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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 the groundwater spring potential with highly accurate models. This study aims to assess and compare the effectiveness of deep neural networks (DeepNNs) and swarm-optimized random forests (SwarmRFs) in predicting groundwater spring potential. This study focuses on a case study conducted in the Gia Lai province, located in the Central Highland of Vietnam. To accomplish this objective, a comprehensive groundwater database was compiled, comprising 938 groundwater spring locations and 12 influential variables, namely land use and land cover (LULC), geology, distance to fault, distance to river, rainfall, normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI), normalized difference water index (NDWI), slope, aspect, elevation, and curvature. The DeepNN model was trained and fine-tuned using the Adaptive Moment Estimation (ADAM) optimizer, while the SwarmRF model employed the Harris Hawks Optimizer (HHO) to search for optimal parameters. The results indicate that both the DeepNN model (accuracy = 77.9%, F-score = 0.783, kappa = 0.559, and AUC = 0.820) and the SwarmRF model (accuracy = 80.2%, F-score = 0.798, kappa = 0.605, and AUC = 0.854) exhibit robust predictive capabilities. The SwarmRF model displays a slight advantage over the DeepNN model in terms of performance. Among the 12 influential factors, geology emerges as the most significant determinant of groundwater spring potential. The groundwater spring potential maps generated through this research can offer valuable information for local authorities to facilitate effective water resource management and support sustainable development planning. [ABSTRACT FROM AUTHOR]

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 impacts. This research proposes and evaluates a new modeling approach based on TensorFlow deep neural networks (TFDeepNN) and geographic information systems (GIS) for forest fire danger modeling. Herein, TFDeepNN was used to create a forest fire danger model, whereas the adaptive moment estimation (ADAM) optimization algorithm was used to optimize the model, and GIS with Python programming was used to process, classify, and code the input and output. The modeling focused on the tropical forests of the Phu Yen Province (Vietnam), which incorporates 306 historical forest fire locations from 2019 to 2023 and ten forest-fire-driving factors. Random forests (RF), support vector machines (SVM), and logistic regression (LR) were used as a baseline for the model comparison. Different statistical metrics, such as F-score, accuracy, and area under the ROC curve (AUC), were employed to evaluate the models' predictive performance. According to the results, the TFDeepNN model (with F-score of 0.806, accuracy of 79.3%, and AUC of 0.873) exhibits high predictive performance and surpasses the performance of the three baseline models: RF, SVM, and LR; therefore, TFDeepNN represents a novel tool for spatially predicting forest fire danger. The forest fire danger map from this study can be helpful for policymakers and authorities in Phu Yen Province, aiding sustainable land-use planning and management. [ABSTRACT FROM AUTHOR]

The safety operation and management of hydropower dam play a critical role in social‐economic development and ensure people's safety in many countries; therefore, modeling and forecasting the hydropower dam's deformations with high accuracy is crucial. This research aims to propose and validate a new model based on deep learning long short‐term memory (LSTM) and the coronavirus optimization algorithm (CVOA), named CVOA‐LSTM, for forecasting the deformations of the hydropower dam. The second‐largest hydropower dam of Vietnam, located in the Hoa Binh province, is focused. Herein, we used the LSTM to establish the deformation model, whereas the CVOA was utilized to optimize the three parameters of the LSTM, the number of hidden layers, the learning rate, and the dropout. The efficacy of the proposed CVOA‐LSTM model is assessed by comparing its forecasting performance with state‐of‐the‐art benchmarks, sequential minimal optimization for support vector regression, Gaussian process, M5' model tree, multilayer perceptron neural network, reduced error pruning tree, random tree, random forest, and radial basis function neural network. The result shows that the proposed CVOA‐LSTM model has high forecasting capability (R2 = 0.874, root mean square error = 0.34, mean absolute error = 0.23) and outperforms the benchmarks. We conclude that CVOA‐LSTM is a new tool that can be considered to forecast the hydropower dam's deformations. [ABSTRACT FROM AUTHOR]

A new hybrid model approach based on Imperialist Competitive Algorithm, a socio-politically optimization, and neural computing networks (ICA-NeuralNet) was developed and proposed in this study with the aim is to improve the quality of the shallow landslide susceptibility assessment at the Ha Long city area, Quang Ninh province. This area, which belongs to one of the three key economic regions of Vietnam, has a high urbanization speed during the last ten years. However, the landslide has been a significant environmental hazard problem during the last five years due to extreme torrential rainstorms. For this regard, a geographic information system (GIS) database was established, which contains 170 landslide polygons that occurred during the last five years and ten influencing factors. The database was used for training and validating the ICA-NeuralNet model. The results showed that the integrated model achieves high performance with classification accuracy rates of 82.4% on the training dataset and 78.2% on the testing dataset. Therefore, the ICA-NeuralNet is subsequently employed for generating a landslide susceptibility map of the study area, which greatly supports the land-use planning as well as hazard mitigation/prevention of local authority. [ABSTRACT FROM AUTHOR]

