Flood is the most devastating and prevalent disaster among all-natural disasters. Every year, flood claims hundreds of human lives and causes damage to the worldwide economy and environment. Consequently, the identification of flood-vulnerable areas is important for comprehensive flood risk management. The main objective of this study is to delineate flood-prone areas in the Panjkora River Basin (PRB), eastern Hindu Kush, Pakistan. An initial extensive field survey and interpretation of Landsat-7 and Google Earth images identified 154 flood locations that were inundated in 2010 floods. Of the total, 70% of flood locations were randomly used for building a model and 30% were used for validation of the model. Eight flood parameters including slope, elevation, land use, Normalized Difference Vegetation Index (NDVI), topographic wetness index (TWI), drainage density, and rainfall were used to map the flood-prone areas in the study region. The relative frequency ratio was used to determine the correlation between each class of flood parameter and flood occurrences. All of the factors were resampled into a pixel size of 30×30 m and were reclassified through the natural break method. Finally, a final hazard map was prepared and reclassified into five classes, i.e., very low, low, moderate, high, very high susceptibility. The results of the model were found reliable with area under curve values for success and prediction rate of 82.04% and 84.74%, respectively. The findings of this study can play a key role in flood hazard management in the target region; they can be used by the local disaster management authority, researchers, planners, local government, and line agencies dealing with flood risk management.

Floods are considered one of the world’s most overwhelming hydro meteorological disasters, which cause tremendous environmental and socioeconomic damages in a developing country such as Pakistan. In this study, we use a Geographic information system (GIS)-based multi-criteria approach to access detailed flood vulnerability in the District Shangla by incorporating the physical, socioeconomic vulnerabilities, and coping capacity. In the first step, 21 essential criteria were chosen under three vulnerability components. To support the analytical hierarchy process (AHP), the used criteria were transformed, weighted, and standardized into spatial thematic layers. Then a weighted overlay technique was used to build an individual map of vulnerability components. Finally, the integrated vulnerability map has been generated from the individual maps and spatial dimensions of vulnerability levels have been identified successfully. The results demonstrated that 25% of the western-middle area to the northern part of the study area comprises high to very high vulnerability because of the proximity to waterways, high precipitation, elevation, and other socioeconomic factors. Although, by integrating the coping capacity, the western-central and northern parts of the study area comprising from high to very high vulnerability. The coping capacities of the central and eastern areas are higher as compared to the northern and southern parts of the study area because of the numerous flood shelters and health complexes. A qualitative approach from the field validated the results of this study. This study’s outcomes would help disaster managers, decision makers, and local administration to quantify the spatial vulnerability of flood and establish successful mitigation plans and strategies for flood risk assessment in the study area.

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.

Land use/cover change (LUCC) is one of the causes of global climate and environmental change. Understanding rapid LUCC in urbanized areas is vital for natural resources management for sustainable development. This study primarily considered Vientiane, the capital of Laos, which experienced rapid LUCC due to both natural and anthropogenic factors. The study used geographical information system (GIS) combined with ERDAS and TerrSet technologies to objectively process the ground surveyed and remotely obtained data in order to investigate the historical LUCC as well as predict future LUCC in the study area during the periods of 1995–2018 and 2030–2050, respectively. A comprehensive list of assessment factors comprised of both natural and anthropogenic factors was used for analysis using the cellular automata–Markov (CA–Markov) model. The results show a historical loss of intact forest of 24.36% and of bare land of 1.01%. There were also tremendous increases in degraded forest (11.36%), agricultural land (8.91%), built-up areas (4.49%) and water bodies (1.16%). Finally, the LUCC prediction results indicate the conversion of land use from one type to another, particularly from natural to anthropogenic use, in the near future. These changes demonstrate that the losses associated with ecosystem services will destructively impact human wellbeing in the city and other areas of the country. The study results provide the basic scientific knowledge for LUCC planners, urban designers and natural resources managers. They serve as a decision-making support tool for the establishment of sustainable land resource utilization policies in Vientiane and other cities of similar conditions.