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Journal Article•DOI•

Landslide susceptibility mapping at Vaz Watershed (Iran) using an artificial neural network model: a comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms

Mohammad Zare1, Hamid Reza Pourghasemi2, Mahdi Vafakhah2, Biswajeet Pradhan3•
University of Tehran1, Tarbiat Modares University2, Universiti Putra Malaysia3
01 Aug 2013-Arabian Journal of Geosciences (Springer Berlin Heidelberg)-Vol. 6, Iss: 8, pp 2873-2888
TL;DR: In this article, the authors compared the results of two artificial neural network (ANN) algorithms, i.e., multilayer perceptron (MLP) and radial basic function (RBF) for spatial prediction of landslide susceptibility in Vaz Watershed, Iran.
Abstract: Landslide susceptibility and hazard assessments are the most important steps in landslide risk mapping. The main objective of this study was to investigate and compare the results of two artificial neural network (ANN) algorithms, i.e., multilayer perceptron (MLP) and radial basic function (RBF) for spatial prediction of landslide susceptibility in Vaz Watershed, Iran. At first, landslide locations were identified by aerial photographs and field surveys, and a total of 136 landside locations were constructed from various sources. Then the landslide inventory map was randomly split into a training dataset 70 % (95 landslide locations) for training the ANN model and the remaining 30 % (41 landslides locations) was used for validation purpose. Nine landslide conditioning factors such as slope, slope aspect, altitude, land use, lithology, distance from rivers, distance from roads, distance from faults, and rainfall were constructed in geographical information system. In this study, both MLP and RBF algorithms were used in artificial neural network model. The results showed that MLP with Broyden–Fletcher–Goldfarb–Shanno learning algorithm is more efficient than RBF in landslide susceptibility mapping for the study area. Finally the landslide susceptibility maps were validated using the validation data (i.e., 30 % landslide location data that was not used during the model construction) using area under the curve (AUC) method. The success rate curve showed that the area under the curve for RBF and MLP was 0.9085 (90.85 %) and 0.9193 (91.93 %) accuracy, respectively. Similarly, the validation result showed that the area under the curve for MLP and RBF models were 0.881 (88.1 %) and 0.8724 (87.24 %), respectively. The results of this study showed that landslide susceptibility mapping in the Vaz Watershed of Iran using the ANN approach is viable and can be used for land use planning.

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Summary

  • Landslide susceptibility and hazard assessments are the most important steps in landslide risk mapping.
  • The main objective of this study was to investigate and compare the results of two artificial neural network (ANN) algorithms, i.e., multilayer perceptron (MLP) and radial basic function (RBF) for spatial prediction of landslide susceptibility in Vaz Watershed, Iran.
  • At first, landslide locations were identified by aerial photographs and field surveys, and a total of 136 landside locations were constructed from various sources.
  • Then the landslide inventory map was randomly split into a training dataset 70 % (95 landslide locations) for training the ANN model and the remaining 30 % (41 landslides locations) was used for validation purpose.
  • Nine landslide conditioning factors such as slope, slope aspect, altitude, land use, lithology, distance from rivers, distance from roads, distance from faults, and rainfall were constructed in geographical information system.
  • Both MLP and RBF algorithms were used in artificial neural network model.
  • The results showed that MLP with Broyden–Fletcher–Goldfarb–Shanno learning algorithm is more efficient than RBF in landslide susceptibility mapping for the study area.
  • Finally the landslide susceptibility maps were validated using the validation data (i.e., 30 % landslide location data that was not used during the model construction) using area under the curve (AUC) method.
  • The results of this study showed that landslide susceptibility mapping in the Vaz Watershed of Iran using the ANN approach is viable and can be used for land use planning.
  • Landslide, Susceptibility, Artificial neural networks, Geographic Information Systems (GIS), Vaz Watershed, Iran, also known as Keyword.

