Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 512158, 9 pages
http://dx.doi.org/10.1155/2015/512158

Research Article

Landslide Occurrence Prediction Using Trainable Cascade Forward Network and Multilayer Perceptron

Mohammad Subhi Al-batah,¹ Mutasem Sh. Alkhasawneh,² Lea Tien Tay,² Umi Kalthum Ngah,² Habibah Hj Lateh,³ and Nor Ashidi Mat Isa²

¹Department of Software Engineering, Faculty of Science and Information Technology, Jadara University, Irbid 2001, Jordan
²School of Electrical & Electronic Engineering, Universiti Sains Malaysia, Engineering Campus, 14300 Nibong Tebal, Penang, Malaysia
³School of Distance Education, Universiti Sains Malaysia, 11600 Penang, Malaysia

Received 28 September 2014; Revised 18 December 2014; Accepted 25 December 2014

Academic Editor: Yudong Zhang

Copyright © 2015 Mohammad Subhi Al-batah et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Landslides are one of the dangerous natural phenomena that hinder the development in Penang Island, Malaysia. Therefore, finding the reliable method to predict the occurrence of landslides is still the research of interest. In this paper, two models of artificial neural network, namely, Multilayer Perceptron (MLP) and Cascade Forward Neural Network (CFNN), are introduced to predict the landslide hazard map of Penang Island. These two models were tested and compared using eleven machine learning algorithms, that is, Levenberg Marquardt, Broyden Fletcher Goldfarb, Resilient Back Propagation, Scaled Conjugate Gradient, Conjugate Gradient with Beale, Conjugate Gradient with Fletcher Reeves updates, Conjugate Gradient with Polakribiere updates, One Step Secant, Gradient Descent, Gradient Descent with Momentum and Adaptive Learning Rate, and Gradient Descent with Momentum algorithm. Often, the performance of the landslide prediction depends on the input factors beside the prediction method. In this research work, 14 input factors were used. The prediction accuracies of networks were verified using the Area under the Curve method for the Receiver Operating Characteristics. The results indicated that the best prediction accuracy of 82.89% was achieved using the CFNN network with the Levenberg Marquardt learning algorithm for the training data set and 81.62% for the testing data set.

1. Introduction

Landslide hazard is a particular case of natural hazard which is defined as the probability of occurrence within a specified period of time and within a given area of a potentially damaging phenomenon [1, 2]. Numerous occurrences of landslides have caused lives to perish and incurred losses in terms of financial stakes, across the entire world annually. However, the main causes behind the occurrence of the landslides are still unspecified. Different factors such as geological, topographic, physical, and human causes (disregard for sustainable form of developments) contribute to landslide occurrences [3]. Therefore, many studies have been conducted and different techniques have been applied to predict the occurrence of landslides. These techniques involve variations and mixtures in approaches, from logical, experience-based analyses, extending to complex mathematical and computer based system.

Over the last two decades, a keen interest has been shown in the application of artificial neural networks (ANNs). It has been widely applied in forecasting, decision making, food industry, agriculture sector, and many other different applications [4–7]. The popularity of ANNs is due in part to their computational simplicity, finite parameterization, and stability. Different ANNs architecture such as the MLP, radial basis function (RBF), and recurrent neural networks (RNN) have been proposed in the literature [8]. Amongst all these models, the most commonly and widely used model for landslide is the MLP model [9, 10].

ANNs are one of the techniques which produce good accuracy when used to predict the occurrences of landslides [11]. However, an ideal method for predicting landslide occurrence has not been agreed upon yet [12]. Therefore, an intelligent computer system is proposed to enable automatic prediction of landslide using MLP and CFNN in ANNs.

Penang Island is being subject of interest for many studies. Pradhan (2010) produced a landslide hazard map for Penang Island using MLP neural network. Five training sites from Penang island and nine different factors involved in their analysis include slope angle, slope aspect, curvature, distance from drainage, distance from lineament, geology, land cover, soil, and rain precipitation [13].

