GROUNDWATER POTENTIAL ASSESSMENT IN GIA LAI PROVINCE (VIETNAM) USING MACHINE LEARNING, REMOTE SENSING AND GIS DOI Open Access
Huu Duy Nguyen,

Van Trong Giang,

Quang-Hai TRUONG

et al.

Geographia Technica, Journal Year: 2024, Volume and Issue: 19(2/2024), P. 13 - 32

Published: May 15, 2024

Population growth, urbanization and rapid industrial development increase the demand for water resources.Groundwater is an important resource in sustainable socio-economic development.The identification of regions with probability existence groundwater necessary helping decision makers to propose effective strategies management this resource.The objective study construct maps potential groundwater, based on machine learning algorithms, namely deep neural networks (DNNs), XGBoost (XGB), CatBoost (CB), Gia Lai province Vietnam.In study, 12 conditioning factors, elevation, aspect, curvature, slope, soil type, river density, distance road, land use/land cover (LULC), Normalized Difference Vegetation Index (NDVI), Normal Built-up (NDBI), Water (NDWI), rainfall were used, along 181 inventory points, models.The proposed models evaluated using receiver operating characteristic (ROC) curve, area under curve (AUC), root-mean-square error (RMSE), mean absolute (MAE).The results showed that predictions most accurate XGB model; CB came second, DNN was performed least well.About 4,990 km² found be category very low potential; 3,045 category; 2,426 classified as moderate, 2,665 high, 2,007 high.The methodology used creating maps.This approach, can provide valuable information factors influencing assist decisionmakers or developers managing resources sustainably.It also supports territory, including tourism.This other geographic a small change input data.

Language: Английский

Modelling spatiotemporal patterns of wildfire risk in the Garden Route District biodiversity hotspots using analytic hierarchy process in South Africa DOI Creative Commons

Phindile Siyasanga Shinga,

Solomon G. Tesfamichael,

Phila Sibandze

et al.

Natural Hazards, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 29, 2024

Abstract The increasing frequency and intensity of wildfires necessitate effective risk management in biodiversity hotspots to mitigate the potential impacts wildfire hazards. study utilised a multi-criteria decision analysis-analytic hierarchy process (MCDA-AHP) model analyse patterns Garden Route District (GRD), focusing on Western Cape, South Africa. used weight assignment overlay analysis evaluate factors, including human, topographic, climatic using data from Landsat WorldClim 1991 2021. was validated MODIS historical fire Global Forest Watch database Confusion Matrix, with burned area extent identified differenced Normalized Burn Ratio (dNBR). results show that despite 53% most area, only 12% burned, high-risk zone accounting for 11%, indicating higher likelihood spreading intensifying. reveal weak positive correlation (r = 0.28) between occurrences areas negative − 0.27) seasons. Human factors significantly impact propagation zones, while topographic have less influence, lower ignition. findings 26% zones southwestern region dominated GRD hotspots, 27% were low-moderate-risk northwestern parts. this can aid assigning risk-based criterion weights support decision-makers regional global prevention management.

Language: Английский

Citations

4
Flood susceptibility assessment using deep neural networks and open-source spatial datasets in transboundary river basin DOI
Huu Duy Nguyen, Dinh Kha Dang,

H Truong

et al.

VIETNAM JOURNAL OF EARTH SCIENCES, Journal Year: 2025, Volume and Issue: unknown

Published: April 16, 2025

The Mekong Basin is the most critical transboundary river basin in Asia. This provides an abundant source of fresh water essential for development agriculture, domestic consumption, and industry, as well production hydroelectricity, it also contributes to ensuring food security worldwide. region often subject floods that cause significant damage human life, society, economy. However, flood risk management challenges this are increasingly substantial due conflicting objectives between several countries data sharing. study integrates deep learning with optimization algorithms, namely Grasshopper Optimisation Algorithm (GOA), Adam Stochastic Gradient Descent (SGD), open-source datasets identify probably occurring basin, covering Vietnam Cambodia. Various statistical indices, Area Under Curve (AUC), root mean square error (RMSE), absolute (MAE), coefficient determination (R²), were used evaluate susceptibility models. results show proposed models performed AUC values above 0.8, specifying DNN-Adam model achieved 0.98, outperforming DNN-GOA (AUC = 0.89), DNN-SGD 0.87), XGB 0.82. Regions very high concentrated Delta along River findings supporting decision-makers or planners proposing appropriate mitigation strategies, planning policies, particularly watershed.

Language: Английский

Citations

0
Assessing Critical Flood-Prone Districts and Optimal Shelter Zones in the Brahmaputra Valley: Strategies for Effective Flood Risk Management DOI
Jatan Debnath, Dhrubajyoti Sahariah, Gowhar Meraj

et al.

Physics and Chemistry of the Earth Parts A/B/C, Journal Year: 2024, Volume and Issue: unknown, P. 103772 - 103772

Published: Oct. 1, 2024

Language: Английский

Citations

2
Flood assessment using machine learning and its implications for coastal spatial planning in Phu Yen Province, Vietnam DOI Creative Commons
Van Truong Tran, Huu Duy Nguyen,

Dang Thi Ngoc

et al.

