A Performance Comparison between GIS-based and Neuron Network Methods for Flood Susceptibility Assessment in Ayutthaya Province - Vajeethaveesin, Thanat; Panboonyuen, Teerapong; Lawawironjwong, Siam; Srestasathiern, Panu; Jaiyen, Saichon; Jitkajornwanich, Kulsawasd (My University, 2022)
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.
Keywords: Analytical hierarchy process; Artificial neuron network; Flood risk assessment; Flood susceptibility mapping; Machine learning