Flood susceptibility mapping of Cheongju, South Korea based on the integration of environmental factors using various machine learning approaches.

Floods are natural occurrences that pose serious risks to human life and the environment, including significant property and infrastructure damage and subsequent socioeconomic challenges. Recent floods in Cheongju County, South Korea have been linked to river overflow. In this study, we created flood susceptibility maps of Cheongju, South Korea using machine learning techniques including support vector regression (SVR), boosted tree (BOOST), and long short-term memory (LSTM) algorithms, based on environmental factors. Potentially influential variables were selected based on flood data gathered through field surveys; these included the slope, aspect, length-slope factor, wind exposition index, terrain wetness index, plan curvature, normalized difference water index, geology, soil drainage, soil depth, soil texture, land use type, and forest density. To improve the robustness of the flood susceptibility model, the most influential factors were identified using the frequency ratio method. Implementing machine learning techniques like SVR and BOOST produced encouraging outcomes, achieving the area under the curve (AUC) of 83.16% and 86.70% for training, and 81.65% and 86.43% for testing, respectively. While, the LSTM algorithm showed superior flood susceptibility mapping performance, with an AUC value of 87.01% for training and 86.91% for testing, demonstrating its robust performance and reliability in accurately assessing flood susceptibility. The results of this study enhance our understanding of flood susceptibility in South Korea and demonstrate the potential of the proposed approach for informing and guiding crucial regional policy decisions, contributing to a more resilient and prepared future.

Comparative study for coastal aquifer vulnerability assessment using deep learning and metaheuristic algorithms.

Coastal aquifer vulnerability assessment (CAVA) studies are essential for mitigating the effects of seawater intrusion (SWI) worldwide. In this research, the vulnerability of the coastal aquifer in the Lahijan region of northwest Iran was investigated. A vulnerability map (VM) was created applying hydrogeological parameters derived from the original GALDIT model (OGM). The significance of OGM parameters was assessed using the mean decrease accuracy (MDA) method, with the current state of SWI emerging as the most crucial factor for evaluating vulnerability. To optimize GALDIT weights, we introduced the biogeography-based optimization (BBO) and gray wolf optimization (GWO) techniques to obtain to hybrid OGM-BBO and OGM-GWO models, respectively. Despite considerable research focused on enhancing CAVA models, efforts to modify the weights and rates of OGM parameters by incorporating deep learning algorithms remain scarce. Hence, a convolutional neural network (CNN) algorithm was applied to produce the VM. The area under the receiver-operating characteristic curves for OGM-BBO, OGM-GWO, and VMCNN were 0.794, 0.835, and 0.982, respectively. According to the CNN-based VM, 41% of the aquifer displayed very high and high vulnerability to SWI, concentrated primarily along the coastline. Additionally, 32% of the aquifer exhibited very low and low vulnerability to SWI, predominantly in the southern and southwestern regions. The proposed model can be extended to evaluate the vulnerability of various coastal aquifers to SWI, thereby assisting land use planers and policymakers in identifying at-risk areas. Moreover, deep-learning-based approaches can help clarify the associations between aquifer vulnerability and contamination resulting from SWI.

Improving the prediction of wildfire susceptibility on Hawai'i Island, Hawai'i, using explainable hybrid machine learning models.

This study presents a comparative analysis of four Machine Learning (ML) models used to map wildfire susceptibility on Hawai'i Island, Hawai'i. Extreme Gradient Boosting (XGBoost) combined with three meta-heuristic algorithms - Whale Optimization (WOA), Black Widow Optimization (BWO), and Butterfly Optimization (BOA) - were employed to map areas susceptible to wildfire. To generate a wildfire inventory, 1408 wildfire points were identified within the study area from 2004 to 2022. The four ML models (XGBoost, WOA-XGBoost, BWO-XGBoost, and BOA-XGBoost) were run using 14 wildfire-conditioning factors categorized into four main groups: topographical, meteorological, vegetation, and anthropogenic. Six performance metrics - sensitivity, specificity, positive predictive values, negative predictive values, the Area Under the receiver operating characteristic Curve (AUC), and the average precision (AP) of Precision-Recall Curves (PRCs) - were used to compare the predictive performance of the ML models. The SHapley Additive exPlanations (SHAP) framework was also used to interpret the importance values of the 14 influential variables for the modeling of wildfire on Hawai'i Island using the four models. The results of the wildfire modeling indicated that all four models performed well, with the BWO-XGBoost model exhibiting a slightly higher prediction performance (AUC = 0.9269), followed by WOA-XGBoost (AUC = 0.9253), BOA-XGBoost (AUC = 0.9232), and XGBoost (AUC = 0.9164). SHAP analysis revealed that the distance from a road, annual temperature, and elevation were the most influential factors. The wildfire susceptibility maps generated in this study can be used by local authorities for wildfire management and fire suppression activity.

