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Abstract Mine operational safety is an important aspect of maintaining the operational continuity of a mining area. In this study, we used the InSAR time series to analyze land surface changes using the ICOPS (improved combined scatterers with optimized point scatters) method. This ICOPS method combines persistent scatterers (PS) with distributed scatterers (DS) to increase surface deformation analysis’s spatial coverage and quality. One of the improvements of this study is the use of machine learning in postprocessing, based on convolutional neural networks, to increase the reliability of results. This study used data from the Sentinel-1 SAR C-band satellite during the 2016–2022 observation period at the Musan mine, North Korea. In the InSAR surface deformation time analysis, the maximum average rate of land subsidence was approximately > 15.00 cm per year, with total surface deformation of 170 cm and 70 cm for the eastern dumping area and the western dumping area, respectively. Analyzing the mechanism of land surface changes also involved evaluating the geological conditions in the Musan mining area. Our research findings show that combining machine learning and statistical methods has great potential to enhance the understanding of mine surface deformation.

This paper introduces two novel class-specific fuzzy clustering algorithms: Mean-based Supervised Clustering (MSC) and Density-based Mean Supervised Clustering (DMSC). These algorithms are designed to construct the hidden layer of the Fuzzy Hypersphere Neural Network (FHNN) classifier, which is structured on the framework of the Radial Basis Function Neural Network (RBFNN). The FHNN classifier utilizes fuzzy sets as labeled pattern clusters in its hidden layer, with classes represented in the output layer formed by the aggregation of these fuzzy sets. An important characteristic of this classifier is its independence from tuning parameters. It meticulously determines centroids and radii for labeled clusters, consistently achieving 100% accuracy across any training set. The FHNN classifier effectively handles outliers and is robust to variations in data presentation, ensuring clear data visualization for users. During the creation of labeled clusters in the hidden layer, binary weight values are adjusted concurrently between the hidden and output layers. This study proposes the formation of fuzzy clusters with varying dimensions tailored to the dataset. The classifier architecture, rooted in the radial basis function neural network, achieves 100% training accuracy due to precise fuzzy cluster formation. Experimental comparisons with RBFNN and similar classifiers using sixteen benchmark datasets demonstrate the superiority of the proposed classifier in pattern recognition tasks.

ABSTRACTClimate change has contributed to the recent increase in wildfire occurrences, vegetation failures, human health risks, physical damage, and economic losses. Wildfire susceptibility mapping is an essential technique for assessing areas prone to wildfires. In this study, we proposed the combination of the damage proxy map (DPM) and differenced normalized burn ratio (dNBR) method to generate a precise wildfire inventory map and used it to predict areas susceptible to wildfire. The wildfire susceptibility maps were produced using frequency ratio (FR), convolutional neural network (CNN), and long short-term memory (LSTM)-based deep learning and their performances were compared. We implemented the proposed method on Maui Island, Hawaii, where wildfires frequently occur. We started the process by generating a wildfire inventory map from 2019 to 2023 based on the DPM method applied to Sentinel-1 synthetic aperture radar (SAR) data combined with a dNBR map retrieved from Sentinel-2 data. The wildfire inventory was randomly divided into a training dataset (70%) and a testing dataset (30%). Fifteen wildfire-related factors, including topographical, meteorological, land use, environmental, and anthropological factors, were selected to predict wildfires. The wildfire-related factors were selected by conducting study literature and considering spatial correlation analysis based on the FR method, information gain ratio analysis (IGR), and multicollinearity assessment using tolerance (TOL) and variance inflation factor (VIF) metrics. The level of susceptibility of an area to wildfire is divided into five, namely very high, high, moderate, low, and very low. The FR, CNN, and LSTM produced wildfire susceptibility maps with similar patterns, significantly influenced by land use and rainfall factors. The highly susceptible areas are located on gentle slopes covered by agricultural land and unhealthy vegetation, and these areas have low rainfall intensity but receive high levels of solar radiation. Meanwhile, areas with relatively low susceptibility occur in forests with high levels of wet canopy evaporation. The prediction results were evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), and the CNN performed slightly better than the FR and LSTM, with AUC values of 0.879, 0.877, and 0.870, respectively. Hence, the use of the CNN algorithm in the proposed method is appropriate, specifically for the study area. In addition, the susceptibility map provides information on susceptible areas on Maui Island, Hawaii, to increase public awareness.

Floods are potentially devastating natural hazards that can threaten human life and ecosystems. The serious impact caused by floods is the reason for the urgent need to provide an appropriate flood susceptibility map (FSM). Floods can be triggered by various meteorological events, such as storms in coastal areas. On 14 March 2019, Cyclone Idai produced massive and severe flooding inundation in the coastal city of Beira, Mozambique. The extensive flood damage in Beira necessitated providing an accurate FSM and analyzing the factors associated with flooding. This study integrated the data of synthetic aperture radar (SAR), geographic information system (GIS), and two deep learning models, namely, the group method of data handling (GMDH) and convolutional neural network (CNN), to generate a precise FSM. We compared the performance of CNN and GMDH models in generating the FSMs for a case study of the March 2019 flood in the Beira Area, Mozambique. The result showed that the models produced a similar pattern of FSM, where the areas near the river, having a lower slope and lower altitude, have a higher flood risk. The FSM generated by CNN and GMDH models were compared to find a better model for flood risk assessment. The prediction performance of CNN model (AUC = 0.90 and RMSE = 0.022) was better than the result of GMDH model (AUC = 0.87 and RMSE = 0.089). The study should be used to mitigate the flood disaster and reduce flood risk in the future.

Landslides are geological hazards that can have severe impacts, threatening both the people and the local environment of highlands or mountain slopes. Landslide susceptibility mapping is an essential tool for predicting landslides and mitigating landslide-associated damage in areas prone to these events. This study aims to investigate the combination of using an adaptive network-based fuzzy inference system (ANFIS) with metaheuristic optimization algorithms: gray wolf optimizer (GWO), particle swarm optimization algorithm (PSO), and the imperialist competitive algorithm (ICA) in mapping landslide potential. The study area was Pyeongchang-gun, South Korea, for which an accurate landslide inventory dataset is available. A landslide inventory map was organized, and the data were separated randomly into training data (70%) and validation data (30%). In addition, 16 landslide-related factors consisting of geo-environmental and topo-hydrological factors were considered as predictive variables. This landslide susceptibility model was be evaluated based on the value of the area under the receiver operating characteristic (ROC) curve (AUC) to measure its accuracy. Based on the maps, the validation results showed that the optimized models of ANFIS-ICA, ANFIS-PSO, and ANFIS-GWO had AUC accuracies of 0.927, 0.947, and 0.968, respectively. The result from the hybrid algorithms model of ANFIS with metaheuristic algorithms outperformed the standalone ANFIS model in terms of accuracy in predicting landslide potential. Therefore, the ML algorithm and optimization algorithm models proposed in this study are more suitable for landslide susceptibility mapping in the study area.

