Solving the spatial extrapolation problem in flood susceptibility using hybrid machine learning, remote sensing, and GIS.

Floods are arguably the most impactful of natural hazards. The increasing magnitude of their effects on the environment, human life, and economic activities calls for improved management of water resources. Flood susceptibility modeling has been used around the world to reduce the damage caused by flooding, although the extrapolation problem still presents a significant challenge. This study develops a machine learning (ML) model utilizing deep neural network (DNN) and optimization algorithms, namely earthworm optimization algorithm (EOA), wildebeest herd optimization (WHO), biogeography-based optimization (BBO), satin bowerbird optimizer (SBO), grasshopper optimization algorithm (GOA), and particle swarm optimization (PSO), to solve the extrapolation problem in the construction of flood susceptibility models. Quang Nam Province was chosen as a case study as it is subject to the significant impact of intense flooding, and Nghe An Province was selected as the region for extrapolation of the flood susceptibility model. Root mean square error (RMSE), receiver operating characteristic (ROC), the area under the ROC curve (AUC), and accuracy (ACC) were applied to assess and compare the fit of each of the models. The results indicated that the models in this study are a good fit in establishing flood susceptibility maps, all with AUC > 0.9. The deep neural network (DNN)-BBO model enjoyed the best results (AUC = 0.99), followed by DNN-WHO (AUC = 0.99), DNN-SBO (AUC = 0.98), DNN-EOA (AUC = 0.96), DNN-GOA (AUC = 0.95), and finally, DNN-PSO (AUC = 0.92). In addition, the models successfully solved the extrapolation problem. These new models can modify their behavior to evaluate flood susceptibility in different regions of the world. The models in this study distribute a first point of reference for debate on the solution to the extrapolation problem, which can support urban planners and other decision-makers in other coastal regions in Vietnam and other countries.

A novel flood risk management approach based on future climate and land use change scenarios.

Climate change and increasing urbanization are two primary factors responsible for the increased risk of serious flooding around the world. The prediction and monitoring of the effects of land use/land cover (LULC) and climate change on flood risk are critical steps in the development of appropriate strategies to reduce potential damage. This study aimed to develop a new approach by combining machine learning (namely the XGBoost, CatBoost, LightGBM, and ExtraTree models) and hydraulic modeling to predict the effects of climate change and LULC change on land that is at risk of flooding. For the years 2005, 2020, 2035, and 2050, machine learning was used to model and predict flood susceptibility under different scenarios of LULC, while hydraulic modeling was used to model and predict flood depth and flood velocity, based on the RCP 8.5 climate change scenario. The two elements were used to build a flood risk assessment, integrating socioeconomic data such as LULC, population density, poverty rate, number of women, number of schools, and cultivated area. Flood risk was then computed, using the analytical hierarchy process, by combining flood hazard, exposure, and vulnerability. The results showed that the area at high and very high flood risk increased rapidly, as did the areas of high/very high exposure, and high/very high vulnerability. They also showed how flood risk had increased rapidly from 2005 to 2020 and would continue to do so in 2035 and 2050, due to the dynamics of climate change and LULC change, population growth, the number of women, and the number of schools - particularly in the flood zone. The results highlight the relationships between flood risk and environmental and socio-economic changes and suggest that flood risk management strategies should also be integrated in future analyses. The map built in this study shows past and future flood risk, providing insights into the spatial distribution of urban area in flood zones and can be used to facilitate the development of priority measures, flood mitigation being most important.

An open dataset on individual perceptions of transport policies.

Many cities are facing challenges caused by the increasing use of motorised transport and Hanoi, Vietnam, is no exception. The proliferation of petrol powered motorbikes has caused serious problems of congestion, pollution, and road safety. This paper reports on a new survey dataset that was created as part of the Urban Transport Modelling for Sustainable Well-Being in Hanoi (UTM-Hanoi) project. The survey of nearly 30,000 respondents gathers data on households' demographics, perceptions, opinions and stated behaviours. The data are informative in their own right and have also been used to experiment with multi-scale spatial statistics, synthetic population generation and machine learning approaches to predicting an individual's perceptions of potential government policies. The paper reports on the key findings from the survey and conducts a technical validation to contrast the outcomes to similar datasets that are available.

Soil salinity prediction using hybrid machine learning and remote sensing in Ben Tre province on Vietnam's Mekong River Delta.

