How do multiple kernel functions in machine learning algorithms improve precision in flood probability mapping?

Abstract

With climate change, hydro-climatic hazards, i.e., floods in the Himalayas regions, are expected to worsen, thus, likely to affect humans and socio-economic growth. Precisely, the Koshi River basin (KRB) is often impacted by flooding over the year. However, studies on estimating and predicting floods still lack in this basin.This study aims at developing flood probability map using machine learning algorithms (MLAs): gaussian process regression (GPR) and support vector machine (SVM) with multiple kernel functions including Pearson VII function kernel (PUK), polynomial, normalized poly kernel, and radial basis kernel function (RBF). Historical flood locations with available topography, hydrogeology, and environmental datasets were further considered to build flood model.Two datasets were carefully chosen to measure the feasibility and robustness of MLAs: training dataset (location of floods between 2010 and 2019) and testing dataset (flood locations of 2020) with thirteen flood influencing factors. 2The validation of the MLAs was evaluated using a validation dataset and statistical indices such as the coefficient of determination (r 2 : 0.546~0.995), mean absolute error (MAE: 0.009~0.373), root mean square error (RMSE: 0.051~0.466), relative absolute error (RAE: 1.81~88.55%), and root-relative square error (RRSE: 10.19~91.00%).Results showed that the SVM-Pearson VII kernel (PUK) yielded better prediction than other algorithms. The resultant map from SVM-PUK revealed that 27.99% area with low, 39.91% area with medium, 31.00% with high, and 1.10% area with very high probabilities of flooding in the study area. The final flood probability map could add a greatt value to the effort of flood risk mitigation and planning processes in KRB.

Detection of Flash Flood Inundated Areas Using Relative Difference in NDVI from Sentinel-2 Images: A Case Study of the August 2020 Event in Charikar, Afghanistan

“…It means that inundation areas would be quickly estimated immediately after a disaster if a cloud-free optical image is obtained immediately following the event. In order to further assess structural damage such as buildings and infrastructures such as roads and bridges in natural disasters, spatial databases in geographic information systems (GIS) would be useful not only at local and regional levels but also at global level [64][65][66][67][68][69]. By superimposing the building and infrastructure inventory data in the GIS database on the inundation map developed by the proposed method, the amount and extent of the structural damage can be easily evaluated.…”

Uncertainty Reduction in Flood Susceptibility Mapping Using Random Forest and eXtreme Gradient Boosting Algorithms in Two Tropical Desert Cities, Shibam and Marib, Yemen

“…The qualitative probability categories (low to very high) on the maps serve as a visual representation of relative likelihood, providing an intuitive interpretation [82]. While we appreciate the convention of expressing probabilities numerically, the qualitative categories offer a user-friendly approach for conveying complex spatial information [83].…”