Road network extraction is a significant challenge in remote sensing (RS). Automated techniques for interpreting RS imagery offer a cost-effective solution for obtaining road network data quickly, surpassing traditional visual interpretation methods. However, the diverse characteristics of road networks, such as varying lengths, widths, materials, and geometries across different regions, pose a formidable obstacle for road extraction from RS imagery. The issue of road extraction can be defined as a task that involves capturing contextual and complex elements while also preserving boundary information and producing high-resolution road segmentation maps for RS data. The objective of the proposed Archimedes tuning process quantum dilated convolutional neural network for road Extraction (ATP-QDCNNRE) technology is to tackle the aforementioned issues by enhancing the efficacy of image segmentation outcomes that exploit remote sensing imagery, coupled with Archimedes optimization algorithm methods (AOA). The findings of this study demonstrate the enhanced road-extraction capabilities achieved by the ATP-QDCNNRE method when used with remote sensing imagery. The ATP-QDCNNRE method employs DL and a hyperparameter tuning process to generate high-resolution road segmentation maps. The basis of this approach lies in the QDCNN model, which incorporates quantum computing (QC) concepts and dilated convolutions to enhance the network’s ability to capture both local and global contextual information. Dilated convolutions also enhance the receptive field while maintaining spatial resolution, allowing fine road features to be extracted. ATP-based hyperparameter modifications improve QDCNNRE road extraction. To evaluate the effectiveness of the ATP-QDCNNRE system, benchmark databases are used to assess its simulation results. The experimental results show that ATP-QDCNNRE performed with an intersection over union (IoU) of 75.28%, mean intersection over union (MIoU) of 95.19%, F1 of 90.85%, precision of 87.54%, and recall of 94.41% in the Massachusetts road dataset. These findings demonstrate the superior efficiency of this technique compared to more recent methods.

Recent developments in remote sensing research have resulted in a large amount of variability in the data provided by researchers. Synthetic aperture radar (SAR) is a tool used to measure surface deformation and assess changes in the Earth’s surface. Here, we consider the usefulness of Interferometric Synthetic Aperture Radar (InSAR) in assessing past volcanic activity as a key to learning the characteristics of the deformation around a volcano. The Hantangang River volcanic field (HRVF) is a geoheritage site in the Korean Peninsula that has interesting geological characteristics. This volcanic field has formed along 110 km of the paleochannel of the Hantangang River. Since the eruptions occurred from 0.15 to 0.51 Ma, the source is limited, which has raised interest in the assessment of volcanic landforms. The recent integration of machine learning and InSAR processing has shown promising results for many purposes, such as classifying, modeling, and detecting surface deformation. To examine the future impact based on information from the past, we utilized a synthetic interferogram with the Okada model and transferred it to a machine learning algorithm. The synthetic interferogram was formed based on Sentinel-1 C-band satellite data to simulate the deformation phases. The orbital errors, the topographical data errors, and the atmospheric effect were also simulated and added to the synthetic interferogram to enrich the learning input. A convolutional neural network (CNN) trained with the unwrapped simulated interferogram data and its performance was evaluated. Our proposed method exhibits the capability to detect volcanic activity’s deformation patterns with synthetic interferogram data. The results show that an overall accuracy of more than 80% was achieved using the CNN algorithms on the validation dataset. This study is the first to use machine learning approaches for detecting prehistorical volcanic deformation and demonstrates potential techniques for developing an approach based on satellite imagery. In addition, this study has introduced the possibility of developing a rapid detection of surface deformation using InSAR data based on a machine learning approach.

Vehicular traffic plays a significant role in terms of economic development; however, it is also a major source of noise pollution. Therefore, it is highly imperative to model traffic noise, especially for expressways due to their high traffic volume and speed, which produce very-high level of traffic noise. Previous traffic prediction models are mostly based on the regression approach and the artificial neural network (ANN), which often fail to describe the trends of noise. In this paper, a deep neural network-based optimization approach is implemented in two ways: i) using different algorithms for training and activation, and ii) integrating with feature selection methods such as correlation-based feature selection (CFS) and wrapper for feature-subset selection (WFS) methods. These methods are integrated to produce traffic noise maps for different time of the day on weekdays, including morning, afternoon, evening, and night. The novelty of this study is the integration of the feature selection method with the deep neural network for vehicular traffic noise modelling. New Klang Valley Expressway (NKVE) in Malaysia was used as a case study due to its increasing heavy and light vehicles, and the motorbike during peak hours, which result in high traffic noise. The results from the models indicate that the WFS-DNN model has the least mean-absolute-deviation (MAD) of 2.28, and the least root-mean-square-error (RMSE) of 3.97. Also, this model shows the best result compared to the other models such as DNN without integration with feature selection methods, CFS-DNN and the ANN networks (MLP and RBF). MAD improvement of 27% - 47% and RMSE improvement of 25% - 38% was achieved compared to other methods. The study provides a generic approach to key parameter selection and dimension reduction with novel trend descriptor which could be useful for future such modelling applications.

Mapping lithological units of an area using remote sensing data can be broadly grouped into pixel-based (PBIA), sub-pixel based (SPBIA) and object-based (GEOBIA) image analysis approaches. Since it is not only the datasets adequacy but also the correct classification selection that influences the lithological mapping. This research is intended to analyze and evaluate the efficiency of these three approaches for lithological mapping in semi-arid areas, by using Sentinel-2A data and many algorithms for image enhancement and spectral analysis, in particular two specialized Band Ratio (BR) and the Independent component analysis (ICA), for that reason the Paleozoic Massif of Skhour Rehamna, situated in the western Moroccan Meseta was chosen. In this study, the support vector machine (SVM) that is theoretically more efficient machine learning algorithm (MLA) in geological mapping is used in PBIA and GEOBIA approaches. The evaluation and comparison of the performance of these different methods showed that SVM-GEOBIA approach gives the highest overall classification accuracy (OA $\approx ~93$ %) and kappa coefficient (K) of 0, 89, while SPBIA classification showed OA of approximately 89% and kappa coefficient of 0, 84, whereas the lithological maps resulted from SVM-PBIA method exhibit salt and pepper noise, with a lower OA of 87% and kappa coefficient of 0, 80 comparing them with the other classification approaches. From the results of this comparative study, we can conclude that the SVM-GEOBIA classification approach is the most suitable technique for lithological mapping in semi-arid regions, where outcrops are often inaccessible, which complicates classic cartographic work.