Pradhan et al. (2010) also investigated the possible application of an artificial neural network model and its cross-application of weights at three study areas in Malaysia, namely, Penang Island, Cameron Highlands, and Selangor. The weight of each factor was calculated. The factors are, namely, slope angle, slope aspect, plan curvature, altitude, stream power index, wetness index, distance from drainage, distance from road, distance from faults, geology, land use, soil texture, soil material, vegetation index, and topography. The results show that case of the weight using the same test area showed slightly higher accuracy than the weight used for the cross-applied area [10].

Lim et al. (2011) used probabilistic methods such as frequency ratio, statistical index, certainty factor and landslide susceptibility analysis, and logistic regression to produce landslide hazard maps for Penang Island. In their study, twelve factors including four topographic factors were used. The importance of the input factors was not estimated in their study [14].

Oh and Pradhan (2011) applied adaptive neurofuzzy inference system (ANFIS) with seven factors: altitude, slope angle, plan curvature, distance from drainage, distance from road, soil texture, and stream power index on an area of Penang Island covering only 8.064 km² of Penang Island [15].

Pang et al. (2012) used Decision Tree (DT) to produce landslide hazard mapping for Penang Island with the same twelve factors, used by [14]. DT model was calculated and constructed using the DT algorithm. The use of DT method improves the landslide hazard map where the percentage of past landslide event increases at three risk levels, that is, most hazardous, hazardous, and moderate, while the percentage is reduced in the nonhazardous level [16].

Digital Elevation Model has been generally used as the basic source for extracting the topographic factors such as slope aspect and curvatures. It also is one of the core database sources for several GIS applications [17]. For this study the DEM with 5 meter/pixel resolution was used to extract the slope angle, slope aspect, profile curvature, plan curvature, and general curvature.

Fourteen factors were used as the input features for the MLP and CFNN. These factors are slope angles, slope aspect, profile curvature, plan curvature, general curvature, distance from the road, distance from the fault lines, elevation, distance from the drainages, soil texture, land cover, vegetation cover, geology, and the rain precipitation as a triggering factor. The MLP and CFNN were trained with eleven learning algorithms to produce the most accurate prediction results. The 11 learning algorithms used were Levenberg Marquardt (LM), Broyden Fletcher Goldfarb (BFG), Resilient Back Propagation (Rp), Scaled Conjugate Gradient (SCG), Conjugate Gradient with Beale (CGB), Conjugate Gradient with Fletcher Reeves updates (CGF), Conjugate Gradient with Polakribiere updates (CGP), One Step Secant (OSS), Gradient Descent (GD), Gradient descent with momentum and adaptive learning rate (GDX) and Gradient Descent with Momentum (GDM) algorithms.

The organization of this paper is as follows. The CFNN and MLP are explained as landslide prediction methods in Section 2. Section 3 introduces the study area and provides descriptions on the data collection and factor extraction. Results of the prediction performance are presented in Section 4. Conclusion is drawn in Section 5. Figure 1 describes the methodology for this work.

Figure 1: Flow chart of the work methodology.

2. MLP and CFNN

The popularity of the MLP and CFNN comes from their stability, simplicity of application, and smaller structure size for a particular problem, as compared to the other structures [18]. The network learns the relationship between pairs of factors (inputs) and output (responses) vectors by altering the weight and bias values [19]. Figure 2 shows an example of a standard MLP and CFNN. It consists of three layers in the order of input, hidden, and output layer. Each layer consists of independent processing units called neurons [20].

Figure 2: MLP and CFNN.

These neurons receive inputs; each input value is multiplied by the weight (the strength of the input). The input is computed using a mathematical function that determines the activation values of the neuron and is then passed to the next layer. The output from the hidden layer is given by

where is the output from hidden layer and and denote the inputs and the weight from input to hidden unit in the first layer, respectively. is the bias for hidden unit and is the transfer function. For the hidden layer, the tan sigmoid function was used.

