Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS
Highlights
► Landslide susceptibility mapping was carried out using ANFIS. ► ANFIS models with the Sigmf and Gaussmf have a good prediction capability. ► ANFIS is robust and efficient method for landslide susceptibility mapping.
Introduction
In recent years, the occurrences of natural disasters in Vietnam have increased significantly mostly due to the effect of the climate change. The northwest mountainous area of Vietnam is one of the regions, which is heavily affected by landslide activities and flooding events. In most of the cases, landslides occurred following heavy rainfalls and especially during tropical rainstorms. However, to date, little effort has been made to assess or forecast these events. Through scientific analysis of landslides, geoscientists and civil engineers can assess and delineate landslide-susceptible areas, offering the potential for a decrease in landslide damage through proper slope management (Pradhan, 2011). So research on landslide susceptibility mapping for the Northwest area, including the Hoa Binh province, is an urgent task in Vietnam. The result of landslide research may provide valuable information that helps to forecast such events as well as to find measures to mitigate subsequent losses to future landslides.
Many techniques and methods have been proposed and developed in the landslide literature. Basically, they can be divided into direct and indirect methods (Guzzetti et al., 1999). Direct methods can be the methods of using total stations, global satellite navigation systems, or other methods for landslide surveying (Van Westen et al., 2003). Through direct methods, using experiences and knowledge of experts, the degree of susceptibility can be directly determined. However, in such cases fieldworks are extensively required, so they are time-consuming and not cost effective. Whereas in the case of the indirect methods, usually a landslide inventory map is used in conjunction with the landslide conditioning factors (Van Westen et al., 2003). Generally, the landslide susceptibility levels are determined based on the correlation between the existing landslide inventory and the conditioning factors as mentioned above. However, the accuracy of landslide susceptibility maps depends on the level of accuracy of mapping of different conditioning factors and landslide inventory.
In recent years, some newer approaches for landslide analysis have been carried out such as artificial neural networks (Ercanoglu, 2005, Ermini et al., 2005, Lee et al., 2003, Lee et al., 2004, Nefeslioglu et al., 2008, Pradhan and Lee, 2010a, Pradhan and Lee, 2010c, Pradhan et al., 2010a), fuzzy logic (Akgun et al., 2011, Ercanoglu and Gokceoglu, 2002, Ercanoglu and Gokceoglu, 2004, Kanungo et al., 2008, Pradhan, 2010a), decision tree (Nefeslioglu et al., 2010), and neuro-fuzzy (Kanungo et al., 2006, Oh and Pradhan, 2011, Pradhan et al., 2010b, Sezer et al., 2011, Vahidnia et al., 2010). Generally, these approaches give rise to qualitative and quantitative maps of the landslide hazard areas, and the spatial results are appealing (Pradhan, 2010b).
In the case of the neuro-fuzzy, which is a combination of fuzzy logic and neural networks, Kanungo et al. (2006) used the weights of the layers obtained from the trained neural network integrated with the ratings obtained from fuzzy logic to obtain a landslide susceptibility index. The membership degrees of each layer class were determined based on the relationship of existing landslides with the classes. Vahidnia et al. (2010) used the output of a fuzzy inference system as target for a neural network. There is no doubt that expert knowledge played an important role to obtain the accuracy of these results. And the subjectivity is not easy to eliminate.