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Landslide susceptibility mapping at VAZ watershed (Iran) using an artificial neural
network model: a comparison between multilayer perceptron (MLP) and radial basic
function (RBF) algorithms
ABSTRACT
Landslide susceptibility and hazard assessments are the most important steps in landslide risk
mapping. The main objective of this study was to investigate and compare the results of two
artificial neural network (ANN) algorithms, i.e., multilayer perceptron (MLP) and radial
basic function (RBF) for spatial prediction of landslide susceptibility in Vaz Watershed, Iran.
At first, landslide locations were identified by aerial photographs and field surveys, and a
total of 136 landside locations were constructed from various sources. Then the landslide
inventory map was randomly split into a training dataset 70 % (95 landslide locations) for
training the ANN model and the remaining 30 % (41 landslides locations) was used for
validation purpose. Nine landslide conditioning factors such as slope, slope aspect, altitude,
land use, lithology, distance from rivers, distance from roads, distance from faults, and
rainfall were constructed in geographical information system. In this study, both MLP and
RBF algorithms were used in artificial neural network model. The results showed that MLP
with Broyden–Fletcher–Goldfarb–Shanno learning algorithm is more efficient than RBF in
landslide susceptibility mapping for the study area. Finally the landslide susceptibility maps
were validated using the validation data (i.e., 30 % landslide location data that was not used
during the model construction) using area under the curve (AUC) method. The success rate
curve showed that the area under the curve for RBF and MLP was 0.9085 (90.85 %) and
0.9193 (91.93 %) accuracy, respectively. Similarly, the validation result showed that the area
under the curve for MLP and RBF models were 0.881 (88.1 %) and 0.8724 (87.24 %),
respectively. The results of this study showed that landslide susceptibility mapping in the Vaz
Watershed of Iran using the ANN approach is viable and can be used for land use planning.
Keyword:
Landslide, Susceptibility, Artificial neural networks, Geographic Information
Systems (GIS), Vaz Watershed, Iran
Citations
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Journal Article•DOI•
Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling–Narayanghat road section in Nepal Himalaya

[...]

Krishna Chandra Devkota1, Amar Deep Regmi2, Hamid Reza Pourghasemi3, Kohki Yoshida2, Biswajeet Pradhan4, In Chang Ryu1, Megh Raj Dhital5, Omar F. Althuwaynee4 •
Kyungpook National University1, Shinshu University2, Tarbiat Modares University3, Universiti Putra Malaysia4, Kathmandu5
01 Jan 2013-Natural Hazards
TL;DR: In this article, a landslide susceptibility assessment of Mugling-Narayanghat road and its surrounding area is made using bivariate (certainty factor and index of entropy) and multivariate (logistic regression) models.
Abstract: Landslide susceptibility maps are vital for disaster management and for planning development activities in the mountainous country like Nepal. In the present study, landslide susceptibility assessment of Mugling–Narayanghat road and its surrounding area is made using bivariate (certainty factor and index of entropy) and multivariate (logistic regression) models. At first, a landslide inventory map was prepared using earlier reports and aerial photographs as well as by carrying out field survey. As a result, 321 landslides were mapped and out of which 241 (75 %) were randomly selected for building landslide susceptibility models, while the remaining 80 (25 %) were used for validating the models. The effectiveness of landslide susceptibility assessment using GIS and statistics is based on appropriate selection of the factors which play a dominant role in slope stability. In this case study, the following landslide conditioning factors were evaluated: slope gradient; slope aspect; altitude; plan curvature; lithology; land use; distance from faults, rivers and roads; topographic wetness index; stream power index; and sediment transport index. These factors were prepared from topographic map, drainage map, road map, and the geological map. Finally, the validation of landslide susceptibility map was carried out using receiver operating characteristic (ROC) curves. The ROC plot estimation results showed that the susceptibility map using index of entropy model with AUC value of 0.9016 has highest prediction accuracy of 90.16 %. Similarly, the susceptibility maps produced using logistic regression model and certainty factor model showed 86.29 and 83.57 % of prediction accuracy, respectively. Furthermore, the ROC plot showed that the success rate of all the three models performed more than 80 % accuracy (i.e. 89.15 % for IOE model, 89.10 % for LR model and 87.21 % for CF model). Hence, it is concluded that all the models employed in this study showed reasonably good accuracy in predicting the landslide susceptibility of Mugling–Narayanghat road section. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose.

542 citations

Journal Article•DOI•
Hybrid integration of Multilayer Perceptron Neural Networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS

[...]