Seagrass is an essential component of coastal ecosystems because of its capability to absorb blue carbon, and its involvement in sustaining marine biodiversity. In this study, support vector machine (SVM) technologies with corrected satellite imagery data, were applied to identify the distribution of seagrasses. Observations of seagrasses from satellite imagery were obtained using GeoEye-1, Sentinel-2 MSI level 1C, and Landsat-8 OLI satellite imagery. The satellite imagery from Google Earth has been obtained at a very high resolution, and was to be used within both the training and testing of a classification method. The optical satellite imagery must be processed for image classification, throughout which radiometric correction, sunglint, and water column adjustments were applied. We restricted the scope of the study area to a maximum depth of 10 m due to the fact that light does not penetrate beyond this level. When classifying the distribution of seagrasses present in the research region, the recently developed SVM technique achieved overall accuracy values of up to 92% (GeoEye-1), 88% (Sentinel-2 MSI level 1C), and 83% (Landsat-8 OLI), respectively. The results of the overall accuracy values are also used to evaluate classification models.

Australia has suffered devastating wildfires recently, and is predisposed to them due to several factors, including topography, meteorology, vegetation, and ignition sources. This study utilized a geographic information system (GIS) technique to analyze and understand the factors that regulate the spatial distribution of wildfire incidents and machine learning to predict wildfire susceptibility in Sydney. Wildfire inventory data were constructed by combining the fire perimeter through field surveys and fire occurrence data gathered from the visible infrared imaging radiometer suite (VIIRS)-Suomi thermal anomalies product between 2011 and 2020 for the Sydney area. Sixteen wildfire-related factors were acquired to assess the potential of machine learning based on support vector regression (SVR) and various metaheuristic approaches (GWO and PSO) for wildfire susceptibility mapping in Sydney. In addition, the 2019–2020 “Black Summer” fire acted as a validation dataset to assess the predictive capability of the developed model. Furthermore, the information gain ratio (IGR) method showed that driving factors such as land use, forest type, and slope degree have a large impact on wildfire susceptibility in the study area, and the frequency ratio (FR) method represented how the factors influence wildfire occurrence. Model evaluation based on area under the curve (AUC) and root average square error (RMSE) were used, and the outputs showed that the hybrid-based SVR-PSO (AUC = 0.882, RMSE = 0.006) model performed better than the standalone SVR (AUC = 0.837, RMSE = 0.097) and SVR-GWO (AUC = 0.873, RMSE = 0.080) models. Thus, optimizing SVR with metaheuristics improved the accuracy of wildfire susceptibility modeling in the study area. The proposed framework can be an alternative to the modeling approach and can be adapted for any research related to the susceptibility of different disturbances.

This study aims to elucidate the behavior of diffusible hydrogen and delayed fracture in martensitic steel with 1500 MPa strength during automotive painting process and under vehicle service conditions. A sequential process of automotive pretreatment line and vehicle service environment is simulated to evaluate the hydrogen pick up in each process. In case of the automotive painting line, the absorption of hydrogen is within the common range in the process of phosphating treatment and electrodeposition. The baking process plays an effective role for desorbing the diffusible hydrogen absorbed during the automotive pre-treatment such as zinc-phosphating, and electrodeposition process. In case of the corrosion environment under the automotive driving conditions, hydrogen induced delayed fracture is accelerated as the exposure time increases. Further, it is clarified that severe plastic deformation are the significant factors for hydrogen induced delayed fracture under with low pH value and present of chloride ion in a chemical solution parameter. In summary, hydrogen is transported constantly during electrodeposition sequential line process of automobile manufacturing below the hydrogen content of 0.5 ppm, which is not critical value for leading to hydrogen delayed fracture based on results of slow strain rate tensile tests. However, exposure to extreme conditions under service environment of vehicle, such as acidic solution and chloride chemistry solution that result in high level of hydrogen absorption, severe plastic deformation in the sheared edge, and constantly applied internal or external stresses, can cause the hydrogen induced delayed fracture in the fully martensitic steels.

Study region: The study area was the Anseong-si area that located in the southernmost part of Gyeonggi-do Province at 127°19′ E, 36°82′ N. Anseong has a transitional climate between that the north and the south regions. Its climate is characterized by the geographical conditions of forming expansive plains that stretch from the Charyeong Range. The entire area of the city is surrounded by many high and low mountains to the south and the west, late-middle age and old age hills are spread, while there are many plains due to the development of rivers. Study focus: In this study, machine learning algorithms were used based on convolutional neural network (CNN) and long short-term memory (LSTM) to generate a groundwater potential map in Anseong, South Korea. A total of 295 wells locations were divided by the median value of transmissivity data (T) and produced “1” point with high groundwater productivity data and “0” as the point with the low groundwater productivity and divided into 70:30 for training and validation the model. 14 groundwater potential related factors were used in this study such as topo-hydrological and geo-environmental factors to define the spatial correlation with the high groundwater productivity data. The validation of the model is evaluated using the receiver operating characteristics (ROC) curve analysis method. The area under the ROC curve (AUC) is calculated to test the model. New hydrological insight for the region: The results shows a good accuracy, as the AUC values were all higher than 0.8. Finally, the groundwater potential maps generated by CNN and LSTM can be used to analyze the areas that potentially provided groundwater in Anseong, South Korea. This study could help the local environment to manage groundwater resources and can be used to assist development planners and decision-makers in groundwater sustainability planning.

Pine wilt disease (PWD) has recently caused substantial pine tree losses in Republic of Korea. PWD is considered a severe problem due to the importance of pine trees to Korean people, so this problem must be handled appropriately. Previously, we examined the history of PWD and found that it had already spread to some regions of Republic of Korea; these became our study area. Early detection of PWD is required. We used drone remote sensing techniques to detect trees with similar symptoms to trees infected with PWD. Drone remote sensing was employed because it yields high-quality images and can easily reach the locations of pine trees. To differentiate healthy pine trees from those with PWD, we produced a land cover (LC) map from drone images collected from the villages of Anbi and Wonchang by classifying them using two classifier methods, i.e., artificial neural network (ANN) and support vector machine (SVM). Furthermore, compared the accuracy of two types of Global Positioning System (GPS) data, collected using drone and hand-held devices, for identifying the locations of trees with PWD. We then divided the drone images into six LC classes for each study area and found that the SVM was more accurate than the ANN at classifying trees with PWD. In Anbi, the SVM had an overall accuracy of 94.13%, which is 6.7% higher than the overall accuracy of the ANN, which was 87.43%. We obtained similar results in Wonchang, for which the accuracy of the SVM and ANN was 86.59% and 79.33%, respectively. In terms of the GPS data, we used two type of hand-held GPS device. GPS device 1 is corrected by referring to the benchmarks sited on both locations, while the GPS device 2 is uncorrected device which used the default setting of the GPS only. The data collected from hand-held GPS device 1 was better than those collected using hand-held GPS device 2 in Wonchang. However, in Anbi, we obtained better results from GPS device 2 than from GPS device 1. In Anbi, the error in the data from GPS device 1 was 7.08 m, while that of the GPS device 2 data was 0.14 m. In conclusion, both classifiers can distinguish between healthy trees and those with PWD based on LC data. LC data can also be used for other types of classification. There were some differences between the hand-held and drone GPS datasets from both areas.