Another combined method, developed by Jang (1993), is the Adaptive Neuro-Fuzzy Inference System (ANFIS). This method, using the Takagi–Sugeno rule format, is the combination of an optimized premise membership function (gradient descent) with an optimized consequent equation (linear least squares estimator). Based on a given input and target, ANFIS can construct a fuzzy inference system where their membership function parameters are to be adjusted using the hybrid learning algorithm. ANFIS has been widely used in modeling complex systems (Soyguder and Alli, 2009). However, its application in landslide studies is still limited to a very few cases. Pradhan et al. (2010b) used eight landslide conditioning factors for the ANFIS model for landslide susceptibility mapping in a study area in Malaysia. Their results indicated a very high prediction accuracy of 97%. Oh and Pradhan (2011) and Sezer et al. (2011) have also used an ANFIS based model for landslide susceptibility mapping in different parts of Malaysia. However, the disadvantage of these approaches is that it is difficult to objectively determine the epoch where the landslide model starts over-fitting in the training phase. Expert opinions are suggested used to determine the number of membership functions of the inputs, the physical meanings of the inputs, and the number of training epochs for preventing over-learning. Oh and Pradhan (2011) also asserted that, in order to check the performance of the neuron-fuzzy model in landslide susceptibility mapping, and for the method to be more generally applied, more case studies should be conducted.
The main difference between this study and the aforementioned literature is that the ANFIS was applied for landslide susceptibility assessment in the Hoa Binh province of Vietnam, using the subtractive clustering method proposed by Chiu (1994). This is the automated data-driven based method for constructing the primary fuzzy models. The main advantages of this method are that it can process a large number of input observations and avoid the explosion of the rule base (Eftekhari and Katebi, 2008). In addition, the method proposed by Jang et al. (1997) was used to control over-fitting of the landslide model by testing the fuzzy inference system (FIS), trained on the training data against the checking data. Using the six different membership functions, six ANFIS models were constructed. Finally, the comparison was carried out to find the most suitable model for the study area.
Section snippets
Study area
The Hoa Binh province is situated at the monsoonal region in the North West part of Vietnam. It covers an area of about 4660 km2 (Fig. 1) between the longitude 104°48′E and 105°50′E, and between the latitude 20°17′N and 21°08′N. The altitude decreases from Northwest to Southeast and varies in the range from 0 to 1510 m. This province is a “transition” area situated between the Northwest Mountains and the Red River delta.
The climate of this region is characterized by high temperatures and high
Data used
Since landslide occurrences in the past and present are keys to future spatial prediction (Guzzetti et al., 1999), a landslide inventory map is a pre-requisite for such a study. The landslide inventory map of the study area was compiled by inheriting the landslide locations from three projects: (1) the landslide inventory map in the Northern mountainous province (Hue et al., 2004); (2) the landslide inventory map 2005 (Thinh et al., 2005); and (3) the landslide inventory map 2007 (My, 2007).
Fuzzy inference system
Fuzzy inference is the process of using fuzzy logic to formulate a non-linear mapping from input data to an output. A fuzzy inference system (FIS) consists of three components: (1) a rule base, which contains a selection of fuzzy rules, (2) a data base, which defines the membership functions used in the fuzzy rules, and (3) a reasoning mechanism that performs the inference procedure on the rules and given facts (Alakhras, 2005, Jang et al., 1997, Kumanan et al., 2008).
There are three types of
Validation and comparison of landslide susceptibility maps
Using the success rate and the prediction rate methods, the validation process was performed by comparing the existing landslide data with the six landslide susceptibility maps. (Brenning, 2005, Chung and Fabbri, 1999, Chung and Fabbri, 2003, Lee et al., 2007). The success rate results were obtained by comparing the landslide training pixels (684 landslide pixels) with the six landslide susceptibility maps. Fig. 8 shows the success rate curves for the six ANFIS landslide models. The model with
Concluding remarks
In this study, an adaptive neuro-fuzzy inference system (ANFIS) was applied to assess the regional landslide susceptibility for the Hoa Binh province of Vietnam. The landslide inventory, comprising all significant landslide events that occurred during the last ten years, was used in the analysis. Among the total of 118 landslide areas, 82 cases with 864 landslide grid cells were selected for the ANFIS training process and the remaining 36 cases with 315 landslide grid cells were used for the
Acknowledgment
This research was funded by the Norwegian Quota scholarship program. The data analysis and write-up were carried out as a part of the first author's Ph.D. studies at the Geomatics section, Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, Norway. Thanks to the anonymous reviewers for their valuable and constructive comments, which were useful to revise the manuscript.
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