Binh Thai Pham1, Dieu Tien Bui2, Indra Prakash3, M. B. Dholakia1•
Gujarat Technological University1, University College of Southeast Norway2, Government of Gujarat3
01 Feb 2017-Catena
TL;DR: Analysis of results indicates that landslide models using machine learning ensemble frameworks are promising methods which can be used as alternatives of individual base classifiers for landslide susceptibility assessment of other prone areas.
Abstract: The main objective of this study is to evaluate and compare the performance of landslide models using machine learning ensemble technique for landslide susceptibility assessment. This technique is a combination of ensemble methods (AdaBoost, Bagging, Dagging, MultiBoost, Rotation Forest, and Random SubSpace) and the base classifier of Multiple Perceptron Neural Networks (MLP Neural Nets). Ensemble techniques have been widely applied in other fields; however, their application is still rare in the assessment of landslide problems. Meanwhile, MLP Neural Nets, which is known as an artificial neural network, has been applied widely and efficiently in landslide problems. In the present study, landslide models of part Himalayan area (India) have been constructed and validated. For the evaluation and comparison of these models, receiver operating characteristic curve and Chi Square test methods have been applied. Overall, all landslide models performed well in landslide susuceptibility assessment but the performance of the MultiBoost model is the highest (AUC = 0.886), followed by Dagging model (AUC = 0.885), the Rotation Forest model (AUC = 0.882), the Bagging and Random SubSpace models (AUC = 0.881), and the AdaBoost model (AUC = 0.876), respectively. Moreover, machine learning ensemble models have improved significantly the performance of the base classifier of MLP Neural Nets (AUC = 0.874). Analysis of results indicates that landslide models using machine learning ensemble frameworks are promising methods which can be used as alternatives of individual base classifiers for landslide susceptibility assessment of other prone areas.

436 citations

Journal Article•DOI•
Groundwater potential mapping at Kurdistan region of Iran using analytic hierarchy process and GIS

[...]

Omid Rahmati1, Aliakbar Nazari Samani1, Mohamad Mahdavi1, Hamid Reza Pourghasemi2, Hossein Zeinivand3 •
University of Tehran1, Shiraz University2, Lorestan University3
01 Sep 2015-Arabian Journal of Geosciences
TL;DR: In this article, a standard methodology has been applied to delineate groundwater resource potential zonation based on integrated analytical hierarchy process (AHP), geographic information system (GIS), and remote sensing (RS) techniques in Kurdistan plain, Iran.
Abstract: Multi-criteria decision analysis (MCDA) as an advantageous tool has been applied by various researchers to improve their management ability. Management of groundwater resource, especially under data-scarce and arid areas, encountered a lot of problems and issues which drives the planers to use of MCDA. In this research, a standard methodology has been applied to delineate groundwater resource potential zonation based on integrated analytical hierarchy process (AHP), geographic information system (GIS), and remote sensing (RS) techniques in Kurdistan plain, Iran. At first, the effective thematic layers on the groundwater potential such as rainfall, lithology, drainage density, lineament density, and slope percent were derived from the spatial geodatabase. Then, the assigned weights of thematic layers based on expert knowledge were normalized by eigenvector technique of AHP. To prepare the groundwater potential index, the weighted linear combination (WLC) method was applied in GIS. Finally, the receiver operating characteristic (ROC) curve was drawn for groundwater potential map, and the area under curve (AUC) was computed. Results indicated that the rainfall and slope percent factors have taken the highest and lowest weights, respectively. Validation of results showed that the AHP method (AUC = 73.66 %) performed fairly good predication accuracy. Such findings revealed that in the regions suffering from data scarcity through the MCDM methodology, the planners would be able to having accurate knowledge on groundwater resources based on geospatial data analysis. Therefore, the developing scenario for future planning of groundwater exploration can be achieved in an efficient manner.

389 citations

Additional excerpts

  • ...It is because of the quick access to data obtained through global positioning systems and RS techniques (Ganapuram et al. 2009; Zare et al. 2013)....

    [...]

Journal Article•DOI•
Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya

[...]

Amar Deep Regmi1, Krishna Chandra Devkota2, Kohki Yoshida1, Biswajeet Pradhan3, Hamid Reza Pourghasemi4, Takashi Kumamoto5, Aykut Akgün •
Shinshu University1, Kyungpook National University2, Universiti Putra Malaysia3, Tarbiat Modares University4, Okayama University5
01 Feb 2014-Arabian Journal of Geosciences
TL;DR: In this article, the authors investigated the application of the frequency ratio (FR), statistical index (SI), and weights-of-evidence (WoE) approaches for landslide susceptibility mapping of this road section and its surrounding area.
Abstract: The Mugling–Narayanghat road section falls within the Lesser Himalaya and Siwalik zones of Central Nepal Himalaya and is highly deformed by the presence of numerous faults and folds. Over the years, this road section and its surrounding area have experienced repeated landslide activities. For that reason, landslide susceptibility zonation is essential for roadside slope disaster management and for planning further development activities. The main goal of this study was to investigate the application of the frequency ratio (FR), statistical index (SI), and weights-of-evidence (WoE) approaches for landslide susceptibility mapping of this road section and its surrounding area. For this purpose, the input layers of the landslide conditioning factors were prepared in the first stage. A landslide inventory map was prepared using earlier reports, aerial photographs interpretation, and multiple field surveys. A total of 438 landslide locations were detected. Out these, 295 (67 %) landslides were randomly selected as training data for the modeling using FR, SI, and WoE models and the remaining 143 (33 %) were used for the validation purposes. The landslide conditioning factors considered for the study area are slope gradient, slope aspect, plan curvature, altitude, stream power index, topographic wetness index, lithology, land use, distance from faults, distance from rivers, and distance from highway. The results were validated using area under the curve (AUC) analysis. From the analysis, it is seen that the FR model with a success rate of 76.8 % and predictive accuracy of 75.4 % performs better than WoE (success rate, 75.6 %; predictive accuracy, 74.9 %) and SI (success rate, 75.5 %; predictive accuracy, 74.6 %) models. Overall, all the models showed almost similar results. The resultant susceptibility maps can be useful for general land use planning.

