Naïve Bayes Classifier for Debris Flow Disaster Mitigation in Mount Merapi Volcanic Rivers, Indonesia, Using X-band Polarimetric Radar

Abstract

Debris flow triggered by rainfall that accompanies a volcanic eruption is a serious secondary impact of a volcanic disaster. The probability of debris flow events can be estimated based on the prior information of rainfall from historical and geomorphological data that are presumed to relate to debris flow occurrence. In this study, a debris flow disaster warning system was developed by applying the Naïve Bayes Classifier (NBC). The spatial likelihood of the hazard is evaluated at a small subbasin scale by including high-resolution rainfall measurements from X-band polarimetric weather radar, a topographic factor, and soil type as predictors. The study was conducted in the Gendol River Basin of Mount Merapi, one of the most active volcanoes in Indonesia. Rainfall and debris flow occurrence data were collected for the upper Gendol River from October 2016 to February 2018 and divided into calibration and validation datasets. The NBC was used to estimate the status of debris flow incidences displayed in the susceptibility map that is based on the posterior probability from the predictors. The system verification was performed by quantitative dichotomous quality indices along with a contingency table. Using the validation datasets, the advantage of the NBC for estimating debris flow occurrence is confirmed. This work contributes to existing knowledge on estimating debris flow susceptibility through the data mining approach. Despite the existence of predictive uncertainty, the presented system could contribute to the improvement of debris flow countermeasures in volcanic regions.

Keywords

Debris flows / Gendol River / Indonesia / Merapi volcano / Naïve Bayes classifier

References

[1] Arthur JD, Wood HAR, Baker AE, Cichon JR, Raines GL. Development and implementation of a Bayesian-based aquifer vulnerability assessment in Florida. Natural Resources Research, 2007, 16(2): 93-107

[2] Barde NC, Patole M. Classification and forecasting of weather using ANN, k-NN and Naïve Bayes Algorithms. International Journal of Science and Research, 2016, 5(2): 1740-1742.

[3] Bayes T. An essay towards solving a problem in the doctrine of chances. Philosophical Transactions of the Royal Society, 1763, 53: 370-418

[4] Bélizal ED, Lavigne F, Hadmoko DS, Degeai JP, Dipayana GA, Mutaqin BW, Marfai MS, Coquet M Rain-triggered lahars following the 2010 eruption of Merapi volcano, Indonesia: A major risk. Journal of Volcanology and Geothermal Research, 2013, 261: 330-347

[5] Berti, M., M.L.V. Martina, S. Franceschini, S. Pignone, A. Simoni, and M. Pizziolo. 2012. Probabilistic rainfall thresholds for landslide occurrence using a Bayesian approach. Journal of Geophysical Research 117: Article F04006.

[6] Brunetti MT, Peruccacci S, Rossi M, Luciani S, Valigi D, Guzzetti F. Rainfall thresholds for the possible occurrence of landslides in Italy. Natural Hazards and Earth System Sciences, 2010, 10: 447-458

[7] Bui, D.T., B. Pradhan, O. Lofman, and I. Revhaug. 2012. Landslide susceptibility assessment in Vietnam using support vector machines, decision tree, and Naïve Bayes models. Mathematical Problems in Engineering 2012: Article 974638.

[8] Capra L, Borselli L, Varley N, Ruiz JCG, Norini G, Sarocchi D, Caballero L, Cortes A. Rainfall-triggered debris at Volcán de Colima, Mexico: Surface hydro-repellency as initiation process. Journal of Volcanology and Geothermal Research, 2010, 189: 105-117

[9] Chorowicz J, Lopez E, Garcia F, Parrot J-F, Rudant J-P, Vinluan R. Keys to analyze active lahars from Pinatubo on SAR ERS imagery. Remote Sensing of Environment, 1997, 62(1): 20-29

[10] Hapsari, R.I., S. Oishi, M. Syarifuddin, and R.A. Asmara. 2017. Uncertainty in debris flow hazard estimation using X-MP radar rainfall forecast. In Proceedings of the 37th IAHR World Congress, 13–18 August 2017, Kuala Lumpur, Malaysia, 1–10.

[11] Hapsari RI, Oishi S, Syarifuddin M, Asmara RA, Legono D. X-MP Radar for developing a lahar rainfall threshold for the Merapi Volcano using a Bayesian approach. Journal of Disaster Research, 2019, 14(5): 811-828

[12] Jenkins S, Komorowski J-C, Baxter PJ, Spence R, Picquout A, Lavigne F, Surono The Merapi 2010 eruption: An interdisciplinary impact assessment methodology for studying pyroclastic density current dynamics. Journal of Volcanology and Geothermal Research, 2013, 261: 316-329

[13] Lane, P.C.L., D. Clarke, and P. Hender. 2012. On developing robust models for favourability analysis: Model choice, feature sets and imbalanced data. In Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis, ACL-HLT 2011, 24 June 2011, Portland, Oregon, USA, 44–52.

