Landslide Hazard Analysis Using a Multilayered Approach Based on Various Input Data Configurations
Abstract
Landslide is a natural disaster that occurs mostly in hill areas. Landslide hazard mapping is used to classify the prone areas to mitigate the risk of landslide hazards. This paper aims to compare spatial landslide prediction performance using an artificial neural network (ANN) model based on different data input configurations, different numbers of hidden neurons, and two types of normalization techniques on the data set of Penang Island, Malaysia. The data set involves twelve landslide influencing factors in which five factors are in continuous values, while the remaining seven are in categorical/discrete values. These factors are considered in three different configurations, i.e., original (OR), frequency ratio (FR), and mixed-type (MT) data, which act as an input to train the ANN model separately. A significant effect on the final output is the number of hidden neurons in the hidden layer. In addition, three data configurations are processed using two different normalization methods, i.e., mean-standard deviation (Mean-SD) and Min-Max. The landslide causative data often consist of correlated information caused by overlapping of input instances. Therefore, the principal component analysis (PCA) technique is used to eliminate the correlated information. The area under the receiver of characteristics (ROC) curve, i.e., AUC is also applied to verify the produced landslide hazard maps. The best result of AUC for both Mean-SD and Min-Max with PCA schemes are 96.72% and 96.38%, respectively. The results show that Mean-SD with PCA of MT data configuration yields the best validation accuracy, AUC, and lowest AIC at 100 number of hidden neurons. MT data configuration with the Mean-SD normalization and PCA scheme is more robust and stable in the MLP model's training for landslide prediction.
Keywords: Landslide; ANN; Hidden Neurons; Normalization; PCA; ROC; Hazard map
Copyright (c) 2021 Geosfera Indonesia and Department of Geography Education, University of Jember
This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License
References
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Begueria, S. (2006). Validation and Evaluation of Predictive Models in Hazard Assessment and Risk Management. Natural Hazards, 37(3), 315–329. https://doi.org/10.1007/s11069-005-5182-6.
Bui, D. T., Tsangaratos, P., Nguyen, V. T., Liem, N. V., & Trinh, P. T. (2020). Comparing the prediction performance of a Deep Learning Neural Network model with conventional machine learning models in landslide susceptibility assessment. Catena, 188(C), 104426. https://doi.org/10.1016/j.catena.2019.104426.
Bui, K. T. T., Tien Bui, D., Zou, J., Van Doan, C., & Revhaug, I. (2018). A novel hybrid artificial intelligent approach based on neural fuzzy inference model and particle swarm optimization for horizontal displacement modeling of hydropower dam. Neural Computing and Applications, 29(12), 1495–1506. https://doi.org/10.1007/s00521-016-2666-0.
Catani, F., Lagomarsino, D., Segoni, S., & Tofani, V. (2013). Landslide susceptibility estimation by random forests technique: Sensitivity and scaling issues. Natural Hazards and Earth System Sciences, 13(11), 2815–2831. https://doi.org/10.5194/nhess-13-2815-2013.
Chen, W., Fan, L., Li, C., & Pham, B. T. (2020). Spatial prediction of landslides using hybrid integration of artificial intelligence algorithms with frequency ratio and index of entropy in Nanzheng county, China. Applied Sciences (Basel), 10(1), 29. https://doi.org/10.3390/app10010029.
Chung, C. J. F., & Fabbri, A. G (1999). Probabilistic prediction models for landslide hazard mapping. Photogrammetric Engineering and Remote Sensing, 65(12), 1389–1399.
Cousineau, D., & Allan, T. (2015). Likelihood and its use in Parameter Estimation and Model Comparison. Mesure et Évaluation En Éducation, 37(3), 63–98. https://doi.org/10.7202/1036328ar.
Cruden, D. M. (1991). A simple definition of a landslide. Bulletin of the International Association of Engineering Geology - Bulletin de l’Association Internationale de Géologie de l’Ingénieur, 43(1), 27–29.
de Oliveira, G. G., Ruiz, L. F. C., Guasselli, L. A., & Haetinger, C. (2019). Random forest and artificial neural networks in landslide susceptibility modeling: a case study of the Fão River Basin, Southern Brazil. Natural Hazards, 99(2), 1049–1073. https://doi.org/10.1007/s11069-019-03795-x.