Spatial modeling of geogenic indoor radon distribution in Chungcheongnam-do, South Korea using enhanced machine learning algorithms.

Prolonged inhalation of indoor radon and its progenies lead to severe health problems for housing occupants; therefore, housing developments in radon-prone areas are of great concern to local municipalities. Areas with high potential for radon exposure must be identified to implement cost-effective radon mitigation plans successfully or to prevent the construction of unsafe buildings. In this study, an indoor radon potential map of Chungcheongnam-do, South Korea, was generated using a group method of data handling (GMDH) algorithm based on local soil properties, geogenic, geochemical, as well as topographic factors. To optimally tune the hyper-parameters of GMDH and enhance the prediction accuracy of modelling radon distribution, the GMDH model was integrated with two metaheuristic optimization algorithms, namely the bat (BA) and cuckoo optimization (COA) algorithms. The goodness-of-fit and predictive performance of the models was quantified using the area under the receiver operating characteristic (ROC) curve (AUC), mean squared error (MSE), root mean square error (RMSE), and standard deviation (StD). The results indicated that the GMDH-COA model outperformed the other models in the training (AUC = 0.852, MSE = 0.058, RMSE = 0.242, StD = 0.242) and testing (AUC = 0.844, MSE = 0.060, RMSE = 0.246, StD = 0.0242) phases. Additionally, using metaheuristic optimization algorithms improved the predictive ability of the GMDH. The GMDH-COA model showed that approximately 7 % of the total area of Chungcheongnam-do consists of very high radon-prone areas. The information gain ratio method was used to assess the predictive ability of considered factors. As expected, soil properties and local geology significantly affected the spatial distribution of radon potential levels. The radon potential map produced in this study represents the first stage of identifying areas where large proportions of residential buildings are expected to experience significant radon levels due to high concentrations of natural radioisotopes in rocks and derived soils beneath building foundations. The generated map assists local authorities to develop urban plans more wisely towards region with less radon concentrations.

Nanofibers in Respiratory Masks: An Alternative to Prevent Pathogen Transmission.

Recent global outbreak of COVID-19 has raised serious awareness about our abilities to protect ourselves from hazardous pathogens and volatile organic compounds. Evidence suggests that personal protection equipment such as respiratory masks can radically decrease rates of transmission and infections due to contagious pathogens. To increase filtration efficiency without compromising breathability, application of nanofibers in production of respiratory masks have been proposed. The emergence of nanofibers in the industry has since introduced a next generation of respiratory masks that promises improved filtration efficiency and breathability via nanometric pores and thin fiber thickness. In addition, the surface of nanofibers can be functionalized and enhanced to capture specific particles. In addition to conventional techniques such as melt-blown, respiratory masks by nanofibers have provided an opportunity to prevent pathogen transmission. As the surge in global demand for respiratory masks increases, herein, we reviewed recent advancement of nanofibers as an alternative technique to be used in respiratory mask production.

Convolutional neural network (CNN) with metaheuristic optimization algorithms for landslide susceptibility mapping in Icheon, South Korea.

