IMPLEMENTATION OF MAPPING-BASED MACHINE LEARNING ALGORITHM AS NON-STRUCTURAL DISASTER MITIGATION TO DETECT LANDSLIDE SUSCEPTIBILITY IN TAKARI DISTRICT
Abstract
This research is primarily dedicated to providing a comprehensive exposition of the methodology applied in the deployment of a cartographic-based machine learning algorithm designed for the precise identification of areas susceptible to landslides within the geographical confines of the Takari District.This research delves into the application of mapping-based machine learning algorithms in the domain of non-structural disaster mitigation, with a specific emphasis on the detection of landslide susceptibility within the Takari District. A range of machine learning algorithms, including Support Vector Machine, Naive Bayes Classifier, Ordinal Logistic Regression, Random Forest, and Decision Tree, were harnessed to evaluate rainfall data within the context of landslide susceptibility. An evaluation of model performance, anchored in accuracy and Kappa metrics, unveiled that both the Ordinal Logistic Regression and Random Forest models exhibited noteworthy precision, reaching a commendable 74.36%. Nevertheless, a meticulous examination of Kappa values disclosed the ascendancy of the Random Forest model, which achieved a superior Kappa value of 0.5397. As portrayed in the visual representation provided, it becomes manifest that the Random Forest algorithm's prognostications yield 66 instances of cloudy atmospheric conditions, 48 occurrences of light precipitation, and 3 episodes of moderate rainfall. These predictions are influenced by several factors, including average temperature, humidity levels, wind speed, duration of sunlight, and wind direction at maximum speed. Consequently, this comprehensive analysis underscores the Random Forest algorithm as the most efficacious model for landslide susceptibility prediction. Furthermore, the study seamlessly integrated overlay maps, encompassing the Slope Inclination Map of the Takari District, Geological Map of the Takari District, and Soil Type Map of the Takari District, to contribute to the formulation of a definitive map delineating the susceptibility to landslides in the Takari District. Furthermore, further research could conduct spatial validation of the model predictions using additional datasets or remote sensing data to validate the accuracy of the landslide susceptibility map and ensure its applicability across different geographical regions.
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M. Heydari, H. B. Ghadim, M. Rashidi, and M. Noori, “Application of holt-winters time series models for predicting climatic parameters (Case study: Robat Garah-Bil station, Iran),” Polish J. Environ. Stud., vol. 29, no. 1, pp. 617–627, 2020, doi: 10.15244/pjoes/100496.
N. W. Himalayas and J. Rohn, “Using PCA in evaluating event-controlling attributes of landsliding in the 2005 Kashmir earthquake region ,” Nat. Hazards, vol. 81, no. 3, pp. 1999–2017, 2017, doi: 10.1007/s11069-016-2172-9.
I. Riadi, R. Umar, and F. D. Aini, “Analisis Perbandingan Detection Traffic Anomaly Dengan Metode Naive Bayes Dan Support Vector Machine (Svm),” Ilk. J. Ilm., vol. 11, no. 1, pp. 17–24, 2019, doi: 10.33096/ilkom.v11i1.361.17-24.
E. Yesilnacar and T. Topal, “Landslide susceptibility mapping : A comparison of logistic regression and neural networks methods in a medium scale study , Hendek region ( Turkey ),” vol. 79, pp. 251–266, 2005, doi: 10.1016/j.enggeo.2005.02.002.
C. Highway, “Landslide Susceptibility Assessment Using Integrated Deep Learning Algorithm along the China-Nepal Highway,” 2018, doi: 10.3390/s18124436.
M. Ho Park, M. Ju, S. Jeong, and J. Young Kim, “Incorporating interaction terms in multivariate linear regression for post-event flood waste estimation,” Waste Manag., vol. 124, pp. 377–384, 2021, doi: 10.1016/j.wasman.2021.02.004.
F. S. Tehrani, M. Calvello, Z. Liu, L. Zhang, and S. Lacasse, Machine learning and landslide studies: recent advances and applications, vol. 114, no. 2. Springer Netherlands, 2022. doi: 10.1007/s11069-022-05423-7.
Z. Guo, Y. Shi, F. Huang, X. Fan, and J. Huang, “Geoscience Frontiers Landslide susceptibility zonation method based on C5 . 0 decision tree and K-means cluster algorithms to improve the efficiency of risk management,” Geosci. Front., vol. 12, no. 6, p. 101249, 2021, doi: 10.1016/j.gsf.2021.101249.