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Garrett, J. H. (1994). Where and Why Artificial Neural Networks Are Applicable in Civil Engineering. Journal of Computing in Civil Engineering, 8(2), 129–130.
Gian Quoc, A., Duc-Tan, T., Nguyen Dinh, C., & Tien Bui, D. (2018). Flexible configuration of wireless sensor network for monitoring of rainfall-induced landslide. Indonesian Journal of Electrical Engineering and Computer Science, 12(3), 1030–1036. https://doi.org/10.11591/ijeecs.v12.i3.pp1030-1036.
Guzzetti, F., Carrara, A., Cardinali, M., & Reichenbach, P. (1999). Landslide hazard evaluation: A review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology (Amsterdam), 31(1–4), 181–216.
Guzzetti, F., Mondini, A. C., Cardinali, M., Fiorucci, F., Santangelo, M., & Chang, K. T. (2012). Landslide inventory maps: New tools for an old problem. Earth - Science Reviews, 112(112), 42–66. https://doi.org/10.1016/j.earscirev.2012.02.001.
Haykin, S., & Lippmann, R. (1994). Neural networks, a comprehensive foundation. International journal of neural systems, 5(4), 363-364.
Huqqani, I. A., Tay, L. T., & Saleh, J. M. (2019). Analysis of landslide hazard mapping of Penang island Malaysia using bivariate statistical methods. Indonesian Journal of Electrical Engineering and Computer Science, 16(2), 781–786. https://doi.org/10.11591/ijeecs.v16.i2.pp781-786.
Husin, N. A., Salim, N., & Ahmad, A. R. (2008). Modeling of dengue outbreak prediction in Malaysia: A comparison of neural network and nonlinear regression model. 2008 International Symposium on Information Technology, 1–4. https://doi.org/10.1109/ITSIM.2008.4632022.
Hutchinson, J. N. (1995). Landslide Hazard Assessment. Keynote Paper. In: Bell, D.H., Ed., Landslides. 6th International Symposium on Landslides, 1805–1841.
Kaur, N., & Kumar, K. (2016). Normalization Based K-means Data Analysis Algorithm. International Journal of Advanced Research in Computer Science and Software Engineering, ISSN, 2277, 455-457.
Kawabata, D., & Bandibas, J. (2009). Landslide susceptibility mapping using geological data, a DEM from ASTER images and an Artificial Neural Network (ANN). Geomorphology (Amsterdam), 113(1–2), 97–109. https://doi.org/10.1016/j.geomorph.2009.06.006.
Kotsiantis, S. B., Kanellopoulos, D., & Pintelas, P. E. (2006). Data Preprocessing for Supervised Learning. International Journal of Computer Science, 1, 111–117.
Lee, D. H., Kim, Y. T., & Lee, S. R. (2020). Shallow landslide susceptibility models based on artificial neural networks considering the factor selection method and various non-linear activation functions. Remote Sensing, 12(7), 1194. https://doi.org/10.3390/rs12071194.
Lee, S., & Talib, J. A. (2005). Probabilistic landslide susceptibility and factor effect analysis. Environmental Earth Sciences, 47(7), 982–990. https://doi.org/10.1007/s00254-005-1228-z.
Liu, L., Li, S., Li, X., Jiang, Y., Wei, W., Wang, Z., & Bai, Y. (2019). An integrated approach for landslide susceptibility mapping by considering spatial correlation and fractal distribution of clustered landslide data. Landslides, 16(4), 715–728. https://doi.org/10.1007/s10346-018-01122-2.
Lombardo, L., & Mai, P. M. (2018). Presenting logistic regression-based landslide susceptibility results. Engineering Geology, 244(C), 14–24. https://doi.org/10.1016/j.enggeo.2018.07.019.
Murakami, S., Tien, T. L., Omar, R. B. C., Nishigaya, T., Aziza, N., Roslan, R., ... & Sakai, N. (2014). Landslides Hazard map in Malay peninsula by using historical landslide database and related information. J Civil Eng Res, 4(3A), 54-58.
Nhu, V. H., Shirzadi, A., Shahabi, H., Singh, S. K., Al-Ansari, N., Clague, J. J., … Ahmad, B. B. (2020). Shallow landslide susceptibility mapping: A comparison between logistic model tree, logistic regression, naïve bayes tree, artificial neural network, and support vector machine algorithms. International Journal of Environmental Research and Public Health, 17(8), 1–30. https://doi.org/10.3390/ijerph17082749.
