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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 predictive capability of a new artificial intelligence method, random subspace (RS), for the prediction of suspended sediment load in rivers was compared with commonly used methods: random forest (RF) and two support vector machine (SVM) models using a radial basis function kernel (SVM-RBF) and a normalized polynomial kernel (SVM-NPK). Using river discharge, rainfall and river stage data from the Haraz River, Iran, the results revealed: (a) the RS model provided a superior predictive accuracy (NSE = 0.83) to SVM-RBF (NSE = 0.80), SVM-NPK (NSE = 0.78) and RF (NSE = 0.68), corresponding to very good, good, satisfactory and unsatisfactory accuracies in load prediction; (b) the RBF kernel outperformed the NPK kernel; (c) the predictive capability was most sensitive to gamma and epsilon in SVM models, maximum depth of a tree and the number of features in RF models, classifier type, number of trees and subspace size in RS models; and (d) suspended sediment loads were most closely correlated with river discharge (PCC = 0.76). Overall, the results show that RS models have great potential in data poor watersheds, such as that studied here, to produce strong predictions of suspended load based on monthly records of river discharge, rainfall depth and river stage alone. [ABSTRACT FROM AUTHOR]

Abstract In many engineering projects, the soil compression coefficient is an important parameter used for estimating the settlement of soil layers. The common practice of determining the soil compression coefficient via the oedometer test is time-consuming and expensive. This study proposes a machine learning solution to replace the conventional tests used for obtaining the coefficient of soil compression. The new approach is an integration of the Multi-Layer Perceptron Neural Network (MLP Neural Nets) and Particle Swarm Optimization (PSO). These two computational intelligence methods work synergistically to establish a prediction model of soil compression coefficient. The PSO metaheuristic is employed to optimize the MLP Neural Nets model structure. To train and validate the proposed method, named as PSO-MLP Neural Nets, a dataset of 154 soil samples featuring 12 influencing factors has been collected from the geotechnical investigation process of a high-rise building project. Experimental results show that the proposed PSO-MLP Neural Nets has attained the most accurate prediction of the soil compression coefficient performance with RMSE = 0.0267, MAE = 0.0145, and R2 = 0.884. The result of the proposed model is significantly better than those obtained from other benchmark methods including the backpropagation neural network, the radial basis function neural network, the support vector regression, the random forest, and the Gaussian process. Based on the experimental results, the newly constructed PSO-MLP Neural Nets is very potential to be a new alternative to assist geotechnical engineers in design phase of civil engineering projects. [ABSTRACT FROM AUTHOR]

An advanced sodium-cooled fast reactor concept has been developed in Korea for transuranics (TRU) transmutation with an electricity output of 600 MW(electric) (called the KALIMER-600 TRU burner). The core design philosophy is primarily based on passive safety mechanisms to meet the Generation IV technology goals. Accordingly, metal fuel has been adopted to enhance its inherent passive safety characteristics. The charged fuel in a ternary metal alloy (U-TRU-Zr) consists of self-recycled TRU and TRU recovered from the spent nuclear fuels of current light water reactors through a pyro-metallurgical process, which is assumed to carry over 5% of the inventory of rare earth (RE) elements. It has been recognized that an additional amount of RE in the fuel would decrease the material attractiveness of the charged fuel with respect to proliferation resistance and physical protection (PR&PP). However, this may raise concerns because most of the reactor physics parameters will tend to negatively affect the passive safety features encoded in the original core concept. Thus, this study investigates the impact of the RE recovery fraction on the core physics performance and important safety parameters such as Doppler coefficients and sodium void reactivity. The results are expected to help provide guidance regarding the development of limiting conditions for RE contents to recycling technology flow sheet developers and ternary metal fuel developers, and to provide insight into optimizing the core passive safety characteristics under accident conditions should a significant amount of RE be needed to enhance PR&PP. [ABSTRACT FROM AUTHOR]

