency of the proposed method, iterative self-organizing data analysis technique (ISODATA) classification method was applied on TerraSAR-X after employing the segmentation process during object-oriented-rule-based method, and the results were compared. The overall accuracy values of the classified maps derived from TerraSAR-X using the rule-based method and Landsat imagery were 86.18 and 93.04, respectively. Consequently, the flooded locations were recognized and mapped by subtracting the two classes of water bodies from these images. The acquired overall accuracy for TerraSAR-X using ISODATA was considerably low at only 57.98. The current research combined the methods and the optimization technique used as an innovative flood detection application. The successful production of a reliable and accurate flood inventory map confirmed the efficiency of the methodology. Therefore, the proposed method can assist researchers and planners in implementing and expediting flood inventory mapping

A comparative assessment between object and pixel-based classification approaches for land-use/land-cover mapping using Spot 5 imagery

Land use/land cover (LULC) classification with high accuracy is necessary, especially in eco-environment research, urban planning, vegetation condition study and soil management. Over the last decade a number of classification algorithms have been developed for the analysis of remotely sensed data. The most notable algorithms are the object-oriented K-Nearest Neighbour (K-NN), Support Vector Machines (SVMs) and the Decision Trees (DTs) amongst many others. In this study, LULC types of Selangor area were analyzed on the basis of the classification results acquired using the pixel-based and object-based image analysis approaches. SPOT 5 satellite images with four spectral bands from 2003 and 2010 were used to carry out the image classification and ground truth data were collected from Google Earth and field trips. In pixel-based image analysis, a supervised classification was performed using the DT classifier. On the other hand, object-oriented (K-NN) image analysis was evaluated using standard nearest neighbour as classifier. Subsequently SVM object-based classification was performed. Five LULC categories were extracted and the results were compared between them. The overall classification accuracies for 2003 and 2010 showed that the object-oriented (K-NN) (90.5% and 91%) performed better results than the pixel-based DT (68.6% and 68.4%) and object-based SVM (80.6% and 78.15%). In general, the object-oriented (K-NN) performed better than both DTs and SVMs. The obtained LULC classification maps can be used to improve various applications such as change detection, urban design, environmental management and zooning

Using ALOS PALSAR derived high - resolution DInSAR to detect slow - moving landslides in tropical forest: Cameron Highlands, Malaysia

Landslide is one of the natural hazards that pose maximum threat for human lives and property in mountainous regions. Mitigation and prediction of this phenomenon can be done through the detection of landslide-susceptible areas. Therefore, an appropriate landslide analysis is needed in order to map and consequently understand the characteristic of this disaster. One of the recent popular remote sensing techniques in deformation analysis is the differential interferometric synthetic aperture radar which is popularly known as DInSAR. Due to the mass vegetation condition in Malaysia, a long-wavelength synthetic aperture radar (∼24 cm) is required in order to be able to penetrate through the forests and reach the bare land. For that reason, ALOS PALSAR HH imagery was used in this study to derive a deformation map of the Gunung Pass area located in the tropical forest of the Cameron Highlands, Malaysia. In this study, the ascending orbit ALOS PALSAR images were acquired in September 2008, January 2009 and December 2009. Subsequently the displacement measurements of the study site (Gunung Pass) were calculated. The accuracy of the result was evaluated through its comparison with ground truth data using the R2 and root mean square error (RMSE) methods. The resulted deformation map showed the landslide locations in the study area from interpretation of the results with 0.84 R2 and 0.151 RMSE. The DInSAR precision was 11.8 cm which proved the efficiency of the proposed method in detecting landslides in a tropical country like Malaysia. It is highly recommended to use the proposed method for any other deformation studies

A novel GIS-based ensemble technique for flood susceptibility mapping using evidential belief function and support vector machine: Brisbane, Australia