[14] Lavigne F, Thouret JC. Sediment transportation and deposition by rain-triggered lahars at Merapi Volcano, Central Java, Indonesia. Geomorphology, 2003, 49: 45-69

[15] Lavigne F, Thouret JC, Voight B, Suwa H, Sumaryono A. Lahars at Merapi Volcano, Central Java: An overview. Journal of Volcanology, 2000, 100(1–4): 423-456.

[16] Liu R, Chen Y, Wu J, Gao L, Barrett D, Xu T, Li L, Huang C Assessing spatial likelihood of flooding hazard using Naive Bayes and GIS: A case study in Bowen Basin, Australia. Stochastic Environmental Research and Risk Assessment, 2015, 30: 1575-1590

[17] Mead SR, Magill CR. Probabilistic hazard modelling of rain-triggered debris. Journal of Applied Volcanology, 2017, 6(8): 1-7.

[18] Niu F, Luo J, Lin Z, Liu M, Yin G. Thaw-induced slope failures and susceptibility mapping in permafrost regions of the Qinghai-Tibet Engineering Corridor, China. Bulletin of Volcanology, 2014, 59: 460-480.

[19] Pagano A, Giordano R, Portoghese I, Fratino U, Vurro M. A Bayesian vulnerability assessment tool for drinking water mains under extreme events. Natural Hazards, 2014, 74(3): 2193-2227

[20] Park SG, Maki M, Iwanami K, Bringi VN, Chandrasekar V. Correction of radar reflectivity and differential reflectivity for rain attenuation at X Band Part II: Evaluation and application. Journal of Atmospheric and Oceanic Technology, 2005, 22(11): 1633-1655

[21] Pham BT, Prakash I, Bui DT, Dholakia MB. Evaluation of predictive ability of support vector machines and naive Bayes trees methods for spatial prediction of landslides in Uttarakhand state (India) using GIS. Journal of Geomatics, 2016, 10(1): 71-79.

[22] Rish, I. 2001. An empirical study of the Naïve Bayes Classifier. In Proceedings of IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence 3: 41–46.

[23] Rodolfo, K.S., and A.T. Arguden. 1991. Rain-lahar generation and sediment delivery-systems at Mayon Volcano Philippines. In Sedimentation in volcanic settings, ed. R.V. Fisher, and G.A. Smith, 71–87. Tulsa, OK: SEPM (Society for Sedimentary Geology) (SEPM Special Publication 45).

[24] Roebber PJ. Visualizing multiple measures of forecast quality. Weather and Forecasting, 2009, 24(2): 601-608

[25] Shieh CL, Chen YS, Tsai TJ, Wu JH. Variability in rainfall threshold for debris flow after the Chi-Chi earthquake in central Taiwan. China. International Journal of Sediment Research, 2009, 24(2): 177-188

[26] Solikhin A, Thouret J-C, Gupta A, Sayudi DS, Oehler J-F, Liew SC. Effects and behavior of pyroclastic and debris deposits of the 2010 Merapi Eruption based on high-resolution optical imagery. Procedia Earth and Planetary Science, 2015, 12: 1-10

[27] Solikhin A, Thouret J-C, Liew SC, Gupta A, Sayudi DS, Oehler J-F, Kassouk Z. High-spatial-resolution imagery helps map deposits of the large (VEI 4) 2010 Merapi Volcano eruption and their impact. Bulletin of Volcanology, 2015, 77(3): 1-42

[28] Syarifuddin M, Oishi S, Legono D, Hapsari RI, Iguchi M. Integrating X-MP radar data to estimate rainfall induced debris flow in the Merapi volcanic area. Advances in Water Resources, 2017, 110: 249-262

[29] Takahashi T. Debris flow: Mechanics, prediction and countermeasures, 2007, London: Taylor and Francis

[30] Wilford DJ, Sakals ME, Innes JL, Sidle RC, Bergerud WA. Recognition of debris flow, debris flood and flood hazard through watershed morphometrics. Landslides, 2004, 1(1): 61-66

[31] Varnes DJ. Schuster RL, Krizek RJ. Slope movement type and processes. Landslides: Analysis and control, 1978, Washington, DC: National Research Council 11-33.

[32] Zhao HF, Zhang LM. Instability of saturated and unsaturated coarse granular soils. Journal of Geotechnical and Geoenvironmental Engineering, 2014, 140(1): 25-35