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View article: One-dimensional deep learning driven geospatial analysis for flash flood susceptibility mapping: a case study in North Central Vietnam
One-dimensional deep learning driven geospatial analysis for flash flood susceptibility mapping: a case study in North Central Vietnam Open
Flash floods rank among the most catastrophic natural disasters worldwide, inflicting severe socio-economic, environmental, and human impacts. Consequently, accurately identifying areas at potential risk is of paramount importance. This st…
View article: Impacts of Resampling and Downscaling Digital Elevation Model and Its Morphometric Factors: A Comparison of Hopfield Neural Network, Bilinear, Bicubic, and Kriging Interpolations
Impacts of Resampling and Downscaling Digital Elevation Model and Its Morphometric Factors: A Comparison of Hopfield Neural Network, Bilinear, Bicubic, and Kriging Interpolations Open
The digital elevation model (DEM) and its derived morphometric factors, i.e., slope, aspect, profile and plan curvatures, and topographic wetness index (TWI), are essential for natural hazard modeling and prediction as they provide critica…
View article: A New Approach Based on Deep Neural Networks and Multisource Geospatial Data for Spatial Prediction of Groundwater Spring Potential
A New Approach Based on Deep Neural Networks and Multisource Geospatial Data for Spatial Prediction of Groundwater Spring Potential Open
Groundwater spring plays a crucial role in human life, including water resource management and planning; therefore, developing accurate prediction models for groundwater spring potential mapping is essential. The objective of this research…
View article: Utilization of Augmented Reality Technique for Sewer Condition Visualization
Utilization of Augmented Reality Technique for Sewer Condition Visualization Open
Wastewater pipelines are largely buried underground, and techniques for assessing and visualizing their condition are critical for planning and rehabilitation. This paper introduces a framework for integrating Geographic Information System…
View article: Spatial Prediction of Fluvial Flood in High-Frequency Tropical Cyclone Area Using TensorFlow 1D-Convolution Neural Networks and Geospatial Data
Spatial Prediction of Fluvial Flood in High-Frequency Tropical Cyclone Area Using TensorFlow 1D-Convolution Neural Networks and Geospatial Data Open
Fluvial floods endure as one of the most catastrophic weather-induced disasters worldwide, leading to numerous fatalities each year and significantly impacting socio-economic development and the environment. Hence, the research and develop…
View article: Comparative Analysis of Deep Learning and Swarm-Optimized Random Forest for Groundwater Spring Potential Identification in Tropical Regions
Comparative Analysis of Deep Learning and Swarm-Optimized Random Forest for Groundwater Spring Potential Identification in Tropical Regions Open
Identifying areas with high groundwater spring potential is crucial as it enables better decision-making concerning water supply, sustainable development, and the protection of sensitive ecosystems; therefore, it is necessary to predict th…
View article: A novel semantic segmentation approach based on U-Net, WU-Net, and U-Net++ deep learning for predicting areas sensitive to pluvial flood at tropical area
A novel semantic segmentation approach based on U-Net, WU-Net, and U-Net++ deep learning for predicting areas sensitive to pluvial flood at tropical area Open
Floods remain one of the most devastating weather-induced disasters worldwide, resulting in numerous fatalities each year and severely impacting socio-economic development and the environment. Therefore, the ability to predict flood-prone …
View article: A novel HHO-RSCDT ensemble learning approach for forest fire danger mapping using GIS
A novel HHO-RSCDT ensemble learning approach for forest fire danger mapping using GIS Open
Accurate prediction models for spatial prediction of forest fire danger play a vital role in predicting forest fires, which can help prevent and mitigate the detrimental effects of such disasters. This research aims to develop a new ensemb…
View article: A New Approach Based on TensorFlow Deep Neural Networks with ADAM Optimizer and GIS for Spatial Prediction of Forest Fire Danger in Tropical Areas
A New Approach Based on TensorFlow Deep Neural Networks with ADAM Optimizer and GIS for Spatial Prediction of Forest Fire Danger in Tropical Areas Open