Landslides are a geological hazard that can pose a serious threat to human health and the environment of highlands or mountain slopes. Landslide susceptibility mapping is an essential tool for predicting and mitigating landslides. This study aimed to investigate the application of deep learning algorithms based on convolutional neural networks (CNNs) with metaheuristic optimization algorithms, namely the grey wolf optimizer (GWO) and imperialist competitive algorithm (ICA), to landslide susceptibility mapping. The study area was Icheon City, South Korea, for which an accurate landslide inventory dataset was available. The landslide inventory map was prepared and randomly divided into datasets of 70% for training and 30% for validation. Additionally, 18 landslide-related factors, including geo-environmental and topo-hydrological factors, were considered as predictive variables. The models were compared using area under the curve (AUC) values in receiver operating characteristic (ROC) curve analysis. The validation results showed that optimized models based on CNN-GWO (AUC = 0.876, RMSE = 0.08) and CNN-ICA (AUC = 0.852, RMSE = 0.09) outperformed the standalone CNN model (AUC = 0.847, RMSE = 0.12). Nevertheless, the CNN model outperformed previous research that used a machine learning algorithm alone. Thus, the deep learning algorithm with optimization algorithms proposed in this study can generate more suitable models for landslide susceptibility mapping in the study area due to its improved accuracy.

Radon potential mapping in Jangsu-gun, South Korea using probabilistic and deep learning algorithms.

The adverse health effects associated with the inhalation and ingestion of naturally occurring radon gas produced during the uranium decay chain mean that there is a need to identify high-risk areas. This study detected radon-prone areas using a geographic information system (GIS)-based probabilistic and machine learning methods, including the frequency ratio (FR) model and a convolutional neural network (CNN). Ten influencing factors, namely elevation, slope, the topographic wetness index (TWI), valley depth, fault density, lithology, and the average soil copper (Cu), calcium oxide (Cao), ferric oxide (Fe2O3), and lead (Pb) concentrations, were analyzed. In total, 27 rock samples with high activity concentration index values were divided randomly into training and validation datasets (70:30 ratio) to train the models. Areas were categorized as very high, high, moderate, low, and very low radon areas. According to the models, approximately 40% of the study area was classified as very high or high risk. Finally, the radon potential maps were validated using the area under the receiver operating characteristic curve (AUC) analysis. This showed that the CNN algorithm was superior to the FR method; for the former, AUC values of 0.844 and 0.840 were obtained using the training and validation datasets, respectively. However, both algorithms had high predictive power. Slope, lithology, and TWI were the best predictors of radon-affected areas. These results provide new information regarding the spatial distribution of radon, and could inform the development of new residential areas. Radon screening is important to reduce public exposure to high levels of naturally occurring radiation.

Optimization modelling to establish false measures implemented with ex-situ plant species to control gully erosion in a monsoon-dominated region with novel in-situ measurements.

Water dominated gullies formation and associated land degradation are the foremost challenges among the planners for sustainability and optimization of land resources. This type of hazardous phenomenon is utmost vulnerable due to huge loss of surface soil in the sub-tropical developing countries like India. The present study has been carried out in rugged badland topography of Garhbeta-I Community Development (C.D.) Block in eastern India for assessing the gully erosion susceptibility (GES) mapping and optimization of land use planning. The GES mapping is the first and foremost steps towards minimization this adverse affect and attaining sustainable development. In this study we also describe the importance of plantation and alternation of ex-situ tree species with in-situ species for minimizes the erosional activity. To meet our research goal here we used two prediction based machine learning algorithm (MLA) namely random forest (RF) and boosted regression tree (BRT) and one optimization model of Ecogeography based optimization (EBO). The research study also carried out by using a total of 199, in which 139 (70%) and 60 (30%) gully head-cut points were used for training and validation purposes respectively and treated as dependent factors, and twenty gully erosion conditioning factors as independent variables. These models are validated through receiver operating characteristics-area under the curve (ROC-AUC), accuracy (ACC), precision (PRE) and Kappa coefficient index analysis. The validation result showed that EBO model with the highest values of AUC-0.954, ACC-0.85, PRE-0.877 and Kappa-0.646 is the most accurate model for GES followed by BRT and RF. The outcome results should help for the sustainable development of this rugged badland topography.

Credal decision tree based novel ensemble models for spatial assessment of gully erosion and sustainable management.

