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"Floods Remote sensing."
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Flood Risk Modelling Based on Machine Learning Using Google Earth Engine in Hulu Sungai Utara Regency
Flood risk modeling is essential for effective disaster mitigation, particularly in flood-prone areas such as Hulu Sungai Utara Regency, Indonesia. This study leverages Google Earth Engine (GEE) to integrate multi-source satellite data and machine learning techniques for flood susceptibility mapping. Key geospatial variables, including the Normalized Difference Vegetation Index (NDVI), elevation, distance from rivers, and the Topographic Position Index (TPI), were analyzed using a weighted overlay method within GEE. A supervised classification approach was employed to enhance accuracy, and validation was performed using historical flood event data. The results indicate that 51.66% (47,875.86 ha) of the study area falls into the low-risk category, 42.90% (39,763.08 ha) is at moderate risk, and 5.44% (5,040.36 ha) is highly susceptible to flooding. This study highlights the advantages of GEE in large-scale flood risk assessments by enabling real-time processing, high computational efficiency, and seamless integration of geospatial datasets. The findings provide critical insights for local governments and disaster management agencies to develop proactive flood mitigation strategies.
Journal Article
A New Hybrid Firefly–PSO Optimized Random Subspace Tree Intelligence for Torrential Rainfall-Induced Flash Flood Susceptible Mapping
Flash flood is one of the most dangerous natural phenomena because of its high magnitudes and sudden occurrence, resulting in huge damages for people and properties. Our work aims to propose a state-of-the-art model for susceptibility mapping of the flash flood using the decision tree random subspace ensemble optimized by hybrid firefly–particle swarm optimization (HFPS), namely the HFPS-RSTree model. In this work, we used data from a flood inventory map consisting of 1866 polygons derived from Sentinel-1 C-band synthetic aperture radar (SAR) data and a field survey conducted in the northwest mountainous area of the Van Ban district, Lao Cai Province in Vietnam. A total of eleven flooding conditioning factors (soil type, geology, rainfall, river density, elevation, slope, aspect, topographic wetness index (TWI), normalized difference vegetation index (NDVI), plant curvature, and profile curvature) were used as explanatory variables. These indicators were compiled from a geological and mineral resources map, soil type map, and topographic map, ALOS PALSAR DEM 30 m, and Landsat-8 imagery. The HFPS-RSTree model was trained and verified using the inventory map and the eleven conditioning variables and then compared with four machine learning algorithms, i.e., the support vector machine (SVM), the random forests (RF), the C4.5 decision trees (C4.5 DT), and the logistic model trees (LMT) models. We employed a range of statistical standard metrics to assess the predictive performance of the proposed model. The results show that the HFPS-RSTree model had the best predictive performance and achieved better results than those of other benchmarks with the ability to predict flash flood, reaching an overall accuracy of over 90%. It can be concluded that the proposed approach provides new insights into flash flood prediction in mountainous regions.
Journal Article
Remote Sensing of Floods and Flood-Prone Areas: An Overview
Klemas, V., 2015. Remote sensing of floods and flood-prone areas: An overview. River floods and coastal storm surges affect the lives of more people than most other weather-related disasters. Floods can destroy homes, roads, and bridges; tear out trees; cause mudslides; and take many human lives. During flooding, timely and detailed situation reports are required by disaster management authorities to locate and identify affected areas and to implement damage mitigation. Remote sensing systems on satellites and aircraft can provide much of the required information for delineating the flood-affected areas, assessing the damage, and feeding models that can predict the vulnerability to flooding of inland and coastal areas. In this article, I provide an overview of remote sensing and modeling techniques for forecasting the vulnerability to flooding of an area, determining the extent and intensity of the flooding, and assessing the damage caused by the flood.
