Intercomparison of Automated Near-Real-Time Flood Mapping Algorithms Using Satellite Data and DEM-Based Methods: A Case Study of 2022 Madagascar Flood DOI Creative Commons
Wenzhao Li, Dongfeng Li, Zheng Fang

et al.

Hydrology, Journal Year: 2023, Volume and Issue: 10(1), P. 17 - 17

Published: Jan. 8, 2023

Numerous algorithms have been developed to automate the process of delineating water surface maps for flood monitoring and mitigation purposes by using multiple sources such as satellite sensors digital elevation model (DEM) data. To better understand causes inaccurate mapping information, we aim demonstrate advantages limitations these through a case study 2022 Madagascar flooding event. The HYDRAFloods toolbox was used perform preprocessing, image correction, automated detection based on state-of-the-art Edge Otsu, Bmax Fuzzy Otsu images; FwDET tool deployed upon cloud computing platform (Google Earth Engine) rapid estimation area/depth Generated from respective techniques were evaluated qualitatively against each other compared with reference map produced European Union Copernicus Emergency Management Service (CEMS). DEM-based show generally overestimated extents. satellite-based that methods are more likely generate consistent results than those Otsu. While synthetic-aperture radar (SAR) data typically favorable over optical under undesired weather conditions, generated SAR tend underestimate extent maps. This also suggests newly launched Landsat-9 serves an essential supplement delineation

Language: Английский

Flood susceptible prediction through the use of geospatial variables and machine learning methods DOI
Navid Mahdizadeh Gharakhanlou, Liliana Pérez

Journal of Hydrology, Journal Year: 2023, Volume and Issue: 617, P. 129121 - 129121

Published: Jan. 13, 2023

Language: Английский

Citations

51
Secure Remote Sensing Data With Blockchain Distributed Ledger Technology: A Solution for Smart Cities DOI Creative Commons
Abdullah Ayub Khan, Asif Ali Laghari, Roobaea Alroobaea

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 69383 - 69396

Published: Jan. 1, 2024

Particularly in the context of smart cities, remote sensing data (RSD) has emerged as one hottest study topics information and communication technology (ICT) today. The development machine learning (ML) artificial intelligence (AI) made it possible to solve a number issues, including automation, control access, optimization, monitoring, management. Simultaneously, there are significant issues with design process hierarchy, inadequate training records, centralized architecture, privacy protection, overall resource consumption restrictions. Distributed Ledger Technology (DLT), on other hand, provides decentralized infrastructure that allows systems eliminate data-sharing procedures cities while transferring from network node node, third-party access solves issues. To an ideal delivery mechanism for analytical model, paper employs Partial Swam Optimization (POS) conjunction secure blockchain distributed consortium network. This work makes three contributions. Firstly, offers safe transmission method combines optimize path reliable across channels. Second, neighborhood encryption sequences carried out using NuCypher proxy re-encryption-enabled value encryption, public key cryptographic approach avoids cypher conversion. Third, Artificial Neural Networks (ANNs) can deliverance classification problem by optimizing record management preservation.

Language: Английский

Citations

23
Assessment of groundwater quality in arid regions utilizing principal component analysis, GIS, and machine learning techniques DOI
Mustafa El-Rawy, Mohamed Wahba, Heba Fathi

et al.

Marine Pollution Bulletin, Journal Year: 2024, Volume and Issue: 205, P. 116645 - 116645

Published: June 25, 2024

Language: Английский

Citations

20
Investigating the Role of the Key Conditioning Factors in Flood Susceptibility Mapping Through Machine Learning Approaches DOI Creative Commons
Khalifa M. Al‐Kindi, Zahra Alabri

Earth Systems and Environment, Journal Year: 2024, Volume and Issue: 8(1), P. 63 - 81

Published: Jan. 1, 2024

Abstract This study harnessed the formidable predictive capabilities of three state-of-the-art machine learning models—extreme gradient boosting (XGB), random forest (RF), and CatBoost (CB)—applying them to meticulously curated datasets topographical, geological, environmental parameters; goal was investigate intricacies flood susceptibility within arid riverbeds Wilayat As-Suwayq, which is situated in Sultanate Oman. The results underscored exceptional discrimination prowess XGB CB, boasting impressive area under curve (AUC) scores 0.98 0.91, respectively, during testing phase. RF, a stalwart contender, performed commendably with an AUC 0.90. Notably, investigation revealed that certain key variables, including curvature, elevation, slope, stream power index (SPI), topographic wetness (TWI), roughness (TRI), normalised difference vegetation (NDVI), were critical achieving accurate delineation flood-prone locales. In contrast, ancillary factors, such as annual precipitation, drainage density, proximity transportation networks, soil composition, geological attributes, though non-negligible, exerted relatively lesser influence on susceptibility. empirical validation further corroborated by robust consensus XGB, RF CB models. By amalgamating advanced deep techniques precision geographical information systems (GIS) rich troves remote-sensing data, can be seen pioneering endeavour realm analysis cartographic representation semiarid fluvial landscapes. findings advance our comprehension vulnerability dynamics provide indispensable insights for development proactive mitigation strategies regions are susceptible hydrological perils.

Language: Английский

Citations

19
Potential flood-prone area identification and mapping using GIS-based multi-criteria decision-making and analytical hierarchy process in Dega Damot district, northwestern Ethiopia DOI Creative Commons

Ajanaw Negese,

Dessalegn Worku,

Alazar Shitaye

et al.