We introduce novel hybrid ensemble models in gully erosion susceptibility mapping (GESM) through a case study in the Bastam sedimentary plain of Northern Iran. Four new ensemble models including credal decision tree-bagging (CDT-BA), credal decision tree-dagging (CDT-DA), credal decision tree-rotation forest (CDT-RF), and credal decision tree-alternative decision tree (CDT-ADTree) are employed for mapping the gully erosion susceptibility (GES) with the help of 14 predictor factors and 293 gully locations. The relative significance of GECFs in modelling GES is assessed by random forest algorithm. Two cut-off-independent (area under success rate curve and area under predictor rate curve) and six cut-off-dependent metrics (accuracy, sensitivity, specificity, F-score, odd ratio and Cohen Kappa) were utilized based on both calibration as well as testing dataset. Drainage density, distance to road, rainfall and NDVI were found to be the most influencing predictor variables for GESM. The CDT-RF (AUSRC = 0.942, AUPRC = 0.945, accuracy = 0.869, specificity = 0.875, sensitivity = 0.864, RMSE = 0.488, F-score = 0.869 and Cohen's Kappa = 0.305) was found to be the most robust model which showcased outstanding predictive accuracy in mapping GES. Our study shows that the GESM can be utilized for conserving soil resources and for controlling future gully erosion.

Comparison of multi-criteria and artificial intelligence models for land-subsidence susceptibility zonation.

Land subsidence (LS) in arid and semi-arid areas, such as Iran, is a significant threat to sustainable land management. The purpose of this study is to predict the LS distribution by generating land subsidence susceptibility models (LSSMs) for the Shahroud plain in Iran using three different multi-criteria decision making (MCDM) and five different artificial intelligence (AI) models. The MCDM models we used are the VlseKriterijumska Optimizacija IKompromisno Resenje (VIKOR), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Complex Proportional Assessment (COPRAS), and the AI models are the extreme gradient boosting (XGBoost), Cubist, Elasticnet, Bayesian multivariate adaptive regression spline (BMARS) and conditional random forest (Cforest) methods. We used the Receiver Operating Characteristic (ROC) curve, Area Under Curve (AUC) and different statistical indices,i.e. accuracy, sensitivity, specificity, F score, Kappa, Mean Absolute Error (MAE) and Nash-Sutcliffe Criteria (NSC)to validate and evaluate the methods. Based on the different validation techniques, the Cforest method yielded the best results with minimum and maximum values of 0.04 and 0.99, respectively. According to the Cforest model, 30.55% of the study area is extremely vulnerable to land subsidence. The results of our research will be of great help to planners and policy makers in the identification of the most vulnerable regions and the implementation of appropriate development strategies in this area.

Quercetin Arrests in G2 phase, Upregulates INXS LncRNA and Downregulates UCA1 LncRNA in MCF-7 Cells.

One of the most prevalent malignancies, which have severe effects on women's health, is breast cancer. Quercetin, a flavonoid found in vegetables, tea, and fruits, is known to have bioactive properties, such as anti-inflammatory, anti-oxidant, as well as anti-cancer. Long non-coding RNAs (lncRNAs) have been recognized to function as primary regulators of diverse cellular processes, including differentiation, development, and cell fate. INXS and UCA1 are lncRNAs that are up regulated and down regulated respectively in cancer cells. This research aimed to assess the impact of quercetin on the expression of INXS and UCA1 genes in MCF-7 cells. Various quercetin concentrations at different times were used to treat MCF-7 cells. The cell viability and IC50 values were determined using MTT assay. Then, MCF-7 cells were incubated with various quercetin concentrations for 24, 48, and 72 h. Cell cycle analyses were evaluated by flow cytometry. The levels of INXS and UCA1 gene expression compared with the GAPDH gene at different concentrations of quercetin were quantified using real-time PCR method. Based on the results, quercetin exerted a dose- and time-dependent inhibitory impact on the viability of MCF-7 cells. Furthermore, quercetin induced cell cycle arrest at the G2 phase in MCF-7 cells. Also, quercetin induced INXS upregulation and UCA1 downregulation in the MCF-7 cell line. These data suggest that quercetin might increase cell death by up regulating INXS and down regulating UCA1 lncRNAs in MCF-7 cells.

Spatial prediction of landslide susceptibility using hybrid support vector regression (SVR) and the adaptive neuro-fuzzy inference system (ANFIS) with various metaheuristic algorithms.