The predicted output of the th node in the output layer is denoted as which can be expressed as in

where denotes the weights from the hidden layer to the output layer. denotes the number of outputs neurons. Combining (1) and (2), the complete representation of the output for the MLP network is obtained as in

CFNN network shares the same structure and the work methodology with MLP network. However, CFNN includes a weight connection from the input to the output layer and from each layer to the successive layers [21]. As shown in Figure 2, for the CFNN network with output nodes, hidden nodes, and input nodes, the output of the th neuron, in the output layer is given by

Because of the fact that neural networks have numerous numbers of neurons, adjusting of the neural weights without a learning algorithm may be quite difficult. For that, various learning algorithms have been developed and established for two reasons: minimizing the error rate between the actual output and the output results and building up the weights, and , for the inputs factors [22, 23]. In this paper, MLP and CFNN were trained with eleven learning algorithms. Detailed descriptions on the learning algorithms can be found in [24]. Both MLP and CFNN with different learning algorithms are assessed based on their performance in producing landslide hazard map of Penang Island.

3. Data Collection and Preparation for the Neural Network

For this work, 14 factors were investigated and analyzed. The data for 14 factors were collected and extracted for the study area. This study is focused on Penang Island which is shown in Figure 3. Study area lies between 5°15′ and 5°30′N latitude and 100°10′ and 100°20′E longitude. It occupies an area of 285 km² and is one of the thirteen states of Malaysia. The island is bounded to the north and east by the state of Kedah, to the south by the state of Perak, and to the west by the Straits of Malacca and Sumatra (Indonesia). It consists of both the island of Penang and a coastal strip on the mainland which is known as Province Wellesley. The island of Penang is the study area in this research work. Penang Island experiences frequent landslides, which occur quite frequently during the rainy seasons [14, 15, 25]. Penang Island has a tropical climate with high temperatures of 29°C to 32°C and humidity ranging from 65% to 96%. Topographic elevations vary between 0 m and 820 m above sea level, and the slope angle ranges from 0° to 87°. Flat lands make up 43.28% of the island. Geological data from the Department of Mineral and Geosciences show that Feringgi granite, Batu Maung granite, clay, and sand granite represent more than 72% of the study area’s geology. Vegetation cover consists mainly of forests and fruit plantations.

Figure 3: Penang Island (source: Google maps).

Data collection on the geographical database of Penang Island was obtained through various government agencies. Factors such as geology, road, fault lines, elevation, drainage, soil texture, land cover, vegetation cover, and rain precipitation maps were obtained from Department of Survey and Mapping Malaysia (JUPEM), Department of Agriculture Malaysia (DOA), Department of Minerals and geosciences Malaysia (JMG), Geographic information System Center of Penang (Pusat PeGIS), Malaysian Meteorological Department (MMD), and Department of Irrigation and Drainage Malaysia (JPS). Topographic factors which include slope angle, slope aspect, profile curvature, plan curvature, and general curvature were extracted from the elevation data [16]. Landslide occurrence locations were also collected and determined. The range of each factor and the ratio of occurrence on the study area are shown in Table 1.

Table 1: Landslide causative factor’s ranges and ratio.