Plumas National Forest, located in the Butte and Plumas counties, has experienced devastating wildfires in recent years, resulting in substantial economic losses and threatening the safety of people. Mapping damaged areas and assessing wildfire susceptibility are necessary to prevent, mitigate, and manage wildfires. In this study, a wildfire susceptibility map was generated using a CNN and metaheuristic optimization algorithms (GWO and ICA) based on images of areas damaged by wildfires. The locations of damaged areas were identified using the damage proxy map (DPM) technique from Sentinel-1 synthetic aperture radar (SAR) data collected from 2016 to 2020. The DPMs’ depicting areas damaged by wildfires were similar to fire perimeters obtained from the California Department of Forestry and Fire Protection (CAL FIRE). Data regarding damaged areas were divided into a training set (50%) for modeling and a testing set (50%) for assessing the accuracy of the models. Sixteen conditioning factors, categorized as topographical, meteorological, environmental, and anthropological factors, were selected to construct the models. The wildfire susceptibility models were evaluated using the area under the receiver operating characteristic (ROC) curve (AUC) and root mean square error (RMSE) analysis. The evaluation results revealed that the hybrid-based CNN-GWO model (AUC = 0.974, RMSE = 0.334) exhibited better performance than the CNN (AUC = 0.934, RMSE = 0.780) and CNN-ICA (AUC = 0.950, RMSE = 0.350) models. Therefore, we conclude that optimizing a CNN with metaheuristics considerably increased the accuracy and reliability of wildfire susceptibility mapping in the study area.

Study region: Sixteen different sites from two provinces (Lorestan and Illam) in the western part of Iran were considered for the field data measurement of cumulative infiltration, infiltration rate, and other effective variables that affect infiltration process. Study focus: Soil infiltration is recognized as a fundamental process of the hydrologic cycle affecting surface runoff, soil erosion, and groundwater recharge. Hence, accurate prediction of the infiltration process is one of the most important tasks in hydrological science. As direct measurement is difficult and costly, and empirical models are inaccurate, the current study proposed a standalone, and optimized deep learning algorithm of a convolutional neural network (CNN) using gray wolf optimization (GWO), a genetic algorithm (GA), and an independent component analysis (ICA) for cumulative infiltration and infiltration rate prediction. First, 154 raw datasets were collected including the time of measuring; sand, clay, and silt percent; bulk density; soil moisture percent; infiltration rate; and cumulative infiltration using field survey. Next, 70 % of the dataset were used for model building and the remaining 30 % was used for model validation. Then, based on the correlation coefficient between input variables and outputs, different input combinations were constructed. Finally, the prediction power of each developed algorithm was evaluated using different visually-based (scatter plot, box plot and Taylor diagram) and quantitatively-based [root mean square error (RMSE), mean absolute error (MAE), the Nash-Sutcliffe efficiency (NSE), and percentage of bias (PBIAS)] metrics. New Hydrological Insights for the Region: Finding revealed that the time of measurement is more important for cumulative infiltration, while soil characteristics (i.e. silt content) are more significant in infiltration rate prediction. This shows that in the study area, silt parameter, which is the dominant constituent parameter, can control infiltration process more effectively. Effectiveness of the variables in the present study, in the order of importance are time, silt, clay, moisture content, sand, and bulk density. This can be related to the fact that most of study area is rangeland and thus, overgrazing leads to compaction of the silt soil that can lead to a slow infiltration process. Soil moisture content and bulk density are not highly effective in our study because these two factors do not significantly change across the study area. Findings demonstrated that the optimum input variable combination, is the one in which all input variables are considered. The results illustrated that CNN algorithms have a very high performance, while a metaheuristic algorithm enhanced the performance of a standalone CNN algorithm (from 7% to 28 %). The results also showed that a CNN-GWO algorithm outperformed the other algorithms, followed by CNN-ICA, CNN-GA, and CNN for both cumulative infiltration and infiltration rate prediction. All developed algorithms underestimated cumulative infiltration, while overestimating infiltration rates.

The volcanic landforms associated with fluvial topography in the Hantangang River Volcanic Field (HRVF) have geoheritage value. The Hantangang basalt geological landform stretches along 110 km of the paleoriver channel of the Hantangang River. Since the eruption that formed this basalt occurred from 0.15 to 0.51 Ma, estimating the eruption in the HRVF that originated from two source vents in North Korea (Orisan Mountain and the 680 m peak) is challenging due to the limited recorded data for this eruption. In this study, we estimated this prehistorical eruption using 3D printing of a terrain model and Q-LavHA simulation. The results from the experiment were further analyzed using findings from an artificial neural network (ANN) and support vector machine (SVM) to classify the experimental lava area. The SVM classification results showed higher accuracy and efficiency in the computational process than the ANN algorithm. Results from the single eruptive vent scenario showed that the experiment had a higher accuracy than the Q-LavHA simulation. Further analysis of multiple vent scenarios in the Q-LavHA simulation has improved the accuracy compared with the single eruptive vent scenarios.

The main purpose of this study was to produce landslide susceptibility maps using evidential belief function (EBF), logistic regression (LR) and support vector machine (SVM) models and to compare their results for the region surrounding Yongin, South Korea. We compiled a landslide inventory map of 82 landslides based on reports and aerial photographs and confirmed these data through extensive field surveys. All landslides were randomly separated into two data sets of 41 landslide data points each; half were selected to establish the model, and the remaining half were used for validation. We divided 18 landslide conditioning factors into the following four categories: topography factors, hydrology factors, soil map and forest map; these were considered for landslide susceptibility mapping. The relationships between landslide occurrence and landslide conditioning factors were analyzed using the EBF, LR and SVM models. The three models were then validated using the area under the curve (AUC) method. According to the validation results, the prediction accuracy of the LR model (AUC = 94.59%) was higher than those of the EBF model (AUC = 92.25%) and the SVM model (AUC = 81.78%); the LR model also had the highest training accuracy.