In this study, we propose and test a novel ensemble method for improving the accuracy of each method in flood susceptibility mapping using evidential belief function (EBF) and support vector machine (SVM). The outcome of the proposed method was compared with the results of each method. The proposed method was implemented four times using different SVM kernels. Hence, the efficiency of each SVM kernel was also assessed. First, a bivariate statistical analysis using EBF was performed to assess the correlations among the classes of each flood conditioning factor with flooding. Subsequently, the outcome of the first stage was used in a multivariate statistical analysis performed by SVM. A highest prediction accuracy of 92.11% was achieved by an ensemble EBF-SVM-radial basis function method; the achieved accuracy was 7% and 3% higher than that offered by the individual EBF method and the individual SVM method, respectively. Among all the applied methods, both the individual EBF and SVM methods achieved the lowest accuracies. The reason for the improved accuracy offered by the ensemble methods is that by integrating the methods, a more detailed assessment of the flooding and conditioning factors can be performed, thereby increasing the accuracy of the final map

Flood susceptibility analysis and its verification using a novel ensemble support vector machine and frequency ratio method

Flood is one of the most commonly occurred natural hazards worldwide. Severe flood occurrences in Kelantan, Malaysia cause damage to both life and property every year. Due to the huge losses in this area, development of appropriate flood modeling is required for the government. Remote sensing and geographic information system techniques can support overall flood management as they can produce rapid data collection and analysis for hydrological studies. The existing models for flood mapping have some weak points that may improve through more sophisticated and ensemble methods. The current research aimed to propose a novel ensemble method by integrating support vector machine (SVM) and frequency ratio (FR) to produce spatial modeling in flood susceptibility assessment. In the literature, mostly statistical and machine learning methods are used individually; however, their integration can enhance the final output. The FR model can perform bivariate statistical analysis and evaluate the correlation between the flooding and classes of each conditioning factors. The weights achieved by FR can be assigned to each conditioning factor and the resulted factors can be used in SVM analysis. In order to examine the efficiency of the proposed ensemble method and to show the proficiency of SVM, another machine learning algorithm such as decision tree (DT) was applied and the results were compared. To perform the methods, the upper catchment of the Kelantan basin in Malaysia was chosen. First, a flood inventory map with a total of 155 flood locations were extracted from various sources over the study area. The flood inventory map was randomly divided into two dataset; 70 % (115 flood locations) for the purpose of training and the remaining 30 % (40 flood locations) was used for validation. The spatial database included digital elevation model, curvature, geology, river, stream power index, rainfall, land use/cover, soil type, topographic wetness index and slope. For model validation, area under curve method was used and both success and prediction rate curves were calculated. The validation results for ensemble method showed 88.71 and 85.21 % for success rate and prediction rate respectively. The DT model showed 87.00 and 82.00 % for the success rate and prediction rate respectively. It is evident that the accuracies were increased using the ensemble method. The acquired results proved the efficiency of the proposed ensemble method as rapid, accurate and reasonable in flood susceptibility assessment

Detection of vertical slope movement in highly vegetated tropical area of Gunung pass landslide, Malaysia, using L-band InSAR technique

Landslides are one of the most dangerous natural hazards in the world which have significant negative impact on so many lives and properties. Interferometric Synthetic Aperture Radar (InSAR) is an imaging technique for measuring changes in the detailed characteristics of the surface which is timely and cost effective. This research aimed to detect the landslide that occurred in Gunung pass area, Malaysia using InSAR generated from ALOSPALSAR repeat pass data. The signals information was converted into amplitude and phase for both scenes where the phases were used to construct the InSAR. Goldstein filter was used to reduce the phase noise and the results were used as an input for phase unwrapping. Using the unwrapped phase, the vertical displacement was measured and landslide was recognized. Results showed the efficiency of InSAR in detecting the movement of landslide in Gunung pass without the differential having to generate DInSAR. The results were validated using the observed reference point of the landslides and the root mean square error (RMSE) was 0.19. Furthermore, advance 3D processing was performed for measuring the volume of the landslides. The achievements of current research represented that PALSAR data yield excellent performance to generate the interferometric and landslide could be detected very precisely in highly vegetated tropical forest

Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS

Flood is one of the most devastating natural disasters that occur frequently in Terengganu, Malaysia. Recently, ensemble based techniques are getting extremely popular in flood modeling. In this paper, weights-of-evidence (WoE) model was utilized first, to assess the impact of classes of each conditioning factor on flooding through bivariate statistical analysis (BSA). Then, these factors were reclassified using the acquired weights and entered into the support vector machine (SVM) model to evaluate the correlation between flood occurrence and each conditioning factor. Through this integration, the weak point of WoE can be solved and the performance of the SVM will be enhanced. The spatial database included flood inventory, slope, stream power index (SPI), topographic wetness index (TWI), altitude, curvature, distance from the river, geology, rainfall, land use/cover (LULC), and soil type. Four kernel types of SVM (linear kernel (LN), polynomial kernel (PL), radial basis function kernel (RBF), and sigmoid kernel (SIG)) were used to investigate the performance of each kernel type. The efficiency of the new ensemble WoE and SVM method was tested using area under curve (AUC) which measured the prediction and success rates. The validation results proved the strength and efficiency of the ensemble method over the individual methods. The best results were obtained from RBF kernel when compared with the other kernel types. Success rate and prediction rate for ensemble WoE and RBF-SVM method were 96.48% and 95.67% respectively. The proposed ensemble flood susceptibility mapping method could assist researchers and local governments in flood mitigation strategies

Remote Sensing Data Reveals Eco-Environmental Changes in Urban Areas of Klang Valley, Malaysia: Contribution from Object Based Analysis.

Understanding the growth and changes in urban environments are the most dynamic system on the earth’s surface is critical for urban planning and sustainable management. This study attempts to present a space-borne satellite-based approach to demonstrate the urban change and its relation with land surface temperature (LST) variation in urban areas of Klang valley, Malaysia. For this purpose an object-based nearest neighbour classifier (S-NN) approach was first applied on SPOT 5 data acquired on 2003 and 2010 and subsequently five land cover categories were extracted. The overall accuracies of the classified maps of 2003 and 2010 were 90.5 % and 91 % respectively. The classified maps were then used as inputs to perform the post classification change detection. The results revealed that the post-classification object-based change detection analysis performed reasonably well with an overall accuracy of 87.5 %, with Kappa statistic of 0.81 %. The changes represented that the urban expanded by 10 % over the period, whereas the urban expansion had caused reduction in soil (1.4 %) and vegetation (11.4 %), and growth in oil palm (2 %), and water (0.7 %). Additionally decision tree method was used to derive the surface heat fluxes from thermal infrared Landsat TM and ETM+bands. Subsequently, a comparison was made with classified result from SPOT 5 images. Results showed high correlation between urban growth and LST

Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in West Sumatera Province, Indonesia

An 8 Richter Scale (RS) earthquake struck West Sumatra on Wednesday, 30 September 2009, at 17.16 pm which led to huge number of landslides. Hence a comprehensive landslide susceptibility mapping (LSM) should be produced in order to reduce the damages to people and infrastructures. In the international landslide literature, various statistical methods such as frequency ratio (FR) and logistic regression (LR) have been widely used individually for LSM, but they have some weaknesses. FR which is able to perform bivariate statistical analysis (BSA) assesses the influence of classes of each conditioning factor on landslide occurrence. However, the correlation between the factors is mostly neglected. On the other hand, LR is able to analyze the relationship among the factors while it is not capable to evaluate the classes of each landslide conditioning factor. This paper aims to propose an ensemble method of FR and LR in order to overcome their weak points. For LSM, a landslide inventory map with a total of 87 landslide locations was extracted from various sources. Then the landslide inventory was randomly divided into two datasets 70% for training the models and the remaining 30% was used for validation purpose. The landslide conditioning factors consist of: altitude, curvature, river, SPI, rainfall, soil type, soil texture, land use/cover (LULC), peak ground acceleration (PGA), geology, slope, aspect, lineament and topographic wetness index (TWI). Four PGA of 7.5, 8, 8.6 and 9 were acquired and PGA 8 which was related to the 2009 earthquake was used to generate the model. Finally, the produced landslide susceptibility maps were validated using an area under the (ROC) curve method. For the model which was derived by PGA 8, the validation results showed 84% and 78% success and prediction rates respectively. Furthermore, the prediction rates for the models made by PGA 7.2, 8.6 and 9 are 79%, 78% and 81% respectively. The result proved the reasonable efficiency of the proposed method for earthquake induced landslide susceptibility mapping. Also the proposed ensemble method can be used in other hazard studies as it is capable to produce rapid and accurate assessment for disaster management and decision making