Ortiz, J. A. V., & Martínez-Graña, A. M. (2018). A neural network model applied to landslide susceptibility analysis (Capitanejo, Colombia). Geomatics, Natural Hazards and Risk, 9(1), 1106–1128. https://doi.org/10.1080/19475705.2018.1513083.
Panchal, G., Ganatra, A., Kosta, Y. P., & Panchal, D. (2010). Searching most efficient neural network architecture using Akaike’s information criterion (AIC). International Journal of Computer Applications, 1(5), 41-44.
Paola, J. D., & Schowengerdt, R. A. (1995). A review and analysis of backpropagation neural networks for classification of remotely-sensed multi-spectral imagery. International Journal of Remote Sensing, 16(16), 3033–3058. https://doi.org/10.1080/01431169508954607.
Pradhan, B., & Lee, S. (2010). Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models. Environmental Earth Sciences, 60(5), 1037–1054. https://doi.org/10.1007/s12665-009-0245-8.
Regmi, A. D., Devkota, K. C., Yoshida, K., Pradhan, B., Pourghasemi, H. R., Kumamoto, T., & Akgun, A. (2014). Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya. Arabian Journal of Geosciences, 7(2), 725–742. https://doi.org/10.1007/s12517-012-0807-z.
Sahana, M., Pham, B. T., Shukla, M., Costache, R., Thu, D. X., Chakrabortty, R., … Prakash, I. (2020). Rainfall induced landslide susceptibility mapping using novel hybrid soft computing methods based on multi-layer perceptron neural network classifier. Geocarto International, 1–25. https://doi.org/10.1080/10106049.2020.1837262.
Scaioni, M., Longoni, L., Melillo, V., & Papini, M. (2014). Remote sensing for landslide investigations: An overview of recent achievements and perspectives. Remote Sensing, 6(10), 9600–9652. https://doi.org/10.3390/rs6109600.
Shahri, A. S., Spross, J., Johansson, F., & Larsson, S. (2019). Landslide susceptibility hazard map in southwest Sweden using artificial neural network. Catena, 183(C), 104225. https://doi.org/10.1016/j.catena.2019.104225.
Sheela, K. G., & Deepa, S. N. (2013). Review on methods to fix number of hidden neurons in neural networks. Mathematical Problems in Engineering, 2013, 1–11. https://doi.org/10.1155/2013/425740.
Sun, X., Chen, J., Bao, Y., Han, X., Zhan, J., & Peng, W. (2018). Landslide susceptibility mapping using logistic regression analysis along the Jinsha river and its tributaries close to Derong and Deqin County, southwestern China. ISPRS International Journal of Geo-Information, 7(11), 438. https://doi.org/10.3390/ijgi7110438.
Tay, L. T., Alkhasawneh, M. S., Ngah, U. K., & Lateh, H. (2014). Landslide hazard mapping of Penang Island using dominant factors. 2014 IEEE 2nd International Symposium on Telecommunication Technologies (ISTT), 154–158. https://doi.org/10.1109/ISTT.2014.7238195.
Tien Bui, D., Hoang, N. D., Martínez-Álvarez, F., Ngo, P. T. T., Hoa, P. V., Pham, T. D., … Costache, R. (2020). A novel deep learning neural network approach for predicting flash flood susceptibility: A case study at a high frequency tropical storm area. Science of the Total Environment, 701(C), 134413. https://doi.org/10.1016/j.scitotenv.2019.134413.
Van Westen, C. J., Rengers, N., & Soeters, R. (2003). Use of geomorphological information in indirect landslide susceptibility assessment. Natural Hazards, 30(3), 399–419. https://doi.org/10.1023/B:NHAZ.0000007097.42735.9e.
Van Westen, C. J. (1993). Application of geographic information systems to landslide hazard zonation. International Institute for Geo-Information Science and Earth Observation. http://www.itc.nl/library/Papers_1993/phd/vanwesten.pdf.
Varnes, D. (1984). Landslide hazard zonation : A review of principles and practice. Natural Hazards, (3) .
Ya’acob, N., Tajudin, N., & Azize, A. M. (2019). Rainfall-landslide early warning system (RLEWS) using TRMM precipitation estimates. Indonesian Journal of Electrical Engineering and Computer Science, 13(3), 1259–1266. https://doi.org/10.11591/ijeecs.v13.i3.pp1259-1266.