Soil salinization is considered one of the disasters that have significant effects on agricultural activities in many parts of the world, particularly in the context of climate change and sea level rise. This problem has become increasingly essential and severe in the Mekong River Delta of Vietnam. Therefore, soil salinity monitoring and assessment are critical to building appropriate strategies to develop agricultural activities. This study aims to develop a low-cost method based on machine learning and remote sensing to map soil salinity in Ben Tre province, which is located in Vietnam's Mekong River Delta. This objective was achieved by using six machine learning algorithms, including Xgboost (XGR), sparrow search algorithm (SSA), bird swarm algorithm (BSA), moth search algorithm (MSA), Harris hawk optimization (HHO), grasshopper optimization algorithm (GOA), particle swarm optimization algorithm (PSO), and 43 factors extracted from remote sensing images. Various indices were used, namely, root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2) to estimate the efficiency of the prediction models. The results show that six optimization algorithms successfully improved XGR model performance with an R2 value of more than 0.98. Among the proposed models, the XGR-HHO model was better than the other models with a value of R2 of 0.99 and a value of RMSE of 0.051, by XGR-GOA (R2 = 0.931, RMSE = 0.055), XGR-MSA (R2 = 0.928, RMSE = 0.06), XGR-BSA (R2 = 0.926, RMSE = 0.062), XGR-SSA (R2 = 0.917, 0.07), XGR-PSO (R2 = 0.916, RMSE = 0.08), XGR (R2 = 0.867, RMSE = 0.1), CatBoost (R2 = 0.78, RMSE = 0.12), and RF (R2 = 0.75, RMSE = 0.19), respectively. These proposed models have surpassed the reference models (CatBoost and random forest). The results indicated that the soils in the eastern areas of Ben Tre province are more saline than in the western areas. The results of this study highlighted the effectiveness of using hybrid machine learning and remote sensing in soil salinity monitoring. The finding of this study provides essential tools to support farmers and policymakers in selecting appropriate crop types in the context of climate change to ensure food security.

Coastal landscape classification using convolutional neural network and remote sensing data in Vietnam.

The length of global coastline is about 356 thousand kilometers with various dynamic natural and anthropogenic. Although the number of studies on coastal landscape categorization has been increasing, it is still difficult to distinguish precisely them because the used methods commonly are traditional qualitative ones. With the leverage of remote sensing data and GIS tools, it helps categorize and identify a variety of features on land and water based on multi-source data. The aim of study is using different natural - social profile data obtained from ALOS, NOAA, and multi-temporal Landsat satellite images as input data of the convolutional-neural-network (CvNet) models for coastal landscape classification. Studies used 900 cut-line samples which represent coastal landscapes in Vietnam for training and optimizing CvNet models. As a result, nine coastal landscapes were identified including: deltas, alluvial, mature and young sand dunes, cliff, lagoon, tectonic, karst, and transitional landscapes. Three CvNet models using three different optimizer types classified the landscapes of other 1150 cut-lines in Vietnam with the accuracies about 98% and low loss function value. Excepting dalmatian, karst and delta coastal landscapes, five others distribute heterogeneous along the coasts in Vietnam. Therefore, the evaluation of additional natural components is necessary and CvNet model have ability to update new landscape types in variety of tropical nation as a step toward coastal landscape classification at both national and global scales.

Analysis of Periodontal Conditions in the Provinces of Vietnam: Results From the National Dental Survey.

Nationwide dental health surveys are crucial for providing essential information on dental health and dental condition-related problems in the community. However, the relationship between periodontal conditions and sociodemographic data has not been well investigated in Vietnam. With data from the National Oral Health Survey in 2019, we performed several machine learning methods on this dataset to investigate the impacts of sociodemographic features on gingival bleeding, periodontal pockets, and Community Periodontal Index. From the experiments, LightGBM produced a maximum AUC (area under the curve) value of 0.744. The other models in descending order were logistic regression (0.705), logiboost (0.704), and random forest (0.684). All methods resulted in significantly high overall accuracies, all exceeding 90%. The results show that the gradient boosting model can predict well the relationship between periodontal conditions and sociodemographic data. The investigated model also reveals that the geographic region has the most significant influence on dental health, while the consumption of sweet foods/drinks is the second most crucial. These findings advocate for a region-specific approach for the dental care program and the implementation of a sugar-risk food reduction program.

Mangrove forest classification and aboveground biomass estimation using an atom search algorithm and adaptive neuro-fuzzy inference system.

BACKGROUND: Advances in earth observation and machine learning techniques have created new options for forest monitoring, primarily because of the various possibilities that they provide for classifying forest cover and estimating aboveground biomass (AGB). METHODS: This study aimed to introduce a novel model that incorporates the atom search algorithm (ASO) and adaptive neuro-fuzzy inference system (ANFIS) into mangrove forest classification and AGB estimation. The Ca Mau coastal area was selected as a case study since it has been considered the most preserved mangrove forest area in Vietnam and is being investigated for the impacts of land-use change on forest quality. The model was trained and validated with a set of Sentinel-1A imagery with VH and VV polarizations, and multispectral information from the SPOT image. In addition, feature selection was also carried out to choose the optimal combination of predictor variables. The model performance was benchmarked against conventional methods, such as support vector regression, multilayer perceptron, random subspace, and random forest, by using statistical indicators, namely, root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). RESULTS: The results showed that all three indicators of the proposed model were statistically better than those from the benchmarked methods. Specifically, the hybrid model ended up at RMSE = 70.882, MAE = 55.458, R2 = 0.577 for AGB estimation. CONCLUSION: From the experiments, such hybrid integration can be recommended for use as an alternative solution for biomass estimation. In a broader context, the fast growth of metaheuristic search algorithms has created new scientifically sound solutions for better analysis of forest cover.