Assessment Analysis of Flood Susceptibility in Tropical Desert Area: A Case Study of Yemen

Abstract

Flooding is one of the catastrophic natural hazards worldwide that can easily cause devastating effects on human life and property. Remote sensing devices are becoming increasingly important in monitoring and assessing natural disaster susceptibility and hazards. The proposed research work pursues an assessment analysis of flood susceptibility in a tropical desert environment: a case study of Yemen. The base data for this research were collected and organized from meteorological, satellite images, remote sensing data, essential geographic data, and various data sources and used as input data into four machine learning (ML) algorithms. In this study, RS data (Sentinel-1 images) were used to detect flooded areas in the study area. We also used the Sentinel application platform (SNAP 7.0) for Sentinel-1 image analysis and detecting flood zones in the study locations. Flood spots were discovered and verified using Google Earth images, Landsat images, and press sources to create a flood inventory map of flooded areas in the study area. Four ML algorithms were used to map flash flood susceptibility (FFS) in Tarim city (Yemen): K-nearest neighbor (KNN), Naïve Bayes (NB), random forests (RF), and eXtreme gradient boosting (XGBoost). Twelve flood conditioning factors were prepared, assessed in multicollinearity, and used with flood inventories as input parameters to run each model. A total of 600 random flood and non-flood points were chosen, where 75% and 25% were used as training and validation datasets. The confusion matrix and the area under the receiver operating characteristic curve (AUROC) were used to validate the susceptibility maps. The results obtained reveal that all models had a high capacity to predict floods (AUC > 0.90). Further, in terms of performance, the tree-based ensemble algorithms (RF, XGBoost) outperform other ML algorithms, where the RF algorithm provides robust performance (AUC = 0.982) for assessing flood-prone areas with only a few adjustments required prior to training the model. The value of the research lies in the fact that the proposed models are being tested for the first time in Yemen to assess flood susceptibility, which can also be used to assess, for example, earthquakes, landslides, and other disasters. Furthermore, this work makes significant contributions to the worldwide effort to reduce the risk of natural disasters, particularly in Yemen. This will, therefore, help to enhance environmental sustainability.

Flood Susceptibility Modeling Using an Advanced Deep Learning-Based Iterative Classifier Optimizer

“…Our research aimed to suggest hybrid ensemble models (DLNN-ICO, ANN-ICO, NB-ICO, and ADT-ICO) for flash FSMs in the study basin. Many investigations have used spatial ensemble models [35,82,96,[110][111][112] for FS mapping. Accurately estimating FS zones is a great challenge for developing realistic and efficient mitigating strategies [39].…”

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

“…Future disaster incidents at a particular site might be estimated by analyzing historical records of previous events [45,46]. Thus, an inventory map is critical to susceptibility modeling, as it can depict a single or numerous incidents in a given area [47]. The inventory map can be produced using various sources, including in situ mapping, flood predictions, aerial photos, and remote sensing images [48,49].…”

“…For the flood inventory, 240 random flood and non-flood points were chosen in Marib and 350 in Shibam. A total of 75% of points were used for training and 25% for validation in both areas [47,55,56].…”

Integrating Machine Learning Ensembles for Landslide Susceptibility Mapping in Northern Pakistan

“…K-nearest neighbors (KNN) is a widely recognized algorithm that is effectively employed for identifying patterns in both classification and regression tasks [62]. It belongs to the category of unsupervised ML algorithms and is commonly referred to as a lazy learning algorithm [63]. The considered principles of KNN involve computing the distances among a single test observation and all observations in a training dataset, subsequently identifying the K-nearest neighbors [64].…”