The drinking and irrigation water scarcity is a major global issue, particularly in arid and semi-arid zones. In rural areas, groundwater could be used as an alternative and additional water supply source in order to reduce human suffering in terms of water scarcity. In this context, the purpose of the present study is to facilitate groundwater potentiality mapping via spatial-modelling techniques, individual and ensemble machine-learning models. Random forest (RF), logistic regression (LR), decision tree (DT) and artificial neural networks (ANNs) are the main algorithms used in this study. The preparation of groundwater potentiality maps was assembled into 11 ensembles of models. Overall, about 374 groundwater springs was identified and inventoried in the mountain area. The spring inventory data was randomly divided into training (75%) and testing (25%) datasets. Twenty-four groundwater influencing factors (GIFs) were selected based on a multicollinearity test and the information gain calculation. The results of the groundwater potentiality mapping were validated using statistical measures and the receiver operating characteristic curve (ROC) method. Finally, a ranking of the 15 models was achieved with the prioritization rank method using the compound factor (CF) method. The ensembles of models are the most stable and suitable for groundwater potentiality mapping in mountainous aquifers compared to individual models based on success and prediction rate. The most efficient model using the area under the curve validation method is the RF-LR-DT-ANN ensemble of models. Moreover, the results of the prioritization rank indicate that the best models are the RF-DT and RF-LR-DT ensembles of models.

Earthquake activities in and around the Korean Peninsula are relatively low in number and intensity compared with neighboring countries such as Japan and China. However, recent seismic activity caused great alarm and concern among citizens and government authorities, and uncovered the level of preparedness toward earthquake disasters. A survey has been conducted on 1256 participants to investigate the seismic literacy of Korean citizens, including seismic knowledge, awareness and management using a questionnaire of citizen earthquake literacy (CEL). The results declared that the citizens had low awareness and literacy, which means that they are not properly prepared for earthquake hazards. To develop an earthquake risk reduction plan and program efficiently and effectively, not only must it appropriately characterize the target audience, but also indicate high potential earthquake zones and potential earthquake damage. Therefore, this study mapped and analyzed the seismic vulnerability in southeast Korea using LogitBoost, logistic model tree (LMT), and logistic regression (LR) machine learning algorithms based on a building damage inventory map. The damaged buildings’ locations were generated after the 2017 Pohang earthquake using the damage proxy map (DPM) method from the Sentinel-1 synthetic aperture radar (SAR) data. DPMs detected coherence loss, which indicates damaged buildings in urban areas in the Pohang earthquake and shows a good correlation with the Korea Meteorological Administration (KMA) report with modified Mercalli intensity (MMI) scale values of more than VII (seven). The damage locations were randomly divided into two datasets: 50% for training the vulnerability models and 50% for validating the models in terms of accuracy and reliability. Fifteen seismic-related factors were used to construct a model of each algorithm. Model validation based on the area under the receiver operating curve (AUC) was used to determine model accuracy. The AUC values of seismic vulnerability maps using the LogitBoost, LMT, and LR algorithms were 0.769, 0.851, and 0.749, respectively. We suggest that earthquake preparedness efforts should focus on reconstruction, retrofitting, renovation, and seismic education in areas with high seismic vulnerability in South Korea. The results of this study are expected to be beneficial for engineers and policymakers aiming at developing disaster risk reduction plans, policies, and programs due to future seismic activity in South Korea.

The availability of groundwater is of concern. The demand for groundwater in Korea increased by more than 100% during the period 1994–2014. This problem will increase with population growth. Thus, a reliable groundwater analysis model for regional scale studies is needed. This study used the geographical information system (GIS) data and machine learning to map groundwater potential in Gangneung-si, South Korea. A spatial correlation performed using the frequency ratio was applied to determine the relationships between groundwater productivity (transmissivity data from 285 wells) and various factors. This study used four topography factors, four hydrological factors, and three geological factors, along with the normalized difference wetness index and land use and soil type. Support vector regression (SVR) and metaheuristic optimization algorithms—namely, grey wolf optimization (GWO), and particle swarm optimization (PSO), were used in the construction of the groundwater potential map. Model validation based on the area under the receiver operating curve (AUC) was used to determine model accuracy. The AUC values of groundwater potential maps made using the SVR, SVR_GWO, and SVR_PSO algorithms were 0.803, 0.878, and 0.814, respectively. Thus, the application of optimization algorithms increased model accuracy compared to the standard SVR algorithm. The findings of this study improve our understanding of groundwater potential in a given area and could be useful for policymakers aiming to manage water resources in the future.

The Vector Assignment Ordered Median Problem (VAOMP) is a new unified approach for location-allocation problems, which are one of the most important forms of applied analysis in GIS (Geospatial Information System). Solving location-allocation problems with exact methods is difficult and time-consuming, especially when the number of objectives and criteria increases. One of the most important criteria in location-allocation problems is the capacity of facilities. Firstly, this study develops a new VAOMP approach by including capacity as a criterion, resulting in a new model known as VAOCMP (Vector Assignment Ordered Capacitated Median Problem). Then secondly, the results of applying VAOMP, in scenario 1, and VAOCMP, in scenario 2, for the location-allocation of fire stations in Tehran, with the objective of minimizing the arrival time of fire engines to an incident site to no more than 5 min, are examined using both the Tabu Search and Simulated Annealing algorithms in GIS. The results of scenario 1 show that 52,840 demands were unable to be served with 10 existing stations. In scenario 2, given that each facility could not accept demand above its capacity, the number of demands without service increased to 59,080, revealing that the number of stations in the study area is insufficient. Adding 35 candidate stations and performing relocation-reallocation revealed that at least three other stations are needed for optimal service. Thirdly, and finally, the VAOMP and VAOCMP were implemented in a modest size problem. The implementation results for both algorithms showed that the Tabu Search algorithm performed more effectively.

Areas at risk of land subsidence in Jakarta can be identified using a land subsidence susceptibility map. This study evaluates the quality of a susceptibility map made using functional (logistic regression and multilayer perceptron) and meta-ensemble (AdaBoost and LogitBoost) machine learning algorithms based on a land subsidence inventory map generated using the Sentinel-1 synthetic aperture radar (SAR) dataset from 2017 to 2020. The land subsidence locations were assessed using the time-series interferometry synthetic aperture radar (InSAR) method based on the Stanford Method for Persistent Scatterers (StaMPS) algorithm. The mean vertical deformation maps from ascending and descending tracks were compared and showed a good correlation between displacement patterns. Persistent scatterer points with mean vertical deformation value were randomly divided into two datasets: 50% for training the susceptibility model and 50% for validating the model in terms of accuracy and reliability. Additionally, 14 land subsidence conditioning factors correlated with subsidence occurrence were used to generate land subsidence susceptibility maps from the four algorithms. The receiver operating characteristic (ROC) curve analysis showed that the AdaBoost algorithm has higher subsidence susceptibility prediction accuracy (81.1%) than the multilayer perceptron (80%), logistic regression (79.4%), and LogitBoost (79.1%) algorithms. The land subsidence susceptibility map can be used to mitigate disasters caused by land subsidence in Jakarta, and our method can be applied to other study areas.