Factors
Class
Area (pixels)
Ratio

Total area
Landslide occurrence area
Total area
Landslide occurrence

Elevation (meter)
0–79
6641099
10902
55.2%
15.85%

80–159
1390848
13026
11.56%
18.94%

160–239
1186046
12181
9.86%
17.71%

240–319
882426
9687
7.33%
14.08%

320–399
608281
8491
5.06%
12.34%

400–479
449927
6900
3.74%
10.03%

480–559
391076
4461
3.25%
6.49%

>560
481490
3138
4%
4.56%

Slope angle (degree)
0–7
6318295
6100
52.52%
8.87%

8–15
1515746
12512
12.6%
18.19%

16–23
2003138
19769
16.65%
28.74%

24–31
1490172
19504
12.39%
28.35%

32–39
554603
8537
4.61%
12.41%

40–47
121293
1825
1.01%
2.65%

>48
539
539
0.23%
0.78%

Slope aspect (degree)
North
811040
7819
6.74%
11.37%

North-East
980909
11490
8.15%
16.70%

East
996431
9081
8.28%
13.20%

South-East
723126
7515
6.01%
10.93%

South
796410
7164
6.62%
10.41%

South-West
903353
9771
7.51%
14.20%

West
958631
8657
7.97%
12.59%

North-west
654463
5217
5.44%
7.58%

Flat
52066830
2072
43.28%
3.01%

General curvature
Convex
2992360
35717
24.86%
51.92%

Concave
3148149
26894
26.17%
39.10%

Flat
5890684
6175
48.97%
8.98%

Profile curvature
Convex
3098090
26030
25.75%
37.84%

Concave
2886983
36014
23.99%
52.35%

Flat
6046120
6742
50.99
9.80%

Plan curvature
Convex
361329
31779
30.18%
46.19%

Concave
2594977
31281
21.56%
45.47%

Flat
5804887
5726
48.26%
8.30%

Land cover
Forest, bush, swam
6112837
52195
50.81%
75.88%

Vegetation
1617410
10951
13.44%
15.92%

Transport
894789
2288
7.44%
3.33%

Settlement
1476907
404
12.28%
0.59%

Cemetery
138389
1442
1.15%
2.1%

Mining
31931
0
0.27%
0.0%

Industry
192485
13
1.6%
0.019%

Government
institution
156625
177
1.3%
0.26%

Public facility
217492
155
1.18%
0.23%

Plains, hills
303211
501
2.51%
0.73%

Buildings
24327
0
0.2%
0%

Religious area
41945
159
0.35%
0.23%

Business
205595
0
1.71%
0%

Sea, lake, river
267665
430
2.22%
0.63%

Public utility
56358
71
0.47%
0.1%

Livestock
81141
0
0.67%
0%

Education
212085
0
1.76%
0%

Vegetation cover
Forest, plant, Bush
5441433
51148
45.23%
74.36%

Swamp
412778
960
3.43%
1.4%

Mixed farms
258984
80
2.15%
0.12%

Fruit farm
145514
1683
1.21%
2.45%

Oil farm
968301
9023
8.05%
13.12%

Sugarcane
176678
0
1.47%
0%

Vegetable
156
0
0.0013%
0%

Farm
55599
87
0.46%
0.13%

Coconut
79156
0
0.66%
0.0%

Pineapple
81
0
0.00067%
0.0%

Paddy
126263
0
1.05%
0.0%

Rubber
2241
0
0.019%
0.0%

Others
65338
208
0.54%
0.3%

None
4298671
5597
35.73%
8.14%

Distance from fault line
0–100
641379
7164
5.33%
10.41%

101–200
651750
3427
5.42%
4.98%

201–300
643860
1939
5.35%
2.82%

301–400
637145
4272
5.30%
6.21%

401–500
630562
5715
5.24%
8.31%

501–600
611528
4756
5.08%
6.92%

601–700
588891
5671
4.89%
8.24%

701–800
549700
4855
4.57%
7.06%

801–900
511179
6090
4.25%
8.85%

9001–1000
457995
3593
3.81%
5.22%

>1000
6107204
21298
50.76%
30.96%

Distance from road
0–49
3807202
5863
31.64%
8.52%

50–99
1044665
2056
8.68%
2.99%

100–149
748374
3324
6.22%
4.83%

150–199
598005
3053
4.97%
4.44%

200–249
504546
3267
4.19%
4.75%

250–299
440547
3368
3.66%
4.9%

300–349
389709
3787
3.24%
5.51%

350–399
354222
4152
2.94%
6.04%

400–449
328068
2805
2.73%
4.08%

>450
3815855
37111
31.72%
53.95%

Distance from drainage
0–49
4229076
41931
35.15%
60.96%

50–99
2214851
11711
18.41%
17.03%

100–149
1497866
6864
12.45%
9.98%

150–199
980562
3980
8.15%
5.79%

200–249
643367
1943
5.35%
2.82%

250–299
455199
1003
3.78%
1.46%

300–349
344950
586
2.87%
0.85%

350–399
274824
517
2.28%
0.75%

400–449
224731
205
1.87%
0.30%

>450
1165767
46
9.69%
0.07%

Geology
Muka head microgranite
174024
1431
1.45%
2.08%

Feringgi, granite
2343321
22931
19.48%
33.34%

Tanjung
Bunga granite
2751458
19292
22.87%
28.05%

Sungai area
granite
318909
500
2.65%
0.73%

Batu Maung
granite
3359896
24365
27.93%
35.42%

Clay, sand, granite
3083585
267
25.63
0.39%

Soil texture
Sand
133506
0
1.11%
0.00%

Sandy clay
443859
505
3.69%
0.74%

Loam
6535957
67126