Applied Water Science, Journal Year: 2022, Volume and Issue: 12(12)

Published: Oct. 18, 2022

Abstract Flood is one of the natural hazards that causes widespread destruction such as huge infrastructural damages, considerable economic losses, and social disturbances across world in general Ethiopia, particular. Dega Damot most vulnerable districts Ethiopia to flood hazards, no previous studies were undertaken map flood-prone areas district despite identification mapping being crucial tasks for residents decision-makers reduce manage risk flood. Hence, this study aimed identify district, northwestern using integration Geographic Information System multi-criteria decision-making method with analytical hierarchy process. Flood-controlling factors elevation, slope, flow accumulation, distance from rivers, annual rainfall, drainage density, topographic wetness index, land use cover, Normalized Difference Vegetation Index, soil type, curvature weighted overlayed together achieve objective study. The result shows about 86.83% area has moderate very high susceptibility flooding, 13.17% low flooding. northeastern southwestern parts dominated by elevation cropland found be more susceptible hazards. final generated model was consistent historical events on ground area, revealing method’s effectiveness used

Language: Английский

Citations

53
Detection of Surface Water and Floods with Multispectral Satellites DOI Creative Commons
Cinzia Albertini, Andrea Gioia, Vito Iacobellis

et al.

Remote Sensing, Journal Year: 2022, Volume and Issue: 14(23), P. 6005 - 6005

Published: Nov. 27, 2022

The use of multispectral satellite imagery for water monitoring is a fast and cost-effective method that can benefit from the growing availability medium–high-resolution free remote sensing data. Since 1970s, has been exploited by adopting different techniques spectral indices. high number available sensors their differences in spatial characteristics led to proliferation outcomes depicts nice picture potential limitations each. This paper provides review applications extent delineation flood monitoring, highlighting trends research studies adopted freely optical imagery. performances most common indices segmentation are qualitatively analyzed assessed according land cover types provide guidance targeted specific contexts. comparison carried out collecting evidence obtained several identifying overall accuracy (OA) with each configuration. In addition, issues faced when dealing discussed, together opportunities offered new-generation passive satellites.

Language: Английский

Citations

43
Living with Floods Using State-of-the-Art and Geospatial Techniques: Flood Mitigation Alternatives, Management Measures, and Policy Recommendations DOI Open Access
Rabin Chakrabortty, Subodh Chandra Pal,

Dipankar Ruidas

et al.

Water, Journal Year: 2023, Volume and Issue: 15(3), P. 558 - 558

Published: Jan. 31, 2023

Flood, a distinctive natural calamity, has occurred more frequently in the last few decades all over world, which is often an unexpected and inevitable hazard, but losses damages can be managed controlled by adopting effective measures. In recent times, flood hazard susceptibility mapping become prime concern minimizing worst impact of this global threat; nonlinear relationship between several causative factors dynamicity risk levels makes it complicated confronted with substantial challenges to reliable assessment. Therefore, we have considered SVM, RF, ANN—three ML algorithms GIS platform—to delineate zones subtropical Kangsabati river basin, West Bengal, India; experienced frequent events because intense rainfall throughout monsoon season. our study, adopted are efficient solving non-linear problems assessment; multi-collinearity analysis Pearson’s correlation coefficient techniques been used identify collinearity issues among fifteen factors. research, predicted results evaluated through six prominent statistical (“AUC-ROC, specificity, sensitivity, PPV, NPV, F-score”) one graphical (Taylor diagram) technique shows that ANN most modeling approach followed RF SVM models. The values AUC model for training validation datasets 0.901 0.891, respectively. derived result states about 7.54% 10.41% areas accordingly lie under high extremely danger zones. Thus, study help decision-makers constructing proper strategy at regional national mitigate particular region. This type information may helpful various authorities implement outcome spheres decision making. Apart from this, future researchers also able conduct their research byconsidering methodology

Language: Английский

Citations

29
Urban flood susceptibility mapping using frequency ratio and multiple decision tree-based machine learning models DOI
Hemal Dey, Wanyun Shao, Hamid Moradkhani

et al.

Natural Hazards, Journal Year: 2024, Volume and Issue: 120(11), P. 10365 - 10393

Published: April 23, 2024

Language: Английский

Citations

11
Forecasting of flash flood susceptibility mapping using random forest regression model and geographic information systems DOI Creative Commons
Mohamed Wahba,

Radwa Essam,

Mustafa El-Rawy

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(13), P. e33982 - e33982

Published: July 1, 2024

Flash floods, rapid and devastating inundations of water, are increasingly linked to the intensifying effects climate change, posing significant challenges for both vulnerable communities sustainable environmental management. The primary goal this research is investigate predict a Flood Susceptibility Map (FSM) Ibaraki prefecture in Japan. This utilizes Random Forest (RF) regression model GIS, incorporating 11 variables (involving elevation, slope, aspect, distance stream, river, road, land cover, topographic wetness index, stream power plan profile curvature), alongside dataset comprising 224 instances flooded non-flooded locations. data was randomly classified into 70 % training set development, with remaining 30 used validation through Receiver Operating Characteristics (ROC) curve analysis. resulting map indicated that approximately two-thirds as exhibiting low very flood susceptibility, while one-fifth region categorized high susceptibility. Furthermore, RF achieved noteworthy an area under ROC 99.56 %. Ultimately, FSM serves crucial tool policymakers guiding appropriate spatial planning mitigation strategies.

Language: Английский

Citations

11
Enhancing Flood Risk Analysis in Harris County: Integrating Flood Susceptibility and Social Vulnerability Mapping DOI
Hemal Dey, Wanyun Shao, Md. Munjurul Haque

et al.

Journal of Geovisualization and Spatial Analysis, Journal Year: 2024, Volume and Issue: 8(1)

Published: May 22, 2024

Language: Английский

Citations

10