365 citations

Journal Article•DOI•
A comparative study of different machine learning methods for landslide susceptibility assessment

[...]

Binh Thai Pham, Biswajeet Pradhan1, Dieu Tien Bui2, Indra Prakash3, M. B. Dholakia4 •
Universiti Putra Malaysia1, Telemark University College2, Government of Gujarat3, Gujarat Technological University4
01 Oct 2016-Environmental Modelling and Software
TL;DR: Analysis and comparison of the results show that all five landslide models performed well for landslide susceptibility assessment, but it has been observed that the SVM model has the best performance in comparison to other landslide models.
Abstract: Landslide susceptibility assessment of Uttarakhand area of India has been done by applying five machine learning methods namely Support Vector Machines (SVM), Logistic Regression (LR), Fisher's Linear Discriminant Analysis (FLDA), Bayesian Network (BN), and Naive Bayes (NB). Performance of these methods has been evaluated using the ROC curve and statistical index based methods. Analysis and comparison of the results show that all five landslide models performed well for landslide susceptibility assessment (AUCź=ź0.910-0.950). However, it has been observed that the SVM model (AUCź=ź0.950) has the best performance in comparison to other landslide models, followed by the LR model (AUCź=ź0.922), the FLDA model (AUCź=ź0.921), the BN model (AUCź=ź0.915), and the NB model (AUCź=ź0.910), respectively. Machine learning methods namely SVM, LR, FLDA, BN, and NB have been evaluated and compared for landslide susceptibility assessment.Results indicate that all these five models can be applied efficiently for landslide assessment and prediction.Analysis of comparative results reaffirmed that the SVM model is one of the best methods.

363 citations

Cites background from "Landslide susceptibility mapping at..."

  • ...Zare et al. (2013), Pradhan and Lee (2010b), and Conforti et al. (2014) utilized artificial neural networks which are based on the biological neural networks to predict spatially landslide distributions....

    [...]

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  • ...Due to their nonlinear approximation properties, RBF networks are able to model complex mappings, which perceptron neural networks can model by means of multiple intermediary layers (Haykin 1994)....

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Abstract: A fairly complete range of slope movement processes are identified and classified according to features that are also to some degree relevant to their recognition, avoidance, control, or correction. The classification includes extremely slow distributed movements of both rock and soil (designated as creep in many classifications). The classification also includes the increasingly recognized overturning or toppling failures and spreading movements. Attention is also paid to movements due to freezing and thawing. Among the attributes that have been used as criteria for identification and classification are type of movement, kind of material, rate of movement, geometry of the area of failure and the resulting deposit, age, causes, degree of disruption of the displaced mass, relation or lack of relation of slide geometry to geologic structure, degree of development, geographic location of type examples, and state of activity. A discussion of the causes of sliding slope movements considers, factors that contribute to increased shear stress and factors that contribute low or reduced shear strength.

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Frequently Asked Questions (1)
Q1. What contributions have the authors mentioned in the paper "Landslide susceptibility mapping at vaz watershed (iran) using an artificial neural network model: a comparison between multilayer perceptron (mlp) and radial basic function (rbf) algorithms abstract landslide susceptibility and hazard assessments are the most important steps in landslide risk" ?

The main objective of this study was to investigate and compare the results of two artificial neural network ( ANN ) algorithms, i. e., multilayer perceptron ( MLP ) and radial basic function ( RBF ) for spatial prediction of landslide susceptibility in Vaz Watershed, Iran. In this study, both MLP and RBF algorithms were used in artificial neural network model. The results showed that MLP with Broyden–Fletcher–Goldfarb–Shanno learning algorithm is more efficient than RBF in landslide susceptibility mapping for the study area. The results of this study showed that landslide susceptibility mapping in the Vaz Watershed of Iran using the ANN approach is viable and can be used for land use planning.