Frequent forest fires are causing severe harm to the natural environment, such as decreasing air quality and threatening different species; therefore, developing accurate prediction models for forest fire danger is vital to mitigate these …
View article: Hybrid BBO-DE Optimized SPAARCTree Ensemble for Landslide Susceptibility Mapping
Hybrid BBO-DE Optimized SPAARCTree Ensemble for Landslide Susceptibility Mapping Open
This paper presents a new hybrid ensemble modeling method called BBO-DE-STreeEns for land-slide susceptibility mapping in Than Uyen district, Vietnam. The method uses subbagging and random subspacing to generate subdatasets for constituent…
View article: A New Approach Based on Balancing Composite Motion Optimization and Deep Neural Networks for Spatial Prediction of Landslides at Tropical Cyclone Areas
A New Approach Based on Balancing Composite Motion Optimization and Deep Neural Networks for Spatial Prediction of Landslides at Tropical Cyclone Areas Open
Landslides are a significant geological hazard that annually cause extensive damage and loss of life worldwide. Therefore, it is crucial to have reliable prediction models for landslide susceptibility in order to identify high-risk areas a…
View article: Assessment of the TREELIM model in predicting present treeline along a longitudinal continentality-maritimity gradient in south-western Norway
Assessment of the TREELIM model in predicting present treeline along a longitudinal continentality-maritimity gradient in south-western Norway Open
Treelines driven by climatic conditions are characteristic of alpine areas. This study assesses the degree of accuracy with which the TREELIM model predicts present treeline elevation along a regional continentality-maritimity gradient in …
View article: Predicting Discharges in Sewer Pipes Using an Integrated Long Short-Term Memory and Entropy A-TOPSIS Modeling Framework
Predicting Discharges in Sewer Pipes Using an Integrated Long Short-Term Memory and Entropy A-TOPSIS Modeling Framework Open
Predicting discharges in sewage systems play an essential role in reducing sewer overflows and impacts on the environment and public health. Choosing a suitable model to predict discharges in these systems is essential to realizing these a…
View article: Comparison of Machine Learning Techniques for Condition Assessment of Sewer Network
Comparison of Machine Learning Techniques for Condition Assessment of Sewer Network Open
Assessment of sewer condition is one of the critical steps in asset management and support investment decisions; therefore, condition assessment models with high accuracy are important that can help utility managers and other authorities c…
View article: A hybridized model based on neural network and swarm intelligence-grey wolf algorithm for spatial prediction of urban flood-inundation
A hybridized model based on neural network and swarm intelligence-grey wolf algorithm for spatial prediction of urban flood-inundation Open
View article: Author Correction: Novel Hybrid Evolutionary Algorithms for Spatial Prediction of Floods
Author Correction: Novel Hybrid Evolutionary Algorithms for Spatial Prediction of Floods Open
View article: Evaluating the predictive power of different machine learning algorithms for groundwater salinity prediction of multi-layer coastal aquifers in the Mekong Delta, Vietnam
Evaluating the predictive power of different machine learning algorithms for groundwater salinity prediction of multi-layer coastal aquifers in the Mekong Delta, Vietnam Open
View article: Development of a novel hybrid multi-boosting neural network model for spatial prediction of urban flood
Development of a novel hybrid multi-boosting neural network model for spatial prediction of urban flood Open
In this study, a new hybridized machine learning algorithm for urban flood susceptibility mapping, named MultiB-MLPNN, was developed using a multi-boosting technique and MLPNN. The model was tested in Amol City, Iran, a data-scarce city in…
View article: First comprehensive quantification of annual land use/cover from 1990 to 2020 across mainland Vietnam
First comprehensive quantification of annual land use/cover from 1990 to 2020 across mainland Vietnam Open
Extensive studies have highlighted a need for frequently consistent land cover information for interdisciplinary studies. This paper proposes a comprehensive framework for the automatic production of the first Vietnam-wide annual land use/…