A core design study to convert a breakeven core into a transuranic (TRU) burner is performed for a 600-MW(electric)--rated metal-fueled sodium-cooled fast reactor. No change in the core and subassembly layouts is assumed, which only allows geometiy variations within the fuel rods. Investigated alternatives are to use variable cladding thicknesses (VCTs), smearing fraction (SF) adjustments, and annular fuel rod concepts with a central liner of a variable diameter consisting of void, Zr, B4C, Al, etc. The VCT concept could not be employed due to a too-high clad inner wall temperature. A SF adjustment below a typical fraction of 75% leads to moderate TRU burning and a reduced sodium void worth but also to a relatively high burnup swing. Placing a central nonfuel rod with the fuel arranged in an annular ring affects the core performance and reactivity coefficients, depending on whether it is a moderator or an absorber. In general, candidate materials of high atomic numbers contribute to large positive sodium void worths but also enhanced negative expansion effects. Among the light elements, vanadium reveals a favorable peiformance with comparable TRU burning and a reduced sodium void worth, suggesting this material can be regarded as a substitute for sodium in the solid state. [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]

Zrebar Lake is one of the largest freshwater lakes in Iran and it plays an important role in the ecosystem of the environment, while its desiccation has a negative impact on the surrounded ecosystem. Despite this, this lake provides an interesting recreation setting in terms of ecotourism. The prediction and forecasting of the water level of the lake through simple but practical methods can provide a reliable tool for future lake water resource management. In the present study, we predict the daily water level of Zrebar Lake in Iran through well-known decision tree-based algorithms, including the M5 pruned (M5P), random forest (RF), random tree (RT) and reduced error pruning tree (REPT). We used five different water input combinations to find the most effective one. For our modeling, we chose 70% of the dataset for training (from 2011 to 2015) and 30% for model evaluation (from 2015 to 2017). We evaluated the models' performances using different quantitative (root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), percent bias (PBIAS) and ratio of the root mean square error to the standard deviation of measured data (RSR)) and visual frameworks (Taylor diagram and box plot). Our results showed that water level with a one-day lag time had the highest effect on the result and, by increasing the lag time, its effect on the result was decreased. This result indicated that all the developed models had a good prediction capability, but the M5P model outperformed the others, followed by RF and RT equally and then REPT. Our results showed that these algorithms can predict water level accurately only with a one-day lag time in water level as an input and they are cost-effective tools for future predictions. [ABSTRACT FROM AUTHOR]

• DNN was verified and assessed for shallow landslide susceptibility mapping. • Three optimization algorithms, Adam, SGD, and RMSProp, were used. • Two loss functions, MSE and CE, were investigated. • DNN with Adam and MSE is the best and outperformed other models. • DNN-Adam-MSE is a new tool for shallow landslide susceptibility modeling. This research aims at investigating the capability of Keras's deep learning models with three robust optimization algorithms (stochastic gradient descent, root mean square propagation, and adaptive moment optimization) and two-loss functions for spatial modeling of landslide hazard at a regional scale. Shallow landslides at the Ha Long area (Vietnam) were selected as a case study. For this regard, set of ten influencing factors (slope, aspect, curvature, topographic wetness index, landuse, distance to road, distance to river, soil type, distance to fault, and lithology) and 193 landslide polygons were prepared to construct a Geographic Information System (GIS) database for the study area. Using the collected database, the DNN with its potential of realizing complex functional mapping hidden in the data is used to generalize a decision boundary that separates the learning space into two distinct categories: landslide (a positive class) and non-landslide (a negative class). Experimental results point out that the utilized the Keras's deep learning model with the Adam optimization and the mean squared error lost function is the best with the prediction performance of 84.0%. The performance is better than those of the employed benchmark approaches of random forest, J48 decision tree, classification tree, and logistic model tree. We conclude that the Keras's deep learning model is a new tool for shallow susceptibility mapping at landslide-prone areas. [ABSTRACT FROM AUTHOR]