The aims of this research were to map and analyze the risk of land subsidence in the Seoul Metropolitan Area, South Korea using satellite interferometric synthetic aperture radar (InSAR) time-series data, and three ensemble machine-learning models, Bagging, LogitBoost, and Multiclass Classifier. Of the types of infrastructure present in the Seoul Metropolitan Area, subway lines may be vulnerable to land subsidence. In this study, we analyzed Persistent Scatterer InSAR time-series data using the Stanford Method for Persistent Scatterers (StaMPS) algorithm to generate a deformation time-series map. Subsidence occurred at four locations, with a deformation rate that ranged from 6–12 mm/year. Subsidence inventory maps were prepared using deformation time-series data from Sentinel-1. Additionally, 10 potential subsidence-related factors were selected and subjected to Geographic Information System analysis. The relationship between each factor and subsidence occurrence was analyzed by using the frequency ratio. Land subsidence susceptibility maps were generated using Bagging, Multiclass Classifier, and LogitBoost models, and map validation was carried out using the area under the curve (AUC) method. Of the three models, Bagging produced the largest AUC (0.883), with LogitBoost and Multiclass Classifier producing AUCs of 0.871 and 0.856, respectively.

Rockfall is one of the most hazardous phenomena in mountainous and hilly regions with high and steep terrain. Such incidents can cause massive damage to people, properties, and infrastructure. Therefore, proper rockfall hazard assessment methods are required to save lives and provide a guide for the development of an area. The aim of this research is to develop a method for rockfall hazard assessment at two different scales (regional and local). A high-resolution airborne laser scanning (ALS) technique was utilized to derive an accurate digital terrain model (DTM); next, a terrestrial laser scanner (TLS) was used to capture the topography of the two most critical areas within the study area. A staking machine-learning model based on different classifiers, namely logistic regression (LR), random forest (RF), artificial neural network (ANN), support vector machine (SVM), and k-nearest neighbor (KNN), was optimized and employed to determine rockfall probability by utilizing various rockfall conditioning factors. A developed 3D rockfall kinematic model was used to obtain rockfall trajectories, velocity, frequency, bouncing height, kinetic energy, and impact location. Next, a spatial model combined with a fuzzy analytical hierarchy process (fuzzy-AHP) integrated in the Geographic Information System (GIS) was developed to assess rockfall hazard in two different areas in Ipoh, Malaysia. Additionally, mitigation processes were suggested and assessed to provide a comprehensive information for urban planning management. The results show that, the stacking random forest–k-nearest neighbor (RF-KNN) model is the best hybrid model compared to other tested models with an accuracy of 89%, 86%, and 87% based on training, validation, and cross-validation datasets, respectively. The three-dimension rockfall kinematic model was calibrated with an accuracy of 93% and 95% for the two study areas and subsequently the rockfall trajectories and their characteristics were derived. The assessment of the suggested mitigation processes proves that the proposed methods can reduce or eliminate rockfall hazard in these areas. According to the results, the proposed method can be generalized and applied in other geographical places to provide decision-makers with a comprehensive rockfall hazard assessment.

On November 8, 2018, a devastating wildfire, known as the Camp Fire wildfire, was reported in Butte County, California, USA. Approximately 88 fatalities ensued, and 18,804 structures were damaged by the wildfire. As a response to this destructive wildfire, this study generated a pre- and post-wildfire maps to provide basic data for evacuation and mitigation planning. This study used Landsat-8 and Sentinel-2 imagery to map the pre- and post-wildfire conditions. A support vector machine (SVM) optimized by the imperialist competitive algorithm (ICA) hybrid model was compared with the non-optimized SVM algorithm for classification of the pre- and post-wildfire map. The SVM−ICA produced a better accuracy (overall accuracies of 83.8% and 83.6% for pre- and post-wildfire using Landsat-8 respectively; 90.8% and 91.8% for pre- and post-wildfire using Sentinel-2 respectively), compared to SVM without optimization (overall accuracies of 80.0% and 78.9% for pre- and post-wildfire using Landsat-8 respectively; 83.3% and 84.8% for pre- and post-wildfire using Sentinel-2 respectively. In total, eight pre- and post-wildfire burned area maps were generated; these can be used to assess the area affected by the Camp Fire wildfire as well as for wildfire mitigation planning in the future.

Imlili Sebkha is a stable and flat depression in southern Morocco that is more than 10 km long and almost 3 km wide. This region is mainly sandy, but its northern part holds permanent water pockets that contain fauna and flora despite their hypersaline water. Google Earth Engine (GEE) has revolutionized land monitoring analysis by allowing the use of satellite imagery and other datasets via cloud computing technology and server-side JavaScript programming. This work highlights the potential application of GEE in processing large amounts of satellite Earth Observation (EO) Big Data for the free, long-term, and wide spatio-temporal wet/dry permanent salt water cavities and moisture monitoring of Imlili Sebkha. Optical and radar images were used to understand the functions of Imlili Sebkha in discovering underground hydrological networks. The main objective of this work was to investigate and evaluate the complementarity of optical Landsat, Sentinel-2 data, and Sentinel-1 radar data in such a desert environment. Results show that radar images are not only well suited in studying desertic areas but also in mapping the water cavities in desert wetland zones. The sensitivity of these images to the variations in the slope of the topographic surface facilitated the geological and geomorphological analyses of desert zones and helped reveal the hydrological functions of Imlili Sebkha in discovering buried underground networks.

Recently, hot stamping technology has been increasingly used in automotive structural parts with ultrahigh strength to meet the standards of both high fuel efficiency and crashworthiness. However, one issue of concern regarding these martensitic steels, which are fabricated using a hot stamping procedure, is that the steel is highly vulnerable to hydrogen delayed cracking caused by the diffusible hydrogen flow through the surface reaction of the coating in a furnace atmosphere. One way to make progress in understanding hydrogen delayed fractures is to elucidate an interaction for desorption with diffusible hydrogen behavior. The role of diffusible hydrogen on delayed fractures was studied for different baking times and temperatures in a range of automotive processes for hot-stamped martensitic steel with aluminum- and silicon-coated surfaces. It was clear that the release of diffusible hydrogen is effective at higher temperatures and longer times, making the steel less susceptible to hydrogen delayed fractures. Using thermal desorption spectroscopy, the phenomenon of the hydrogen delayed fracture was attributed to reversible hydrogen in microstructure sites with low trapping energy.