View article: Thirty-Year Dynamics of LULC at the Dong Thap Muoi Area, Southern Vietnam, Using Google Earth Engine
Thirty-Year Dynamics of LULC at the Dong Thap Muoi Area, Southern Vietnam, Using Google Earth Engine Open
The main purpose of this paper is to assess the land use and land cover (LULC) changes for thirty years, from 1990–2020, in the Dong Thap Muoi, a flooded land area of the Mekong River Delta of Vietnam using Google Earth Engine and random f…
View article: Deep learning neural networks for spatially explicit prediction of flash flood probability
Deep learning neural networks for spatially explicit prediction of flash flood probability Open
View article: Mapping wind erosion hazard with regression-based machine learning algorithms
Mapping wind erosion hazard with regression-based machine learning algorithms Open
View article: Flash flood susceptibility mapping using a novel deep learning model based on deep belief network, back propagation and genetic algorithm
Flash flood susceptibility mapping using a novel deep learning model based on deep belief network, back propagation and genetic algorithm Open
View article: An advanced meta-learner based on artificial electric field algorithm optimized stacking ensemble techniques for enhancing prediction accuracy of soil shear strength
An advanced meta-learner based on artificial electric field algorithm optimized stacking ensemble techniques for enhancing prediction accuracy of soil shear strength Open
Shear strength is a crucial property of soils regarded as its intrinsic capacity to resist failure when forces act on the soil mass. This study proposes an advanced meta-leaner to discern the shear strength property and generate a reliable…
View article: Novel Credal Decision Tree-Based Ensemble Approaches for Predicting the Landslide Susceptibility
Novel Credal Decision Tree-Based Ensemble Approaches for Predicting the Landslide Susceptibility Open
Landslides are natural and often quasi-normal threats that destroy natural resources and may lead to a persistent loss of human life. Therefore, the preparation of landslide susceptibility maps is necessary in order to mitigate harmful eff…
View article: Novel Ensemble of Multivariate Adaptive Regression Spline with Spatial Logistic Regression and Boosted Regression Tree for Gully Erosion Susceptibility
Novel Ensemble of Multivariate Adaptive Regression Spline with Spatial Logistic Regression and Boosted Regression Tree for Gully Erosion Susceptibility Open
The extreme form of land degradation through different forms of erosion is one of the major problems in sub-tropical monsoon dominated region. The formation and development of gullies is the dominant form or active process of erosion in th…
View article: Coastal Wetland Classification with Deep U-Net Convolutional Networks and Sentinel-2 Imagery: A Case Study at the Tien Yen Estuary of Vietnam
Coastal Wetland Classification with Deep U-Net Convolutional Networks and Sentinel-2 Imagery: A Case Study at the Tien Yen Estuary of Vietnam Open
The natural wetland areas in Vietnam, which are transition areas from inland and ocean, play a crucial role in minimizing coastal hazards; however, during the last two decades, about 64% of these areas have been converted from the natural …
View article: Novel Machine Learning Approaches for Modelling the Gully Erosion Susceptibility
Novel Machine Learning Approaches for Modelling the Gully Erosion Susceptibility Open
The extreme form of land degradation caused by the formation of gullies is a major challenge for the sustainability of land resources. This problem is more vulnerable in the arid and semi-arid environment and associated damage to agricultu…
View article: A New Hybrid Firefly–PSO Optimized Random Subspace Tree Intelligence for Torrential Rainfall-Induced Flash Flood Susceptible Mapping
A New Hybrid Firefly–PSO Optimized Random Subspace Tree Intelligence for Torrential Rainfall-Induced Flash Flood Susceptible Mapping Open
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 mapp…
View article: Optimization of state-of-the-art fuzzy-metaheuristic ANFIS-based machine learning models for flood susceptibility prediction mapping in the Middle Ganga Plain, India
Optimization of state-of-the-art fuzzy-metaheuristic ANFIS-based machine learning models for flood susceptibility prediction mapping in the Middle Ganga Plain, India Open