Highlights • Assessment of detectors' locations and number of detectors in PGSFR. • Generation of spatial weighting function using forward fixed-source calculation in MCNPX. • Calculation of transient power during withdrawal of a single control assembly by using FREK. Abstract This paper presents the assessment of the ex-core detectors in the Prototype Gen-IV Sodium-cooled Fast Reactor (PGSFR) in response to the movement of single primary control assembly, to evaluate the detectors' locations and to determine a proper number of detectors. The detectors are located below the containment vessel and distributed in different ranges from the center and different azimuthal angles. Another position considered is in the radial direction of the core, at the sodium coolant gap between the outermost assembly and core shroud. To calculate the detection rate, the contribution of each fuel region to the detector and the core power distribution are required. Initially, the aforementioned contribution is generated by performing a continuous-energy forward fixed-source Monte Carlo calculation using MCNPX. The reactor kinetics code FREK simulates the power change due to an inadvertent withdrawal of a primary control assembly up to 10% from its critical position followed by full insertion of secondary control assemblies. The response from each detector location was evaluated, and consequently, the detectors located below the containment vessel, which can "see" all the fuel assemblies, responded better to the core power increase with a minor error of less than ∼0.6%. In addition, the impact of locating the detector below the containment vessel at different locations from the center or at different azimuthal angles was insignificant but several detectors should be distributed at even azimuthal angles for effective core monitoring and redundancy purposes. [ABSTRACT FROM AUTHOR]

In this paper, an investigation is conducted in order to understand impacts of Particle Swarm Optimization (PSO) parameters on the convergence and the quality of the inverse kinematics solutions provided by the IK-PSO (inverse kinematics solver using PSO) – a heuristic inverse kinematics solver algorithm. Over a large panel of parameters investigations, a statistical proof of convergence is provided for 5 links to 60 links articulated system. A recommended set of parameters intervals are presented for this class of IK problems. Investigations are based on the standard inertia weight PSO, and concerned the impact of the inertia weight, the swarm size and the maximum iteration number. For a given set of parameters, the existence of a solution with a given position error is also proved. All tests were conducted over 100 times. The density of probability function, PDF, is used to approximate and analyze the fineness functions, which are the square of the position error. Results showed IK-PSO is an interesting IK solver when a set of good parameters are used. For these parameters, the algorithm showed a statistical proof of convergence with a high resolution, by mean of error position. The algorithm also showed time-effectiveness compared to CCD method, which is assumed to be a real-time IK heuristic solver used in gaming. [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 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]

The objective of this research is to propose and confirm a new machine learning approach of Best-First tree (BFtree), AdaBoost (AB), MultiBoosting (MB), and Bagging (Bag) ensembles for potential groundwater mapping and assessing role of influencing factors. The Yasuj-Dena area (Iran) is selected as a case study. For this regard, a Yasuj-Dena database was established with 362 springs locations and 12 groundwater-influencing factors (slope, aspect, elevation, stream power index (SPI), length of slope (LS), topographic wetness index (TWI), topographic position index (TPI), land use, lithology, distance from fault, distance from river, and rainfall). The database was employed to train and validate the proposed groundwater models. The area under the curve (AUC) and statistical metrics were employed to check and confirm the quality of the models. The result shows that the BFTree-Bag model (AUC = 0.810, kappa = 0.495) has the highest prediction performance, followed by the BFTree-MB model (AUC = 0.785, kappa = 0.477), and the BFTree-MB model (AUC = 0.745, kappa = 0.422). Compared to the benchmark of Random Forests, the BFTree-Bag model performs better; therefore, we conclude that the BFtree-Bag is a new tool should be used for modeling of groundwater potential. [ABSTRACT FROM AUTHOR]