Journal of Water and Climate Change, Journal Year: 2024, Volume and Issue: 15(8), P. 3738 - 3761

Published: July 22, 2024

ABSTRACT The objective of this study was the development a new machine learning model using radial basis function neural network (RBFNN) to build flood susceptibility maps and damage assessment for Phu Yen province Vietnam. built will be optimized by five algorithms, namely Giant Trevally Optimization (GTO), Golden Jackal (GJO), Brown-Bear (BBO), Gray Wolf Optimizer (GWO), Whale Algorithm (WOA) find out best establish map. These models were evaluated statistical indices such as root mean square error (RMSE), absolute (MAE), receiver operating characteristic (ROC), area under curve (AUC), coefficient determination (COD). result showed that all optimization algorithms successfully improving performance RBFNN model, among them hybrid RBFNN–BBO has highest with AUC = 0.998 R2 0.8 RBFNN–GTO lowest 0.755 0.65. regions identified high- very-high (1,075 km2) concentrated on plain along three largest rivers in province.

Language: Английский

Citations

1
SAR-driven flood inventory and multi-factor ensemble susceptibility modelling using machine learning frameworks DOI Creative Commons

Krishnagopal Halder,

Anitabha Ghosh,

Amit Kumar Srivastava

et al.

Geomatics Natural Hazards and Risk, Journal Year: 2024, Volume and Issue: 15(1)

Published: Oct. 16, 2024

Climate change has substantially increased both the occurrence and intensity of flood events, particularly in Indian subcontinent, exacerbating threats to human populations economic infrastructure. The present research employed novel ML models—LR, SVM, RF, XGBoost, DNN, Stacking Ensemble—developed Python environment leveraged 18 flood-influencing factors delineate flood-prone areas with precision. A comprehensive inventory, obtained from Sentinel-1 Synthetic Aperture Radar (SAR) data using Google Earth Engine (GEE) platform, provided empirical for entire model training validation. Model performance was assessed precision, recall, F1-score, accuracy, ROC-AUC metrics. results highlighted Ensemble's superior predictive ability (0.965), followed closely by, XGBoost (0.934), DNN (0.929), RF (0.925), LR (0.921), SVM (0.920) respectively, establishing feasibility applications disaster management. maps depicting susceptibility flooding generated by current provide actionable insights decision-makers, city planners, authorities responsible management, guiding infrastructural community resilience enhancements against risks.

Language: Английский

Citations

1
Cutting-Edge strategies for absence data identification in natural hazards: Leveraging Voronoi-Entropy in flood susceptibility mapping with advanced AI techniques DOI
Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi‐Niaraki, Farman Ali

et al.

Journal of Hydrology, Journal Year: 2024, Volume and Issue: unknown, P. 132337 - 132337

Published: Nov. 1, 2024

Language: Английский

Citations

1
Flash flood susceptibility modeling using optimized deep learning method in the Uttarakhand Himalayas DOI
Mohd Rihan, Javed Mallick,

Intejar Ansari

et al.

Earth Science Informatics, Journal Year: 2024, Volume and Issue: 18(1)

Published: Dec. 11, 2024

Language: Английский

Citations

1
Feature selection using modified chaotic satin bowerbird algorithm with deep transfer learning for Multispectral Image Classification DOI

M. Rajakani,

R Kavitha,

S. Rajesh

et al.

International Journal of Information Technology, Journal Year: 2024, Volume and Issue: unknown

Published: May 9, 2024

Language: Английский

Citations

0
GROUNDWATER POTENTIAL ASSESSMENT IN GIA LAI PROVINCE (VIETNAM) USING MACHINE LEARNING, REMOTE SENSING AND GIS DOI Open Access
Huu Duy Nguyen,

Van Trong Giang,

Quang-Hai TRUONG

et al.

Geographia Technica, Journal Year: 2024, Volume and Issue: 19(2/2024), P. 13 - 32

Published: May 15, 2024

Population growth, urbanization and rapid industrial development increase the demand for water resources.Groundwater is an important resource in sustainable socio-economic development.The identification of regions with probability existence groundwater necessary helping decision makers to propose effective strategies management this resource.The objective study construct maps potential groundwater, based on machine learning algorithms, namely deep neural networks (DNNs), XGBoost (XGB), CatBoost (CB), Gia Lai province Vietnam.In study, 12 conditioning factors, elevation, aspect, curvature, slope, soil type, river density, distance road, land use/land cover (LULC), Normalized Difference Vegetation Index (NDVI), Normal Built-up (NDBI), Water (NDWI), rainfall were used, along 181 inventory points, models.The proposed models evaluated using receiver operating characteristic (ROC) curve, area under curve (AUC), root-mean-square error (RMSE), mean absolute (MAE).The results showed that predictions most accurate XGB model; CB came second, DNN was performed least well.About 4,990 km² found be category very low potential; 3,045 category; 2,426 classified as moderate, 2,665 high, 2,007 high.The methodology used creating maps.This approach, can provide valuable information factors influencing assist decisionmakers or developers managing resources sustainably.It also supports territory, including tourism.This other geographic a small change input data.

Language: Английский

Citations

0