Alkhasawneh, M. S., Ngah, U. K., Tay, L. T., & Isa, N. A. M. (2014). Determination of importance for comprehensive topographic factors on landslide hazard mapping using artificial neural network. Environmental Earth Sciences, 72(3), 787–799. https://doi.org/10.1007/s12665-013-3003-x.
Alkhasawneh, M. S., Ngah, U. K., Tay, L. T., Mat Isa, N. A., & Al-Batah, M. S. (2013). Determination of important topographic factors for landslide mapping analysis using MLP network. The Scientific World Journal, 2013(415023), 1-12. https://doi.org/10.1155/2013/415023.
Begueria, S. (2006). Validation and Evaluation of Predictive Models in Hazard Assessment and Risk Management. Natural Hazards, 37(3), 315–329. https://doi.org/10.1007/s11069-005-5182-6.
Bui, D. T., Tsangaratos, P., Nguyen, V. T., Liem, N. V., & Trinh, P. T. (2020). Comparing the prediction performance of a Deep Learning Neural Network model with conventional machine learning models in landslide susceptibility assessment. Catena, 188(C), 104426. https://doi.org/10.1016/j.catena.2019.104426.
Bui, K. T. T., Tien Bui, D., Zou, J., Van Doan, C., & Revhaug, I. (2018). A novel hybrid artificial intelligent approach based on neural fuzzy inference model and particle swarm optimization for horizontal displacement modeling of hydropower dam. Neural Computing and Applications, 29(12), 1495–1506. https://doi.org/10.1007/s00521-016-2666-0.
Catani, F., Lagomarsino, D., Segoni, S., & Tofani, V. (2013). Landslide susceptibility estimation by random forests technique: Sensitivity and scaling issues. Natural Hazards and Earth System Sciences, 13(11), 2815–2831. https://doi.org/10.5194/nhess-13-2815-2013.
Chen, W., Fan, L., Li, C., & Pham, B. T. (2020). Spatial prediction of landslides using hybrid integration of artificial intelligence algorithms with frequency ratio and index of entropy in Nanzheng county, China. Applied Sciences (Basel), 10(1), 29. https://doi.org/10.3390/app10010029.
Chung, C. J. F., & Fabbri, A. G (1999). Probabilistic prediction models for landslide hazard mapping. Photogrammetric Engineering and Remote Sensing, 65(12), 1389–1399.
Cousineau, D., & Allan, T. (2015). Likelihood and its use in Parameter Estimation and Model Comparison. Mesure et Évaluation En Éducation, 37(3), 63–98. https://doi.org/10.7202/1036328ar.
Cruden, D. M. (1991). A simple definition of a landslide. Bulletin of the International Association of Engineering Geology - Bulletin de l’Association Internationale de Géologie de l’Ingénieur, 43(1), 27–29.
de Oliveira, G. G., Ruiz, L. F. C., Guasselli, L. A., & Haetinger, C. (2019). Random forest and artificial neural networks in landslide susceptibility modeling: a case study of the Fão River Basin, Southern Brazil. Natural Hazards, 99(2), 1049–1073. https://doi.org/10.1007/s11069-019-03795-x.
De Souto, M. C. P., De Araujo, D. S. A., Costa, I. G., Soares, R. G. F., Ludermir, T. B., & Schliep, A. (2008). Comparative study on normalization procedures for cluster analysis of gene expression datasets. 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), 2792–2798. https://doi.org/10.1109/IJCNN.2008.4634191.
Garrett, J. H. (1994). Where and Why Artificial Neural Networks Are Applicable in Civil Engineering. Journal of Computing in Civil Engineering, 8(2), 129–130.
Gian Quoc, A., Duc-Tan, T., Nguyen Dinh, C., & Tien Bui, D. (2018). Flexible configuration of wireless sensor network for monitoring of rainfall-induced landslide. Indonesian Journal of Electrical Engineering and Computer Science, 12(3), 1030–1036. https://doi.org/10.11591/ijeecs.v12.i3.pp1030-1036.
Guzzetti, F., Carrara, A., Cardinali, M., & Reichenbach, P. (1999). Landslide hazard evaluation: A review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology (Amsterdam), 31(1–4), 181–216.
