A Performance Comparison between GIS-based and Neuron Network Methods for Flood Susceptibility Assessment in Ayutthaya Province
DOI:
https://doi.org/10.48048/tis.2022.2038Keywords:
Analytical hierarchy process, Artificial neuron network, Flood risk assessment, Flood susceptibility mapping, Machine learningAbstract
Flooding has been a long withstanding issue in Thailand. Due to its geographical setup, mitigation and management of floods are challenging and hard to execute. One of the tools used in managing the events is “flood susceptibility mapping,” in which an incident probability as well as a rescue path is estimated and planned. To create one, the traditional GIS method called FRAM (flood risk assessment model), combined with AHP (analytical hierarchy process), is used and implemented on ArcGIS software. In this method, we first created a comparison table to compute weights for each of the selected factors. Then the computed weights were used in the FRAM model in ArcGIS to create a flood susceptibility map for each region. Each region was then classified as very high, high, medium, low, and very low risk. On the other hand, in computer science, machine learning and AI are prevalent and being adopted to various domains, promising the effectiveness of the method, potentially beat the forementioned traditional method. Therefore, ANN (artificial neural network) is adopted in this work to create the flood susceptibility map. The ANN technique is developed by using causal factors. The ANN classifies areas as either flood areas or flood-free areas. The 2 methods from different disciplines (GIS and Computer Science) are applied and described in this paper with the intention to prove whether the machine learning is really efficient and can outperform the traditional GIS approach. Data on Thailand’s Ayutthaya Province is used in this work as a case study - in order to assess flood prone areas and compared for performance evaluation. Both of which use the 6 selected factors according to the literature: (i) flow accumulation, (ii) elevation, (iii) land use, (iv) rainfall intensity, (v) slope and (vi) soil types. The results from the 2 methods were verified with historical flood data and compared. The results showed that ANN (obtained via sensitivity analysis) outperformed the FRAM with precision of 79.90 %, recall of 79.04 %, F1-score of 79.08 % and accuracy of 79.31 %. In addition, we found that (according to our ANN experiments) the main causal factors related to flood susceptibility map only included 3 factors: flow accumulation, elevation, and soil types. Therefore, the proposed methodology for assessment of flood susceptibility areas using these 3 factors could be considered sufficient and applied to other regions in related applications, when needed.
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SMH Shah, Z Mustaffa, and KW Yusof. Disasters worldwide and floods in the Malaysian region: A brief review. Indian J. Sci. Tech. 2017; 10, 1-9.
Ministry of Agriculture and Cooperatives. Report on impact of flood on farmers. Ministry of Agriculture and Cooperatives, 2011.
D Komori, S Nakamura, M Kiguchi, A Nishijima, D Yamazaki, S Suzuki, A Kawasaki, K Oki and T Oki. Characteristics of the 2011 Chao Phraya River flood in central Thailand. Hydrolog. Res. Lett. 2012; 6, 41-6.
N Thanvisitthpon. Impacts of repetitive floods and satisfaction with flood relief efforts: A case study of the flood-prone districts in Thailand’s Ayutthaya province. Clim. Risk Manag. 2017; 18, 15-20.
K Spachinger, W Dorner, R Metzka, K Serrhini and S Fuchs. Flood risk and flood hazard maps-visualisation of hydrological risks. IOP Conf. Ser. Earth Environ. Sci. 2008; 4, 012043.
NN Kourgialas and GP Karatzas. A flood risk decision making approach for Mediterranean tree crops using GIS; climate change effects and flood-tolerant species. Environ. Sci. Pol. 2016; 63, 132-42.
SP Ozkan and C Tarhan. Detection of flood hazard in urban areas using GIS: Izmir case. Proc. Tech. 2016; 22, 373-81.
N Kazakis, I Kougias and T Patsialis. Assessment of flood hazard areas at a regional scale using an index-based approach and analytical hierarchy process: Application in Rhodope-Evros region, Greece. Sci. Total Environ. 2015; 538, 555-63.
D Oikonomidis, S Dimogiannia, N Kazakis and K Voudouris. A GIS/remote sensing-based methodology for groundwater potentiality assessment in Tirnavos area, Greece. J. Hydrolog. 2015; 525, 197-208.
OF Althuwaynee, B Pradhan, HJ Park and JH Lee. A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping. Catena 2014; 114, 21-36.