This study aims to present a technique that combines multi-sensor spatial data to monitor wetland areas after a flash-flood event in a Saharan arid region. To extract the most efficient information, seven satellite images (radar and optical) taken before and after the event were used. To achieve the objectives, this study used Sentinel-1 data to discriminate water body and soil roughness, and optical data to monitor the soil moisture after the event. The proposed method combines two approaches: one based on spectral processing, and the other based on categorical processing. The first step was to extract four spectral indices and utilize change vector analysis on multispectral diachronic images from three MSI Sentinel-2 images and two Landsat-8 OLI images acquired before and after the event. The second step was performed using pattern classification techniques, namely, linear classifiers based on support vector machines (SVM) with Gaussian kernels. The results of these two approaches were fused to generate a collaborative wetland change map. The application of co-registration and supervised classification based on textural and intensity information from Radar Sentinel-1 images taken before and after the event completes this work. The results obtained demonstrate the importance of the complementarity of multi-sensor images and a multi-approach methodology to better monitor changes to a wetland area after a flash-flood disaster.

Landslide is one of the most important geomorphological hazards that cause significant ecological and economic losses and results in billions of dollars in financial losses and thousands of casualties per year. The occurrence of landslide in northern Iran (Alborz Mountain Belt) is often due to the geological and climatic conditions and tectonic and human activities. To reduce or control the damage caused by landslides, landslide susceptibility mapping (LSM) and landslide risk assessment are necessary. In this study, the efficiency and integration of frequency ratio (FR) and random forest (RF) in statistical- and artificial intelligence-based models and different digital elevation models (DEMs) with various spatial resolutions were assessed in the field of LSM. The experiment was performed in Sangtarashan watershed, Mazandran Province, Iran. The study area, which extends to 1072.28 km2, is severely affected by landslides, which cause severe economic and ecological losses. An inventory of 129 landslides that occurred in the study area was prepared using various resources, such as historical landslide records, the interpretation of aerial photos and Google Earth images, and extensive field surveys. The inventory was split into training and test sets, which include 70 and 30% of the landslide locations, respectively. Subsequently, 15 topographic, hydrologic, geologic, and environmental landslide conditioning factors were selected as predictor variables of landslide occurrence on the basis of literature review, field works and multicollinearity analysis. Phased array type L-band synthetic aperture radar (PALSAR), ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), and SRTM (Shuttle Radar Topography Mission) DEMs were used to extract topographic and hydrologic attributes. The RF model showed that land use/land cover (16.95), normalised difference vegetation index (16.44), distance to road (15.32) and elevation (13.6) were the most important controlling variables. Assessment of model performance by calculating the area under the receiving operating characteristic curve parameter showed that FR−RF integrated model (0.917) achieved higher predictive accuracy than the individual FR (0.865) and RF (0.840) models. Comparison of PALSAR, ASTER, and SRTM DEMs with 12.5, 30 and 90 m spatial resolution, respectively, with the FR−RF integrated model showed that the prediction accuracy of FR−RF−PALSAR (0.917) was higher than FR−RF−ASTER (0.865) and FR−RF−SRTM (0.863). The results of this study could be used by local planners and decision makers for planning development projects and landslide hazard mitigation measures.

Polymetallic vein-type ores are important sources of precious metal and a principal type of orebody for various base-metals. In this research, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) remote sensing data were used for mapping hydrothermal alteration zones associated with epithermal polymetallic vein-type mineralization in the Toroud–Chahshirin Magmatic Belt (TCMB), North of Iran. The TCMB is the largest known goldfield and base metals province in the central-north of Iran. Propylitic, phyllic, argillic, and advanced argillic alteration and silicification zones are typically associated with Au-Cu, Ag, and/or Pb-Zn mineralization in the TCMB. Specialized image processing techniques, namely Selective Principal Component Analysis (SPCA), Band Ratio Matrix Transformation (BRMT), Spectral Angle Mapper (SAM) and Mixture Tuned Matched Filtering (MTMF) were implemented and compared to map hydrothermal alteration minerals at the pixel and sub-pixel levels. Subtle differences between altered and non-altered rocks and hydrothermal alteration mineral assemblages were detected and mapped in the study area. The SPCA and BRMT spectral transformation algorithms discriminated the propylitic, phyllic, argillic and advanced argillic alteration and silicification zones as well as lithological units. The SAM and MTMF spectral mapping algorithms detected spectrally dominated mineral groups such as muscovite/montmorillonite/illite, hematite/jarosite, and chlorite/epidote/calcite mineral assemblages, systematically. Comprehensive fieldwork and laboratory analysis, including X-ray diffraction (XRD), petrographic study, and spectroscopy were conducted in the study area for verifying the remote sensing outputs. Results indicate several high potential zones of epithermal polymetallic vein-type mineralization in the northeastern and southwestern parts of the study area, which can be considered for future systematic exploration programs. The approach used in this research has great implications for the exploration of epithermal polymetallic vein-type mineralization in other base metals provinces in Iran and semi-arid regions around the world.

To effectively prevent land subsidence over abandoned coal mines, it is necessary to quantitatively identify vulnerable areas. In this study, we evaluated the performance of predictive Bayesian, functional, and meta-ensemble machine learning models in generating land subsidence susceptibility (LSS) maps. All models were trained using half of a land subsidence inventory, and validated using the other half of the dataset. The model performance was evaluated by comparing the area under the receiver operating characteristic (ROC) curve of the resulting LSS map for each model. Among all models tested, the logit boost, which is a meta-ensemble machine leaning model, generated LSS maps with the highest accuracy (91.44%), i.e., higher than that of the other Bayesian and functional machine learning models, including the Bayes net (86.42%), naïve Bayes (85.39%), logistic (88.92%), and multilayer perceptron models (86.76%). The LSS maps produced in this study can be used to mitigate subsidence risk for people and important facilities within the study area, and as a foundation for further studies in other regions.

Traffic emissions are considered one of the leading causes of environmental impact in megacities and their dangerous effects on human health. This paper presents a hybrid model based on data mining and GIS models designed to predict vehicular Carbon Monoxide (CO) emitted from traffic on the New Klang Valley Expressway, Malaysia. The hybrid model was developed based on the integration of GIS and the optimized Artificial Neural Network algorithm that combined with the Correlation based Feature Selection (CFS) algorithm to predict the daily vehicular CO emissions and generate prediction maps at a microscale level in a small urban area by using a field survey and open source data, which are the main contributions to this paper. The other contribution is related to the case study, which represents the spatial and quantitative variations in the vehicular CO emissions between toll plaza areas and road networks. The proposed hybrid model consists of three steps: the first step is the implementation of the correlation-based Feature Selection model to select the best model’s predictors; the second step is the prediction of vehicular CO by using a multilayer perceptron neural network model; and the third step is the creation of micro scale prediction maps. The model was developed using six traffic CO predictors: number of vehicles, number of heavy vehicles, number of motorbikes, temperature, wind speed and a digital surface model. The network architecture and its hyperparameters were optimized through a grid search approach. The traffic CO concentrations were observed at 15-min intervals on weekends and weekdays, four times per day. The results showed that the developed model had achieved validation accuracy of 80.6 %. Overall, the developed models are found to be promising tools for vehicular CO simulations in highly congested areas.

