landslide susceptibility assesment in chittagong

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BANGLADESH USING ADAPTIVE NEURO FUZZY INFERENCE SYSTEM ... study is to evaluate the application of Adaptive Neuro Fuzzy Inference System (ANFIS) ... that through scientific research and analysis it is possible to predict the most landslide ... (Ayalew & Yamagishi, 2005), Fuzzy logic (Pourghasemi, Pradhan, ...

Proceedings, International Conference on Disaster Risk Mitigation, Dhaka, Bangladesh, September 23 - 24, 2017


ABSTRACT The main objective of the study is to evaluate the application of Adaptive Neuro Fuzzy Inference System (ANFIS) and GIS which is regarded as a quite new approach for landslide susceptibility mapping in Chittagong district of Bangladesh. The necessary input parameters and the landslide inventory map with a total of 105 landslide locations was obtained from different sources. Ten landslide triggering factors-elevation, slope, aspect, curvature, normalized difference vegetation index (NDVI), soil type, rainfall, distance to road, distance to stream, distance to rivers, and distance to faults were considered in the analysis. At first, the landslide inventory database was randomly divided into two parts containing a testing dataset 70% (73 landslide locations) for training the landslide models and the remaining 30% (32 landslides locations) was used for validation of the models. The hybrid learning algorithm with five different membership functions were applied to generate the landslide susceptibility maps. The validation results showed that the area under the curve (AUC) for five ANFIS models vary from 0.75 to 0.88. Quantitively, the prediction results showed quite a satisfactory performance for landslide susceptibility assessment using ANFIS.

Introduction Bangladesh is a land of natural disaster due to its geographical location and climate change. Each year a number of natural disasters occur here. Among these, landslide is the common one which causes an irreparable socioeconomic consequence in the hilly regions of Bangladesh especially in Chittagong district. Due to rapid and unplanned urbanization in the Chittagong city it has been repeatedly hit by landslides in recent years. Statistics showed that landslide caused more than 500 deaths in the recent 18 years causing death of 156 people in the running year of 2017. It is a matter of hope that through scientific research and analysis it is possible to predict the most landslide probable zones. Various GIS (Bai et al., 2010; Kamp, Growley, Khattak, & Owen, 2008) Logistic Regression (Ayalew & Yamagishi, 2005), Fuzzy logic (Pourghasemi, Pradhan, & Gokceoglu, 2012) based techniques have been used to landslide mapping(LSM).More recently different machine learning approaches like- Artificial Neural Network(Choi, Oh, Lee, Lee, & Lee, 2012; Ermini, Catani, & Casagli, 2005), Decision Tree(Tien Bui, Pradhan, Lofman, Revhaug, & Dick, 2012), Genetic Algorithm(Kavzoglu, Sahin, & Colkesen, 2015) have been applied to asses this topic. Nowadays some soft computing techniques like- Support Vector Machine (Damaševičius, 2010; Oh & Pradhan, 2011; Polykretis, Chalkias, & Ferentinou, 2017;), Adaptive Neuro Fuzzy Inference System (Bui, Pradhan, Lofman, Revhaug, & Dick, 2012; Oh & Pradhan, 2011; Polykretis et al., 2017; Sezer, Pradhan, & Gokceoglu, 2011; Tien Bui et al., 2012) etc. have been widely used for landslide mapping which have been proven as outperforming the previous ones. In Bangladesh, in 2015, Ahmed (Ahmed, 2015) used a GIS-based MultiCriteria Decision Analysis (MCDA) method where— the Artificial Hierarchy Process(AHP), Weighted Linear Combination (WLC), and Ordered Weighted Average (OWA)—were applied to scientifically assess the landslide susceptible areas in Chittagong Metropolitan area(CMA). But more sophisticated result can be obtained through the most recent soft computing method. So, the main objective of this research is to identify the most landslide probable zones of Chittagong district of Bangladesh using Adaptive Neuro Fuzzy Inference System(ANFIS) and GIS. 1

MS Student, Dept. of Computer Science and Engineering, University of Dhaka, Dhaka-1000, Bangladesh Associate Professor, Dept. of Computer Science and Engineering, University of Dhaka, Dhaka-1000, Bangladesh 3 MS Student and Research Associate, Institute of Water and Flood Management, BUET, Dhaka-1000, Bangladesh Corresponding author: Email- [email protected] 2

Proceedings, International Conference on Disaster Risk Mitigation, Dhaka, Bangladesh, September 23 - 24, 2017