This paper proposes and implements an early warning and monitoring system for rainfall-induced landslide (named as EWMRIL) with a case study at the Nam Dan landslide (northern Vietnam). The proposed system consists of six sensor nodes and one rainfall station that are used to sense large amounts of data in real-time such as soil moisture, pore-water pressure (PWP), movement status, and rainfall. In addition, a new flexible configuration for the wireless communication system is proposed that is capable not only to save the energy consuming but also to ensure the reliability of the system. Using wireless communication system, the sensed data were sent to the computer station for analyzing and predicting the instability of the landslide in terms of factor of safety (FoS) using the finite element seepage analysis and the limit equilibrium slope stability analysis methods. These methods are available in the SEEP/W and SLOPE/W modules of the GeoStudio software. Based on the analyzing results, the system proposed three warning levels for the landslide Early, Intermediate, and Imminent. Experiment result in the rainy season from August to September 2016 has proven the validity of the EWMRILsystem. The result of this study is useful for landslide risk prevention and management in landslide prone-areas. [ABSTRACT FROM AUTHOR]

• Sensitivity/Uncertainty analysis using SERPENT 2 and ENDF/B-VIII.0. • Error of k eff of CERCER core was significant and larger than that of CERMET one. • ENDF/B-VIII.0 was found to be insensitive to kinetic parameter simulation results. The reliability of eigenvalue and kinetic parameter calculation results for CERCER and CERMET fueled ADS was investigated by conducting sensitivity and uncertainty analysis by SERPENT 2 and ENDF/B-VIII.0 library. The total error of k eff of CERCER core was found to be 1268.7 pcm and larger than that of CERMET one (609.4 pcm). It mostly comes from cross section uncertainty of 237Np (n,g) – the biggest contributor, 239Pu (n,f), 232Th (n,g) and 237Np (n,f). Therefore, the accuracy of nuclear data of those isotopes is recommended to be improved to achieve more reliable eigenvalue results. For kinetic parameters, the uncertainty of evaluated results mainly originated from the 237Np (n,g), 239Pu (n,inl) and 23Na (n,ela) cross sections. Nevertheless, its impact on simulation accuracy is not significant (less than 1%). Thus, ENDF/B-VIII.0 nuclear data is sufficient to evaluate β eff , l eff, and λ eff and discuss the safety features of ADS. [ABSTRACT FROM AUTHOR]

The present research examines the landslide susceptibility in Rudraprayag district of Uttarakhand, India using the conditional probability (CP) statistical technique, the boost regression tree (BRT) machine learning algorithm, and the CP-BRT ensemble approach to improve the accuracy of the BRT model. Using the four fold of data, the models' outcomes were cross-checked. The locations of existing landslides were detected by general field surveys and relevant records. 220 previous landslide locations were obtained, presented as an inventory map, and divided into four folds to calibrate and authenticate the models. For modelling the landslide susceptibility, twelve LCFs (landslide conditioning factors) were used. Two statistical methods, i.e. the mean absolute error (MAE) and the root mean square error (RMSE), one statistical test, i.e. the Freidman rank test, as well as the receiver operating characteristic (ROC), efficiency and precision were used for authenticating the produced landslide models. The results of the accuracy measures revealed that all models have good potential to recognize the landslide susceptibility in the Garhwal Himalayan region. Among these models, the ensemble model achieved a higher accuracy (precision: 0.829, efficiency: 0.833, AUC: 89.460, RMSE: 0.069 and MAE: 0.141) than the individual models. According to the outcome of the ensemble simulations, the BRT model's predictive accuracy was enhanced by integrating it with the statistical model (CP). The study showed that the areas of fallow land, plantation fields, and roadsides with elevations of more than 1500 m. with steep slopes of 24° to 87° and eroding hills are highly susceptible to landslides. The findings of this work could help in minimizing the landslides' risk in the Western Himalaya and its adjoining areas with similar landscapes and geological characteristics. Unlabelled Image • Considering the twelve landslide conditioning factors landslide susceptibility maps were prepared using CP, BRT and CP-BRT models. • After integration of BRT model with CP model the level of accuracy was increased. • Nearly 20% of the study areas have very high probability of landslide. [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]

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]