Guzzetti, F., Mondini, A. C., Cardinali, M., Fiorucci, F., Santangelo, M., & Chang, K. T. (2012). Landslide inventory maps: New tools for an old problem. Earth - Science Reviews, 112(112), 42–66. https://doi.org/10.1016/j.earscirev.2012.02.001.
Haykin, S., & Lippmann, R. (1994). Neural networks, a comprehensive foundation. International journal of neural systems, 5(4), 363-364.
Huqqani, I. A., Tay, L. T., & Saleh, J. M. (2019). Analysis of landslide hazard mapping of Penang island Malaysia using bivariate statistical methods. Indonesian Journal of Electrical Engineering and Computer Science, 16(2), 781–786. https://doi.org/10.11591/ijeecs.v16.i2.pp781-786.
Husin, N. A., Salim, N., & Ahmad, A. R. (2008). Modeling of dengue outbreak prediction in Malaysia: A comparison of neural network and nonlinear regression model. 2008 International Symposium on Information Technology, 1–4. https://doi.org/10.1109/ITSIM.2008.4632022.
Hutchinson, J. N. (1995). Landslide Hazard Assessment. Keynote Paper. In: Bell, D.H., Ed., Landslides. 6th International Symposium on Landslides, 1805–1841.
Kaur, N., & Kumar, K. (2016). Normalization Based K-means Data Analysis Algorithm. International Journal of Advanced Research in Computer Science and Software Engineering, ISSN, 2277, 455-457.
Kawabata, D., & Bandibas, J. (2009). Landslide susceptibility mapping using geological data, a DEM from ASTER images and an Artificial Neural Network (ANN). Geomorphology (Amsterdam), 113(1–2), 97–109. https://doi.org/10.1016/j.geomorph.2009.06.006.
Kotsiantis, S. B., Kanellopoulos, D., & Pintelas, P. E. (2006). Data Preprocessing for Supervised Learning. International Journal of Computer Science, 1, 111–117.
Lee, D. H., Kim, Y. T., & Lee, S. R. (2020). Shallow landslide susceptibility models based on artificial neural networks considering the factor selection method and various non-linear activation functions. Remote Sensing, 12(7), 1194. https://doi.org/10.3390/rs12071194.
Lee, S., & Talib, J. A. (2005). Probabilistic landslide susceptibility and factor effect analysis. Environmental Earth Sciences, 47(7), 982–990. https://doi.org/10.1007/s00254-005-1228-z.
Liu, L., Li, S., Li, X., Jiang, Y., Wei, W., Wang, Z., & Bai, Y. (2019). An integrated approach for landslide susceptibility mapping by considering spatial correlation and fractal distribution of clustered landslide data. Landslides, 16(4), 715–728. https://doi.org/10.1007/s10346-018-01122-2.
Lombardo, L., & Mai, P. M. (2018). Presenting logistic regression-based landslide susceptibility results. Engineering Geology, 244(C), 14–24. https://doi.org/10.1016/j.enggeo.2018.07.019.
Murakami, S., Tien, T. L., Omar, R. B. C., Nishigaya, T., Aziza, N., Roslan, R., ... & Sakai, N. (2014). Landslides Hazard map in Malay peninsula by using historical landslide database and related information. J Civil Eng Res, 4(3A), 54-58.
Nhu, V. H., Shirzadi, A., Shahabi, H., Singh, S. K., Al-Ansari, N., Clague, J. J., … Ahmad, B. B. (2020). Shallow landslide susceptibility mapping: A comparison between logistic model tree, logistic regression, naïve bayes tree, artificial neural network, and support vector machine algorithms. International Journal of Environmental Research and Public Health, 17(8), 1–30. https://doi.org/10.3390/ijerph17082749.
Ortiz, J. A. V., & Martínez-Graña, A. M. (2018). A neural network model applied to landslide susceptibility analysis (Capitanejo, Colombia). Geomatics, Natural Hazards and Risk, 9(1), 1106–1128. https://doi.org/10.1080/19475705.2018.1513083.
Panchal, G., Ganatra, A., Kosta, Y. P., & Panchal, D. (2010). Searching most efficient neural network architecture using Akaike’s information criterion (AIC). International Journal of Computer Applications, 1(5), 41-44.