M. S. G. Adnan et al., “A novel framework for addressing uncertainties in machine learning-based geospatial approaches for flood prediction,” J. Environ. Manage., vol. 326, no. PB, p. 116813, 2023, doi: 10.1016/j.jenvman.2022.116813.
J. Huang, J. Lu, and C. X. Ling, “Comparing naive bayes, decision trees, and SVM with AUC and accuracy,” Proc. - IEEE Int. Conf. Data Mining, ICDM, pp. 553–556, 2003, doi: 10.1109/icdm.2003.1250975.
S. V. Burger, Introduction to Machine Learning with Python with R. 2018. [Online]. Available: http://linkinghub.elsevier.com/retrieve/pii/B9780123965028000139
M. Ouyang, Y. Ito, and T. Tokunaga, “Quantifying the inundation impacts of earthquake-induced surface elevation change by hydrological and hydraulic modeling,” Sci. Rep., vol. 11, no. 1, pp. 1–13, 2021, doi: 10.1038/s41598-021-83309-7.
H. Harsa et al., “Machine learning and artificial intelligence models development in rainfall-induced landslide prediction,” IAES Int. J. Artif. Intell., vol. 12, no. 1, pp. 262–270, 2023, doi: 10.11591/ijai.v12.i1.pp262-270.
F. Huang, J. Zhang, C. Zhou, Y. Wang, J. Huang, and L. Zhu, “Faming Huang I Jing Zhang I Chuangbing Zhou I Yuhao Wang I Jinsong Huang I Li Zhu A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide sus- ceptibility prediction,” no. August 2019, pp. 217–229, 2020, doi: 10.1007/s10346-019-01274-9.
S. V. Leavitt, R. S. Lee, P. Sebastiani, C. Robert Horsburgh, H. E. Jenkins, and L. F. White, “Estimating the relative probability of direct transmission between infectious disease patients,” Int. J. Epidemiol., vol. 49, no. 3, pp. 764–775, 2021, doi: 10.1093/IJE/DYAA031.
X. Tang, J. Li, M. Liu, W. Liu, and H. Hong, “Flood susceptibility assessment based on a novel random Naïve Bayes method: A comparison between different factor discretization methods,” Catena, vol. 190, no. March, p. 104536, 2020, doi: 10.1016/j.catena.2020.104536.
A. A. Boxwala, J. Kim, J. M. Grillo, and L. Ohno-Machado, “Using statistical and machine learning to help institutions detect suspicious access to electronic health records,” J. Am. Med. Informatics Assoc., vol. 18, no. 4, pp. 498–505, 2011, doi: 10.1136/amiajnl-2011-000217.
L. Liu and X. yi You, “Water quality assessment and contribution rates of main pollution sources in Baiyangdian Lake, northern China,” Environ. Impact Assess. Rev., vol. 98, no. November 2022, p. 106965, 2023, doi: 10.1016/j.eiar.2022.106965.
M. H. Hasan, A. Ahmed, K. M. Nafee, and M. A. Hossen, “Use of machine learning algorithms to assess flood susceptibility in the coastal area of Bangladesh,” Ocean Coast. Manag., vol. 236, no. November 2022, p. 106503, 2023, doi: 10.1016/j.ocecoaman.2023.106503.
R. F. de M. M. A. Ponti, Machine learning A Practical Approach on the Statistical Learning Theory, vol. 45, no. 13. Springer International Publishing AG, 2018. [Online]. Available: https://books.google.ca/books?id=EoYBngEACAAJ&dq=mitchell+machine+learning+1997&hl=en&sa=X&ved=0ahUKEwiomdqfj8TkAhWGslkKHRCbAtoQ6AEIKjAA
E. K. Sahin, “Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest,” SN Appl. Sci., vol. 2, no. 7, pp. 1–17, 2020, doi: 10.1007/s42452-020-3060-1.
N. Shahzad, X. Ding, and S. Abbas, “A Comparative Assessment of Machine Learning Models for Landslide Susceptibility Mapping in the Rugged Terrain of Northern Pakistan,” Appl. Sci., vol. 12, no. 5, 2022, doi: 10.3390/app12052280.
J. Liu et al., “Assessment of Flood Susceptibility Using Support Vector Machine in the Belt and Road Region,” Nat. Hazards Earth Syst. Sci. Discuss., no. May, pp. 1–37, 2021.
E. K. Sahin, “Implementation of free and open-source semi-automatic feature engineering tool in landslide susceptibility mapping using the machine-learning algorithms RF, SVM, and XGBoost,” Stoch. Environ. Res. Risk Assess., vol. 37, no. 3, pp. 1067–1092, 2023, doi: 10.1007/s00477-022-02330-y.
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