M Abdel-Basset, NA Nabeeh, HA El-Ghareeb and A Aboelfetouh. Utilising neutrosophic theory to solve transition difficulties of IoT-based enterprises. Enterprise Inform. Syst. 2020; 14, 1304-24.
RA Deshmukh and R Hiremath. Analyzing the key performance indicators of advanced sustainable manufacturing system using AHP approach. In: P Pawar, B Ronge, R Balasubramaniam, A Vibhute, S Apte (Eds.). Techno-Societal 2018. Springer, Cham, Switzerland, 2020, p. 745-50.
A Arabameri, K Rezaei, A Cerdà, C Conoscenti and Z Kalantari. A comparison of statistical methods and multi-criteria decision making to map flood hazard susceptibility in Northern Iran. Sci. Total Environ. 2019; 660, 443-58.
T Wuest, D Weimer, C Irgens and KD Thoben. Machine learning in manufacturing: Advantages, challenges, and applications. Prod. Manuf. Res. 2016; 4, 23-45.
XH Le, HV Ho, G Lee and S Jung. Application of long short-term memory (LSTM) neural network for flood forecasting. Water 2019; 11, 1387.
Z Wang, C Lai, X Chen, B Yang, S Zhao and X Bai. Flood hazard risk assessment model based on random forest. J. Hydrolog. 2015; 527, 1130-41.
TMD Melo and OC Pedrollo. Artificial neural networks for estimating soil water retention curve using fitted and measured data. Appl. Environ. Soil Sci. 2015; 2015, 535216.
MS Tehrany, S Jones and F Shabani. Identifying the essential flood conditioning factors for flood prone area mapping using machine learning techniques. Catena 2019; 175, 174-92.
MS Tehrany, B Pradhan and MN Jebur. Flood susceptibility mapping using a novel ensemble weights-of evidence and support vector machine models in GIS. J. Hydrolog. 2014; 512, 332-43.
H Mojaddadi, B Pradhan, H Nampak, N Ahmad and AHB Ghazali. Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS. Geomatics Nat. Hazards Risk 2017; 8, 1080-102.
SH Mahmoud and TY Gan. Multi-criteria approach to develop flood susceptibility maps in arid regions of Middle East. J. Clean. Prod. 2018; 196, 216-29.
MS Tehrany, L Kumar, MN Jebur and F Shabani. Evaluating the application of the statistical index method in flood susceptibility mapping and its comparison with frequency ratio and logistic regression methods. Geomatics Nat. Hazards Risk 2019; 10, 79-101.
K Seejata, A Yodying, T Wongthadam, N Mahavik and S Tantanee. Assessment of flood hazard areas using analytical hierarchy process over the lower Yom Basin, Sukhothai Province. Proc. Eng. 2018; 212, 340-7.
L Stephens, DQ Fuller, N Boivin, T Rick, N Gauthier, AU Kay and et al. Archaeological assessment reveals Earth’s early transformation through land use. Science 2019; 365, 897-902.
R Monjo. Measure of rainfall time structure using the dimensionless n-index. Clim. Res. 2016; 67, 71-86.
CJ Weir, I Butcher, V Assi, SC Lewis, GD Murray, P Langhorne and MC Brady. Dealing with missing standard deviation and mean values in meta-analysis of continuous outcomes: A systematic review. BMC Med. Res. Meth. 2018; 18, 25.
Google Colab, Available at: https://colab.research.google.com/notebooks/welcome.ipynb, accessed May 2020.
DP Kingma and J Ba. Adam: A method for stochastic optimization, Available at: https://arxiv.org/abs/1412.6980, accessed May 2020.
F Pedregosa, G Varoquaux, A Gramfort, V Michel, B Thirion, O Grisel, M Blondel, P Prettenhofer, R Weiss, V Dubourg, J Vanderplas, A Passos, D Cournapeau, M Brucher, M Perrot and É Duchesnay. Scikit-learn: Machine learning in python. J. Mach. Learn. Res. 2011; 12, 2825-30.
DR Jensen and DE Ramirez. Revision: Variance inflation in regression. Adv. Decis. Sci. 2013; 2013, 671204.
JI Daoud. Multicolinearity and regression analysis. J. Phys. Conf. Ser. 2017; 949, 012009.
R-squared definition, Available at: https://www.investopedia.com/terms/r/r-squared.asp, accessed July 2020.
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