This research proposes an alternative for estimating shear strength of soil based on a hybridization of Support Vector Regression (SVR) and Particle Swarm Optimization (PSO). SVR is used as a function approximation method for making prediction of the soil shear strength based on a set of twelve variables including sample depth, sand content, loam content clay content, moisture content, wet density, dry density, void ratio, liquid limit, plastic limit, plastic index, and liquid index. The hybrid framework, named as PSO–SVR, relies on PSO, as a metaheuristic, to optimize the training phase of the employed function approximator. A data set consisting of 443 soil samples associated with the experimental results of shear strength has been collected from a housing project in Vietnam. This data set is then used to train and verify the performance of the PSO–SVR model specifically constructed for shear strength estimation. The hybrid model has achieved a good modeling outcome with Root Mean Square Error (RMSE) = 0.038, Mean Absolute Percentage Error (MAPE) = 9.701%, and Coefficient of Determination (R2) = 0.888. Hence, the PSO–SVR model can be a potential alternative to be participated in the design phase of high-rise housing projects. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Flash floods induced by torrential rainfalls are considered one of the most dangerous natural hazards, due to their sudden occurrence and high magnitudes, which may cause huge damage to people and properties. This study proposed a novel modeling approach for spatial prediction of flash floods based on the tree intelligence-based CHAID (Chi-square Automatic Interaction Detector)random subspace, optimized by biogeography-based optimization (the CHAID-RS-BBO model), using remote sensing and geospatial data. In this proposed approach, a forest of tree intelligence was constructed through the random subspace ensemble, and, then, the swarm intelligence was employed to train and optimize the model. The Luc Yen district, located in the northwest mountainous area of Vietnam, was selected as a case study. For this circumstance, a flood inventory map with 1866 polygons for the district was prepared based on Sentinel-1 synthetic aperture radar (SAR) imagery and field surveys with handheld GPS. Then, a geospatial database with ten influencing variables (land use/land cover, soil type, lithology, river density, rainfall, topographic wetness index, elevation, slope, curvature, and aspect) was prepared. Using the inventory map and the ten explanatory variables, the CHAID-RS-BBO model was trained and verified. Various statistical metrics were used to assess the prediction capability of the proposed model. The results show that the proposed CHAID-RS-BBO model yielded the highest predictive performance, with an overall accuracy of 90% in predicting flash floods, and outperformed benchmarks (i.e., the CHAID, the J48-DT, the logistic regression, and the multilayer perception neural network (MLP-NN) models). We conclude that the proposed method can accurately estimate the spatial prediction of flash floods in tropical storm areas. [ABSTRACT FROM AUTHOR]

• A new QPSO-CDTreeEns is proposed for flash flood modeling. • Ten flash flood indicators were considered. • QPSO-CDTreeEns has a high performance and better than benchmarks SVM, CART, and LR. • LULC, slope, curvature, and TWI are the most important indicators. • QPSO-CDTreeEns is a new tool for flash flood study. Flash flood is considered as one of the most destructive natural hazards worldwide, especially in tropical countries, where tropical cyclones with torrential rains are recurrent problems yearly. Therefore, an accurate prediction of susceptible areas to flash floods is crucial for developing measures to prevent, avoid, and minimize damages associated with flash floods. The aim of this research is to propose a new state-of-the-art model based on hybridizing Quantum Particle Swarm Optimization (QPSO) and the Credal Decision Tree (CDT) ensemble, namely the QPSO-CDTreeEns model, for spatial prediction of the flash flood. The concept of the proposed model is to build a forest tree of the CDT established through the Random Subspace ensemble. Therein, QPSO is integrated to optimize the three parameters, the subspace size, number of trees, and the maximum depth of trees. A district suffered from a high frequency of flash floods in the north-western mountainous area of Vietnam was selected as a case study. In this regard, a geospatial database that includes a total of 1698 flash flood and inundation polygons derived from Sentinel-1 C-band SAR images and ten input indicators were used to construct and to verify the proposed model. The result shows that the QPSO-CDT-Ens model performed well (Overall accuracy = 90.4, Kappa coefficient = 0.807) and outperformed the five machine learning algorithms in flash flood susceptibility mapping. Among the ten factors, the land-use/land-cover (LULC), the slope, the curvature, and the TWI are the most important indicators. We conclude that the proposed model is a promising tool for flash flood susceptibility mapping in the tropics and may assist decision-makers in sustainable land-use planning in the national disaster mitigation strategies. [ABSTRACT FROM AUTHOR]

Flash flood is one of the most dangerous hydrologic and natural phenomena and is considered as the top ranking of such events among various natural disasters due to their fast onset characteristics and the proportion of individual fatalities. Mapping the probability of flash flood events remains challenges because of its complexity and rapid onset of precipitation. Thus, this study aims to propose a state-of-the-art data mining approach based on a hybrid equilibrium optimized SysFor, namely, the HE-SysFor model, for spatial prediction of flash floods. A tropical storm region located in the Northwest areas of Vietnam is selected as a case study. For this purpose, 1866 flash-flooded locations and ten indicators were used. The results show that the proposed HE-SysFor model yielded the highest predictive performance (total accuracy = 93.8%, Kappa index = 0.875, F1-score = 0.939, and AUC = 0.975) and produced the better performance than those of the C4.5 decision tree (C4.5), the radial basis function-based support vector machine (SVM-RBF), the logistic regression (LReg), and deep learning neural network (DeepLNN) models in both the training and the testing phases. Among the ten indicators, elevation, slope, and land cover are the most important. It is concluded that the proposed model provides an alternative tool and may help for effectively monitoring flash floods in tropical areas and robust policies for decision making in mitigating the flash flood impacts. • HE-SysFor is proposed for flash flood modeling. • Ten flash flood indicators were considered. • Elevation, slope, land cover, and rainfall are the most important indicators. • HE-SysFor has a high performance, better than benchmarks, SVM-RBF, LReg, C4.5, and DeepLNN. • HE-SysFor and wrapper technique are new tools for flash flood study. [ABSTRACT FROM AUTHOR]