Paola, J. D., & Schowengerdt, R. A. (1995). A review and analysis of backpropagation neural networks for classification of remotely-sensed multi-spectral imagery. International Journal of Remote Sensing, 16(16), 3033–3058. https://doi.org/10.1080/01431169508954607.
Pradhan, B., & Lee, S. (2010). Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models. Environmental Earth Sciences, 60(5), 1037–1054. https://doi.org/10.1007/s12665-009-0245-8.
Regmi, A. D., Devkota, K. C., Yoshida, K., Pradhan, B., Pourghasemi, H. R., Kumamoto, T., & Akgun, A. (2014). Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya. Arabian Journal of Geosciences, 7(2), 725–742. https://doi.org/10.1007/s12517-012-0807-z.
Sahana, M., Pham, B. T., Shukla, M., Costache, R., Thu, D. X., Chakrabortty, R., … Prakash, I. (2020). Rainfall induced landslide susceptibility mapping using novel hybrid soft computing methods based on multi-layer perceptron neural network classifier. Geocarto International, 1–25. https://doi.org/10.1080/10106049.2020.1837262.
Scaioni, M., Longoni, L., Melillo, V., & Papini, M. (2014). Remote sensing for landslide investigations: An overview of recent achievements and perspectives. Remote Sensing, 6(10), 9600–9652. https://doi.org/10.3390/rs6109600.
Shahri, A. S., Spross, J., Johansson, F., & Larsson, S. (2019). Landslide susceptibility hazard map in southwest Sweden using artificial neural network. Catena, 183(C), 104225. https://doi.org/10.1016/j.catena.2019.104225.
Sheela, K. G., & Deepa, S. N. (2013). Review on methods to fix number of hidden neurons in neural networks. Mathematical Problems in Engineering, 2013, 1–11. https://doi.org/10.1155/2013/425740.
Sun, X., Chen, J., Bao, Y., Han, X., Zhan, J., & Peng, W. (2018). Landslide susceptibility mapping using logistic regression analysis along the Jinsha river and its tributaries close to Derong and Deqin County, southwestern China. ISPRS International Journal of Geo-Information, 7(11), 438. https://doi.org/10.3390/ijgi7110438.
Tay, L. T., Alkhasawneh, M. S., Ngah, U. K., & Lateh, H. (2014). Landslide hazard mapping of Penang Island using dominant factors. 2014 IEEE 2nd International Symposium on Telecommunication Technologies (ISTT), 154–158. https://doi.org/10.1109/ISTT.2014.7238195.
Tien Bui, D., Hoang, N. D., Martínez-Álvarez, F., Ngo, P. T. T., Hoa, P. V., Pham, T. D., … Costache, R. (2020). A novel deep learning neural network approach for predicting flash flood susceptibility: A case study at a high frequency tropical storm area. Science of the Total Environment, 701(C), 134413. https://doi.org/10.1016/j.scitotenv.2019.134413.
Van Westen, C. J., Rengers, N., & Soeters, R. (2003). Use of geomorphological information in indirect landslide susceptibility assessment. Natural Hazards, 30(3), 399–419. https://doi.org/10.1023/B:NHAZ.0000007097.42735.9e.
Van Westen, C. J. (1993). Application of geographic information systems to landslide hazard zonation. International Institute for Geo-Information Science and Earth Observation. http://www.itc.nl/library/Papers_1993/phd/vanwesten.pdf.
Varnes, D. (1984). Landslide hazard zonation : A review of principles and practice. Natural Hazards, (3) .
Ya’acob, N., Tajudin, N., & Azize, A. M. (2019). Rainfall-landslide early warning system (RLEWS) using TRMM precipitation estimates. Indonesian Journal of Electrical Engineering and Computer Science, 13(3), 1259–1266. https://doi.org/10.11591/ijeecs.v13.i3.pp1259-1266.
Published
2021-04-25
How to Cite
HUQQANI, Ilyas Ahmad; TIEN, Tay Lea; MOHAMAD-SALEH, Junita.
Landslide Hazard Analysis Using a Multilayered Approach Based on Various Input Data Configurations.
Geosfera Indonesia, [S.l.], v. 6, n. 1, p. 20-39, apr. 2021.
ISSN 2614-8528.
Available at: <https://jurnal.unej.ac.id/index.php/GEOSI/article/view/23347>. Date accessed: 22 dec. 2024.
doi: https://doi.org/10.19184/geosi.v6i1.23347.
Section
Original Research Articles