Landslides are natural and sometimes quasi-natural hazards that are destructive to natural resources and cause loss of human life every year. Hence, preparing susceptibility maps for landslide monitoring is essential to minimizing their negative effects. The main aim of the current research was to develop landslide susceptibility maps for Icheon Township, South Korea, using hybrid Machin learning and metaheuristic algorithms, that is, the bee algorithm (Bee), the adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), and the grey wolf optimizer (GWO), and to compare their predictive accuracy. Based on identified landslide locations, an inventory map was prepared and divided into training and validation data sets (70%/30%). the predicated model outcomes were validated with root mean square error (RMSE), and area under receiver operating characteristic curve (AUC), and pairwise comparison values for the ANFIS, ANFIS-Bee, ANFIS-GWO, SVR, SVR-Bee, and SVR-GWO models were obtained. The area under the curve was obtained with the training and validation data sets. Based on the training data sets, AUC of 80%, 83%, 83%, 69%, 81%, and 80% were obtained for the SVR, SVR-GWO, SVR-Bee, ANFIS, ANFIS-GWO, and ANFIS-Bee models, respectively. For the validation data sets, values of 79%, 82%, 82%, 68%, 79%, and 79%, respectively, were obtained. The SVR-GWO and SVR-Bee models were the most predictive models in terms of constructing the exceptionally focused landslide susceptibility map, with little spatial variation in the highly susceptible classes. Furthermore, the MSE, RMSE, and pairwise comparisons indicated that the SVR-GWO and SVR-Bee models were superior models for this study township. In addition, ANFIS individually was not superior to the ensembles of ANFIS-GWO and ANFIS-Bee for landslide assessment. These landslide susceptibility maps provide a platform for land use planning with an eye toward sustainable development of infrastructure and damage reduction for Icheon Township.

Multi-hazard probability assessment and mapping in Iran.

Several areas of Iran are prone to numerous natural hazards. An effective multi-hazard risk reduction requires analysis of the individual hazards and their interplay. This research develops a multi-hazard probability map for three hazards (i.e. landslides, floods, and earthquakes) for the management of hazard-prone areas in Lorestan Province, Iran, using anew ensemble model named SWARA-ANFIS-GWO. First, based on flood and landslide occurrence maps, hazard-prone areas were identified and sub-divided into two subsets.70% of these locations were randomly chosen to be used for the construction of susceptibility maps, while the remaining 30% of the instances were used to assess the accuracy of the models. Then, eleven factors relating to terrain and land use were selected for the preparation of landslide and flood susceptibility maps. An earthquake map was prepared based on a probabilistic seismic hazard analysis (PSHA). The SWARA method was implemented for weighting contributing factors and evaluating spatial relationships between the three hazards and predisposing factors. Subsequently, the ANFIS approach was used to acquire weights for each value while using a gray Wolf metaheuristic algorithm. Finally, all weight values were further assessed using the MATLAB software. The predicated results from the models were validated with ROC (rate of change) curves. The resulting AUCs (area under the curve) of the validation data indicated accuracies of 84% and 80% for floods and landslides, respectively, and 87% and 82.6%for flood and landslides based on the training data, respectively. Finally, the flood, landslide, and earthquake maps were combined to create a multi-hazard probability map of the Lorestan Province. This multi-hazard map serves as a valuable tool for land use planning and sustainable infrastructure development for the Lorestan Province.

Experimental wound healing using microamperage electrical stimulation in rabbits.

We investigated the effects of microamperage electrical stimulation (MES) on the healing of skin incision in rabbits. Thirty male adult rabbits were randomly divided into sham-treated and experimental groups. Each group was divided into three subgroups, based on the duration of experiment (4, 7, and 15 days). A full-thickness incision was made on the skin of each rabbit. The experimental group received an MES of 200 microamperes current intensity for 2 h/day. Morphometrical and biomechanical evaluations were carried out. The mean number of fibroblasts at day 7 and the mean of tensile strength at day 15 were found to be significantly higher for the experimental group than for those in the sham-treated group (p < 0.01 and p < 0.05, respectively). Daily application of MES significantly accelerated the wound-healing process of full-thickness incision in the rabbits' skin.