Journal Article
FLOOD MONITORING USING SENTINEL-1 SAR DATA: A CASE STUDY BASED ON AN EVENT OF 2018 AND 2019 SOUTHERN PART OF KERALA
Flood is a natural hazard influenced by rainfall and dam collapse, which propels release of enormous amount of water. In the last two decades flood is the second largest natural hazards occurred worldwide, which caused serious damage to life properties, settlements and economic activities. Flood mapping is a process that is useful for assessment and reduces the risk factor during the flood. An effective monitoring of flood prone area is necessary to handle GIS techniques and without remote sensing data it is difficult to identify the flooded area in this study Microwave remote sensing plays a lead role in natural hazards, here Synthetic Aperture Radar (SAR) data is the best way for monitoring flood hazards. In this study Southern part of the Kerala is chosen as the study area, In August 2018, during the south west monsoon due to heavy rainfall a severe flood affected the southern part of Kerala which saw a 37% increase in the rate of normal rainfall. The objective of the study is to find the flood zone area using SAR data and estimate the flood occurrence over a period of time. However a satellite imagery of optical data is used to analyse the pre and post event of flood, but during a heavy rainfall, cloud may interrupt the data acquisition. SAR satellite imagery fromSentinel-1A is a cloud penetrating data available in all kind of weather conditions during day and night time, which provides a good source of high resolution data sets. To identify the flood affected area an adapted technology of threshold methodology developed by using SAR data and change detection for the year 2018 and 2019, will illustrate flood extended part in southern part of Kerala. The result shows the estimation of flood extended part of the study area and the damages occurred during a flooded time period of post and pre event, vulnerability assessing of crop and agriculture is to obtain an intensity of the damaged areas which is closely associated with the river channel, the Polarization displays a similar sequence for amount of flooding. The study helps to find the reason of flood extent and to equip with better planning for risk reduction and management during a flooding period.
Journal Article
Application of Remote-Sensing-Based Hydraulic Model and Hydrological Model in Flood Simulation
Floods are one of the main natural disaster threats to the safety of people’s lives and property. Flood hazards intensify as the global risk of flooding increases. The control of flood disasters on the basin scale has always been an urgent problem to be solved that is firmly associated with the sustainable development of water resources. As important nonengineering measures for flood simulation and flood control, the hydrological and hydraulic models have been widely applied in recent decades. In our study, on the basis of sufficient remote-sensing and hydrological data, a hydrological (Xin’anjiang (XAJ)) and a two-dimensional hydraulic (2D) model were constructed to simulate flood events and provide support for basin flood management. In the Chengcun basin, the two models were applied, and the model parameters were calibrated by the parameter estimation (PEST) automatic calibration algorithm in combination with the measured data of 10 typical flood events from 1990 to 1996. Results show that the two models performed well in the Chengcun basin. The average Nash–Sutcliffe efficiency (NSE), percentage error of peak discharge (PE), and percentage error of flood volume (RE) were 0.79, 16.55%, and 18.27%, respectively, for the XAJ model, and those values were 0.76, 12.83%, and 11.03% for 2D model. These results indicate that the models had high accuracy, and hydrological and hydraulic models both had good application performance in the Chengcun basin. The study can a provide decision-making basis and theoretical support for flood simulation, and the formulation of flood control and disaster mitigation measures in the basin.
Journal Article
Evolution of Flood Forecasting: A Comprehensive Review of Traditional and Sophisticated Approaches
Flood forecasting is considered vital worldwide, as communities, infrastructure, and the environment face significant risks from floods. This study provides a comprehensive overview of both traditional and advanced flood forecasting methods, focusing on their strengths, limitations, and suitability for different situations. Traditional methods, such as empirical rainfall-runoff relationships and analysis of historical flood data, serve as fundamental approaches based on past patterns and local knowledge. However, these approaches often lack precision and responsiveness to real-time changes in climate and land use. Conversely, the accuracy and lead times of flood forecasts have been enhanced using advanced computational models, remote sensing, machine learning, and deep learning techniques. Technologies like hydrodynamic modeling, satellite-based monitoring, and hybrid models demonstrate higher predictive capabilities by incorporating real-time data and spatial analysis. Recent flood case studies are examined in this research, comparing the accuracy, efficiency, and flexibility of traditional versus modern methods. The results indicate that while traditional techniques are valued for their simplicity and low cost, modern forecasting methods offer greater precision and adaptability, both of which are crucial for proactive disaster management in a changing climate. This study recommends a hybrid approach that combines traditional knowledge with modern technology to improve the accuracy and reliability of flood forecasting systems.