54.33%
97.59%

Sandy loam
1606303
790
13.35
1.15%

Silty clay
332707
55
2.77%
0.08%

Urban land
2978861
309
24.76%
0.45%

Rain precipitation
2254–2319.8
85398
849
0.71%
1.23%

2319.9–2379.3
549900
1571
4.57%
2.28%

2379.4–2433.2
1489192
10850
12.38%
15.77%

2433.3–2481.9
2815573
16632
23.4%
24.18%

2481.0–2535.8
3202204
6815
26.62%
9.91%

2535.9–2595.8
3003842
22832
24.97%
33.19%

2595.9–2661.1
559762
6617
4.65%
9.61%

2661.2–2722.8
227570
1755
1.89%
2.55%

2733.9–2903
97752
865
0.81%
1.26%

The data is prepared for the neural network, including training data set and testing data set. The data were normalized to range between 0 and 1, for each of the factors individually based on

where the sample is the sample to be normalized and is the minimum or the maximum sample value for every single input factor. The neural network outputs are represented by an output of 1 for landslide and 0 for no landslide. An effective neural network requires a comprehensive trained data set. Therefore 137572 data samples were selected from each factor in this analysis, where 68786 samples represent landslides and 68786 samples represent no landslides. Two-thirds of the data (91715) were used for training and the remaining one-third (45857) was used for testing. The two neural networks, MLP and CFNN, were trained using the Matlab software.

To determine the network parameters, the experiments were carried out by varying the number of hidden neurons from 1 to 100. For each number of hidden neuron, the network was trained by varying the number of epochs from 1 to 1000. The purpose was to find the number of epoch that produced the best generalization for each number of hidden neuron. The optimum epoch and hidden neuron, which produced the minimum value of mean squared error for the testing set, was noted and its prediction accuracy was determined.

4. Results and Discussions

Two characteristics were used for the neural network performance analysis, which are the accuracy and the MSE. Model validation using MSE is tested by calculating the mean squared errors after each epoch. The MSE is defined as the average squared error between the actual output and the predicted output. The MSE at every epoch is given by

where and are the actual output and the predicted output for a given set of estimated parameters after epochs, respectively, and is the number of data that were used to calculate the MSE.

To verify the accuracy of each model, the Receiver Operating Characteristics (ROC) method was used and the Area Under the Curve method (AUC) was calculated for all the models. AUC is one of the popular accepted methods for models prediction in natural hazard and the extracted AUC becomes the value of the accuracy.

The ROC plots the false positive rate on the -axis and the false negative rate on the -axis. The plot shows the tradeoff between the two rates, where AUC is one of the indicators computed based on ROC. In addition to that, the AUC explains the accuracy of the model in predicting landslides. In general, the lowest value of AUC is 0.5, which means that the model does not predict any better than a random approach.

Table 2 shows the testing performance for the training data sets that were achieved from the standard MLP and CFN, using the eleven different learning algorithms based on the 14 input factors. Based on the results in Table 2, it can be clearly seen that the performance values vary considerably across the model of the neural network and the learning algorithms. The best performance achieved was obtained through the CFNN model with LM learning algorithm. The accuracy is 82.89% with MSE of 0.0620. The worst accuracy of 71.15% and MSE of 0.1839 was obtained through MLP model with GDM learning algorithm.

Table 2: The training accuracy (%) and MSE for MLP and CFNN using the eleven learning algorithms with data set.