Study Area and Data Used Chittagong district is situated in between 21°54' and 22°59'N latitudes and in between 91°17' and 92°13' E longitudes. Its area is about 5282.98 Its annual rainfall is 3194 mm. The hills of Chittagong are mostly constituted of loose and weather-beaten tertiary sedimentary rocks. When there is heavy rainfall within a short period of time water infiltrates easily into the loose rocks and increase the pore water pressure and finally surpass the shear strength of the material which triggers to initiate landslide(Banglapedia). A Digital Elevation Model (DEM), was collected from the USGS and NASA website. It has a resolution of 90m and the scale 1:877.345. The slope, aspect, and curvature data were extracted from the DEM data using ArcGIS 10.5. The slope, aspect, curvature map was grouped into nine, ten and three different classes respectively. The tectonic faults were extracted from USGS website and distance to fault line (Euclidian) was extracted from it. It was classified on ten classes. The road network data was collected from LGED and from this data distance(Euclidian) to road was calculated. The river network data was extracted from DEM and distance to stream was extracted from it. The soil type map was collected from BARC and it was categorized in six different classes. The Normalized Difference Vegetation Index(NDVI) map was created from LandSAT-8 image which was collected from USGS and NASA website. Rainfall data was collected from BMD. The average annual (1970-2017) rainfall data was compiled from seven observed station. The rainfall data was interpolated using IDW (inverse distance weight) method to create a surface. All data of the variable for each grid cell were made normalization (range: from 0 to 1) using the following normalization formula(eq.1). ---(1) Finally, the normalized values of each parameter were used in MATLAB software MathWorks R2015a for ANFIS simulation.

Methodology Adaptive Neuro Fuzzy Inference System(ANFIS) is a combination of both Fuzzy logic and Neural Networks which overcomes the limitation of both. ANFIS can be trained (which is the limitation of Fuzzy logic) to generate If-Then rules (which is the limitation of Artificial Neural Network). Actually, ANFIS works on the basic following steps- Generate a Fuzzy Inference System(FIS) using a sub-clustering or grid partitioning method; Integrate the expert knowledge with real world input-output data and Train a Neural Network to relate the combined output to landslide parameters so that the errors are minimized (Vahidnia et al., 2010). Then the model is used for further training of new data using hybrid learning or back propagation algorithm. For this study we have used subclustering method and hybrid learning algorithm. The conceptual of ANFIS simulation is shown in the following Fig.1.

Table 1: ANFIS structure and input parameters

Figure 1: A conceptual diagram of ANFIS

Number of Layers

Input: 10

Types of Membership Function

Gaussian, Bell, Sigmoid, DSigmoid and PSigmoid

Training Optimization Method

Hybrid Learning Algorithm

Training epoch number


Training Error tolerance


Output: 1

Proceedings, International Conference on Disaster Risk Mitigation, Dhaka, Bangladesh, September 23 - 24, 2017

Experimental Result and Validation A landslide inventory map with 105 observed historical landslide locations was prepared first. Landslide location data were collected from different published literatures (Ahmed & Rubel, 2013; Rahman, Ahmed, Huq, Rahman, & Al-Hussaini, 2016; Sarker & Rashid, 2013) and from Asian Disaster Preparedness Center (ADPC) database. It should be mentioned here that the recent landslide (2017) data were incorporated as the landslide inventory in preparing the training and checking of ANFIS simulation. The database was spilt into two parts such as training dataset-containing 70%(73 landslide locations and 3500grid cells) for calibration of the model and validation data sets-containing 30%(32 landslide location and1498 grid cells) for validation of the model. All of the landslide grid cells denoting the presence of the landslide were assigned the value 1 and the landslide free grid cells was assigned the value 0. The ten landslide conditioning factors (used as input) with one output variable (presence or absence of landslide) consisting of 5000 grids cells were used for training ANFIS. The experiment was performed using MathWorks R2015a. Five different membership functions- Gaussian, Bell, Sigmoid, DSigmoid and PSigmoid were used to gain better output which led to 32 If-Then rules. The training parameters and the structure of ANFIS structure have been shown in Table.1.

ROC Curve 1 0.9 0.8

True Positive Rate

0.7 0.6 Gausian MF(AUC= 0.887) Gbell MF(AUC=0.875)

0.5 0.4






0.1 0 0






False Positive Rate

Figure 2: Landslide Susceptible Map (Gaussian)