Journal Article
Remote Sensing Methods for Flood Prediction: A Review
Floods are a major cause of loss of lives, destruction of infrastructure, and massive damage to a country’s economy. Floods, being natural disasters, cannot be prevented completely; therefore, precautionary measures must be taken by the government, concerned organizations such as the United Nations Office for Disaster Risk Reduction and Office for the coordination of Human Affairs, and the community to control its disastrous effects. To minimize hazards and to provide an emergency response at the time of natural calamity, various measures must be taken by the disaster management authorities before the flood incident. This involves the use of the latest cutting-edge technologies which predict the occurrence of disaster as early as possible such that proper response strategies can be adopted before the disaster. Floods are uncertain depending on several climatic and environmental factors, and therefore are difficult to predict. Hence, improvement in the adoption of the latest technology to move towards automated disaster prediction and forecasting is a must. This study reviews the adoption of remote sensing methods for predicting floods and thus focuses on the pre-disaster phase of the disaster management process for the past 20 years. A classification framework is presented which classifies the remote sensing technologies being used for flood prediction into three types, which are: multispectral, radar, and light detection and ranging (LIDAR). Further categorization is performed based on the method used for data analysis. The technologies are examined based on their relevance to flood prediction, flood risk assessment, and hazard analysis. Some gaps and limitations present in each of the reviewed technologies have been identified. A flood prediction and extent mapping model are then proposed to overcome the current gaps. The compiled results demonstrate the state of each technology’s practice and usage in flood prediction.
Journal Article
Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier
Mapping flood-prone areas is a key activity in flood disaster management. In this paper, we propose a new flood susceptibility mapping technique. We employ new ensemble models based on bagging as a meta-classifier and K-Nearest Neighbor (KNN) coarse, cosine, cubic, and weighted base classifiers to spatially forecast flooding in the Haraz watershed in northern Iran. We identified flood-prone areas using data from Sentinel-1 sensor. We then selected 10 conditioning factors to spatially predict floods and assess their predictive power using the Relief Attribute Evaluation (RFAE) method. Model validation was performed using two statistical error indices and the area under the curve (AUC). Our results show that the Bagging–Cubic–KNN ensemble model outperformed other ensemble models. It decreased the overfitting and variance problems in the training dataset and enhanced the prediction accuracy of the Cubic–KNN model (AUC=0.660). We therefore recommend that the Bagging–Cubic–KNN model be more widely applied for the sustainable management of flood-prone areas.
Journal Article
Flood risk assessment and mapping using AHP in arid and semiarid regions
Identifying flood risk-prone areas in the regions of extreme aridity conditions is essential for mitigating flood risk and rainwater harvesting. Accordingly, the present work is addressed to the assessment of the flood risk depending on spatial analytic hierarchy process of the integration between both Remote Sensing Techniques (RST) and Geographic Information Systems (GIS). This integration results in enhancing the analysis with the savings of time and efforts. There are several remote sensing-based data used in conducting this research, including a digital elevation model with an accuracy of 30 m, spatial soil and geologic maps, historical daily rainfall records, and data on rainwater drainage systems. Five return periods (REPs) (2, 5, 10, 25, 50, 100, and 200 years) corresponding to flood hazards and vulnerability developments maps were applied via the weighted overlay technique. Although the results indicate lower rates of annual rainfall (53–71 mm from the southeast to the northwest), the city has been exposed to destructive flash floods. The flood risk categories for a 100-year REP were very high, high, medium, low, and very low with 17%, 41%, 33%, 8%, and 1% of total area, respectively. These classes correspond to residential zones and principal roads, which lead to catastrophic flash floods. These floods have caused socioeconomic losses, soil erosion, infrastructure damage, land degradation, vegetation loss, and submergence of cities, as well life loss. The results prove the GIS and RST effectiveness in mitigating flood risks and in helping decision makers in flood risk mitigation and rainwater harvesting.
Journal Article