Network type
MLP
CFNN

Learning algorithm
Accuracy
MSE
Hidden nodes
Epoch
Accuracy
MSE
Hidden nodes
Epoch

LM
81.57
0.0910
82
22
82.89
0.0620
72
33

BFG
81.35
0.1150
60
191
79.68
0. 1326
68
152

Rpro
77.62
0.1535
52
274
77.73
0.1290
89
190

SCG
77.22
0.1548
81
178
77.19
0.1280
142
64

CGB
77.28
0.1534
80
147
78.14
0.1258
72
231

CGF
75.75
0.1625
31
134
76.76
0.1334
23
127

CGP
76.54
0.1578
55
144
76.28
0.1370
82
148

OSS
76.13
0.1624
49
168
74.98
0.1422
18
147

GD
71.23
0.1836
80
989
71.24
0.1607
82
968

GDX
72.34
0.1791
19
140
72.27
0.1590
25
122

GDM
71.15
0.1839
87
999
71.24
0.1606
89
977

The best accuracy rate obtained using MLP was achieved by the LM algorithm, that is, accuracy of 81.57% with MSE of 0.0910. On the contrary, the worst accuracy was 71.15% with MSE of 0.1839 achieved by the GDM algorithm. For CFNN, the worst performance was achieved using the GD algorithm with 71.24% with MSE of 0.1607. Meanwhile, the LM algorithm showed the best accuracy regardless of the neural network model. The LM learning algorithm achieved the best accuracy of 82.89%, with MSE of 0.0620 and 81.57% with MSE of 0.0910 for CFNN and MLP, respectively. On the other hand, GDM algorithm has the worst results in MLP neural network, whereas GD algorithm has the worst results in CFNN.

Overall, CFNN model achieved better accuracy and MSE as compared to MLP model, using six learning algorithms, that is, LM, Rprop, CGB, CGF, GD, and GDM, while for learning algorithms including BFG, SCG, OSS, CGP, and GDX, the MLP achieved better accuracy compared to CFNN.

In Table 3, the testing data sample was tested by using the same networks parameters. As expected the test accuracy result followed the training accuracy result where the CFNN with LM training algorithm achieved the best accuracy. Figures 4, 5, 6, and 7 show the ROC of CFNN and MLP with 11 learning algorithms applied on the testing data set.

Table 3: Accuracy obtained using testing data set.

Network type
MLP
CFNN

Learning algorithm
Testing data accuracy
Testing data accuracy

LM
81.11
81.62

BFG
79.69
75.80

Rprop
75.61
77.93

SCG
75.58
76.34

CGB
75.52
75.81

CGF
74.47
75.16

CGP
74.96
74.82

OSS
74.37
73.87

GD
71.04
71.17

GDX
71.87
71.84

GDM
70.90
70.91

Figure 4: ROC curve for MLP neural network trained with LM, BFG, Rp, GD, and SCG.

Figure 5: ROC curve for MLP neural network trained with CGF, CGP, OSS, GD, GDX, and SCG.

Figure 6: ROC curve for CF neural network trained with LM, BFG, Rp, SCG, and CGB.

Figure 7: ROC curve for CF neural network trained with CGF, CGP, OSS, GD, GDX, and SCG.

5. Conclusion

In this paper, fourteen suitable factors were collected and applied as input factors for ANN models. Two efficient neural network models, MLP and CFNN, are proposed and compared using eleven learning algorithms. The 14 factors show a good performance in predicting the landslide occurrence of Penang Island with accuracy up to 81.62%. The comparison results show that the CFNN network trained with LM can successfully be adopted for prediction of the landslide with significantly high performance. Moreover, applying the CFNN for prediction of the landslide on different study areas could be subject of interest in the future work.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgments

This research was supported by Ministry of Education, Malaysia (Grant no. 203/PJJAUH/6711279), Japan Science and Technology Agency (JST)/Japan International Cooperation Agency (JICA), and Science and Technology Research Partnership for Sustainable Development (SATREPS). The authors would also like to thank the JKR, JPS, JMG, and PEGIS, Malaysia, for the data used in this research.

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