Figure 3: ROC curves for different membership functions

The experimental results showed that the Gaussian membership function(MF) performs the best with training error 0.010954762 and checking error 0.213512379 which are lowest among the used MFs. Finally, the grid cells (424470 cells) of the entire study area for ten landslides triggering factors were used to determine landslide susceptibility index based on the previously trained model. And then, the landslide susceptibility index was converted into GIS gridded data for landslide susceptibility mapping. The gridded data were joined with the GIS shape file using a unique ID (Field ID). Inverse distance weight (IDW) interpolation was used for creating surface from the predicted point data of the cells. Finally, the landslide susceptibility index was classified into five categories based on the visual inspection and expert opinion (Figure 2). For verification of the predicted by ANFIS model the success rate and area under the ROC curves(AUC) were computed for five different membership functions used in the model. The Receiver Operating Characteristic(ROC) curves was created from the existing Figure 3: ROC curves for 5 MFs landslide data and predicted landslide susceptibility map. Generally, the value of AUC varies from 0.5 to 1. The value 1 indicates perfect prediction and 0.5 indicates lowest prediction. The closer the value of AUC to 1, the better the result. AUC curves demonstrate that the maximum area was obtained from Gaussian 88.7% and minimum area was covered by DSigmoid 80.01%.

Proceedings, International Conference on Disaster Risk Mitigation, Dhaka, Bangladesh, September 23 - 24, 2017

Conclusion As overfitting may mislead to the output result and ANFIS is very sensitive to it, it was a great challenge to determine the number of epochs and number of membership functions. Experimental result demonstrates that ANFIS has high performance capacity although the performance relies not only on the methodology used but also on the data available and used. The simulated landslide susceptibility maps can be used for preliminary landslide zoning and land use planning.

Acknowledgements The author would like to thank Ministry of Information and Communication Technology (ICT Division), Peoples’ Republic of Bangladesh, for providing the fellowship for conducting the research.

References Ahmed, B. (2015). Landslide susceptibility mapping using multi-criteria evaluation techniques in Chittagong Metropolitan Area, Bangladesh. Landslides, 12(6), 1077-1095. Ayalew, L., & Yamagishi, H. (2005). The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology, 65(1), 15-31. Bai, S.-B., Wang, J., Lü, G.-N., Zhou, P.-G., Hou, S.-S., & Xu, S.-N. (2010). GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area, China. Geomorphology, 115(1), 23-31. Banglapedia. Retrieved 31-08-2017, Bui, D. T., Pradhan, B., Lofman, O., Revhaug, I., & Dick, O. B. (2012). Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS. Computers & Geosciences, 45, 199-211. Choi, J., Oh, H.-J., Lee, H.-J., Lee, C., & Lee, S. (2012). Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using ASTER images and GIS. Engineering Geology, 124, 12-23. Damaševičius, R. (2010). Structural analysis of regulatory DNA sequences using grammar inference and support vector machine. Neurocomputing, 73(4), 633-638. Ermini, L., Catani, F., & Casagli, N. (2005). Artificial neural networks applied to landslide susceptibility assessment. Geomorphology, 66(1), 327-343. Kamp, U., Growley, B. J., Khattak, G. A., & Owen, L. A. (2008). GIS-based landslide susceptibility mapping for the 2005 Kashmir earthquake region. Geomorphology, 101(4), 631-642. Kavzoglu, T., Sahin, E. K., & Colkesen, I. (2015). Selecting optimal conditioning factors in shallow translational landslide susceptibility mapping using genetic algorithm. Engineering Geology, 192, 101-112. Keerthi, S. S., & Lin, C.-J. (2003). Asymptotic behaviors of support vector machines with Gaussian kernel. Neural computation, 15(7), 1667-1689. Oh, H.-J., & Pradhan, B. (2011). Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area. Computers & Geosciences, 37(9), 1264-1276. doi: 10.1016/j.cageo.2010.10.012 Polykretis, C., Chalkias, C., & Ferentinou, M. (2017). Adaptive neuro-fuzzy inference system (ANFIS) modeling for landslide susceptibility assessment in a Mediterranean hilly area. Bulletin of Engineering Geology and the Environment. doi: 10.1007/s10064-017-1125-1 Pourghasemi, H. R., Pradhan, B., & Gokceoglu, C. (2012). Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Natural hazards, 63(2), 965996. Rahman, M., Ahmed, B., Huq, F., Rahman, S., & Al-Hussaini, T. (2016). Landslide inventory in an urban setting in the context of Chittagong Metropolitan Area, Bangladesh. Paper presented at the Proceedings of the 3rd International Conference on Advances in Civil Engineering. Sarker, A. A., & Rashid, A. M. (2013). Landslide and Flashflood in Bangladesh Disaster Risk Reduction Approaches in Bangladesh (pp. 165-189): Springer. Sezer, E. A., Pradhan, B., & Gokceoglu, C. (2011). Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang valley, Malaysia. Expert Systems with Applications, 38(7), 8208-8219. doi: 10.1016/j.eswa.2010.12.167 Tien Bui, D., Pradhan, B., Lofman, O., Revhaug, I., & Dick, O. B. (2012). Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS. Computers & Geosciences, 45, 199-211. doi: 10.1016/j.cageo.2011.10.031

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