A Mw 7.4 earthquake hit Donggala County, Central Sulawesi Province, Indonesia, on 28 September 2018, triggering a tsunami and liquefaction in Palu City and Donggala. Around 2101 fatalities ensued and 68,451 houses were damaged by the earthquake. In light of this devastating event, a post-earthquake map is required to establish the first step in the evacuation and mitigation plan. In this study, remote sensing imagery from the Landsat-8 and Sentinel-2 satellites was used. Pre- and post-earthquake satellite images were classified using artificial neural network (ANN) and support vector machine (SVM) classifiers and processed using a decorrelation method to generate the post-earthquake damage map. The affected areas were compared to the field data, the percentage conformity between the ANN and SVM results was analyzed, and four post-earthquake damage maps were generated. Based on the conformity analysis, the Landsat-8 imagery (85.83%) was superior to that of Sentinel-2 (63.88%). The resulting post-earthquake damage map can be used to assess the distribution of seismic damage following the Palu earthquake and may be used to mitigate damage in the event of future earthquakes.

We assessed landslide susceptibility using Chi-square Automatic Interaction Detection (CHAID), exhaustive CHAID, and Quick, Unbiased, and Efficient Statistical Tree (QUEST) decision tree models in Jumunjin-eup, Gangneung-si, Korea. A total of 548 landslides were identified based on interpretation of aerial photographs. Half of the 548 landslides were selected for modeling, and the remaining half were used for verification. We used 20 landslide control factors that were classified into five categories, namely topographic elements, hydrological elements, soil maps, forest maps, and geological maps, to determine landslide susceptibility. The relationships of landslide occurrence with landslide-inducing factors were analyzed using CHAID, exhaustive CHAID, and QUEST models. The three models were then verified using the area under the curve (AUC) method. The results showed that the CHAID model (AUC = 87.1%) was more accurate than the exhaustive CHAID (AUC = 86.9%) and QUEST models (AUC = 82.8%). The verification results showed that the CHAID model had the highest accuracy. There was high susceptibility to landslides in mountainous areas and low susceptibility in coastal areas. Analyzing the characteristics of the landslide control factors in advance will enable us to obtain more accurate results.

The main purpose of this study was to produce landslide susceptibility maps using various ensemble-based machine learning models (i.e., the AdaBoost, LogitBoost, Multiclass Classifier, and Bagging models) for the Sacheon-myeon area of South Korea. A landslide inventory map including a total of 762 landslides was compiled based on reports and aerial photograph interpretations. The landslides were randomly separated into two datasets: 70% of landslides were selected for the model establishment and 30% were used for validation purposes. Additionally, 20 landslide condition factors divided into five categories (topographic factors, hydrological factors, soil map, geological map, and forest map) were considered in the landslide susceptibility mapping. The relationships among landslide occurrence and landslide conditioning factors were analyzed and the landslide susceptibility maps were calculated and drawn using the AdaBoost, LogitBoost, Multiclass Classifier, and Bagging models. Finally, the maps were validated using the area under the curve (AUC) method. The Multiclass Classifier method had higher prediction accuracy (85.9%) than the Bagging (AUC = 85.4%), LogitBoost (AUC = 84.8%), and AdaBoost (84.0%) methods.

Volcanic eruptions cause pyroclastic flows, which can destroy plantations and settlements. We used image data from Landsat 7 Bands 7, 4 and 2 and Landsat 8 Bands 7, 5 and 3 to observe and analyze the distribution of pyroclastic flow deposits for two volcanos, Mount Sinabung and Merapi, over a period of 10 years (2001–2017). The satellite data are used in conjunction with an artificial neural network method to produce maps of pyroclastic precipitation for Landsat 7 and 8, then we calculated the pyroclastic precipitation area using an artificial neural network method after dividing the images into four classes based on color. Red, green, blue and yellow were used to indicate pyroclastic deposits, vegetation and forest, water and cloud, and farmland, respectively. The area affected by a volcanic eruption was deduced from the neural network processing, including calculating the area of pyroclastic deposits. The main differences between the pyroclastic flow deposits of Mount Sinabung and Mount Merapi are: the sediment deposits of the pyroclastic flows of Mount Sinabung tend to widen, whereas those of Merapi elongated; the direction of pyroclastic flow differed; and the area affected by an eruption was greater for Mount Merapi than Mount Sinabung because the VEI (Volcanic Explosivity Index) during the last 10 years of Mount Merapi was larger than Mount Sinabung.

A multi-temporal SAR interferometry (InSAR) method was applied to a time-series analysis of the period from 1992 - 1998 to detect precursor volcanic activity at Baekdusan volcano, located on the border between China and North Korea. Permanent scatterers were selected for stable signal processing to monitor ground motion using the permanent scatterer InSAR (PSInSAR) technique, which generated a time-series surface deformation map using the refined small baseline subset (SBAS) InSAR method. The time-series analysis results were verified using a multi-track comparison technique for two different tracks (88/230 and 89/230). The time-series analysis of the two tracks represents an unaltered phase signal (mm scale) with time, except for periods with insufficient interferograms to make a precise time-series surface deformation map due to factors including atmospheric phase delay and other error effects (i.e., those that display a maximum value of the phase difference from -2.14 to +2.64 cm). These phase signals also have the Root Mean Square Error (RMSE) range from 0.32 - 2.56 cm. No surface deformation was detected from the Japanese Earth Resources Satellites (JERS-1) synthetic aperture radar (SAR) data collected between 1992 - 1998 at the Baekdusan volcano, as monitored using volcanic surface movements with dynamic magma activity. No volcanic eruption precursor signals were demonstrated during the inactive period (1992 - 1998) prior to the active period (2002 - 2006) of this volcano.

Infection of leukemia in humans causes many complications in its later stages. It impairs bone marrow's ability to produce blood. Morphological diagnosis of human blood cells is a well-known and well-proven technique for diagnosis in this case. The binary classification is employed to distinguish between normal and leukemiainfected cells. In addition, various subtypes of leukemia require different treatments. These sub-classes must also be detected to obtain an accurate diagnosis of the type of leukemia. This entails using multi-class classification to determine the leukemia subtype. This is usually done using a microscopic examination of these blood cells. Due to the requirement of a trained pathologist, the decision process is critical, which leads to the development of an automated software framework for diagnosis. Researchers utilized state-of-the-art machine learning approaches, such as Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes, K-Nearest Neighbor (KNN), and others, to provide limited accuracies of classification. More advanced deep-learning methods are also utilized. Due to constrained dataset sizes, these approaches result in over-fitting, reducing their outstanding performances. This study introduces a deep learning-machine learning combined approach for leukemia diagnosis. It uses deep transfer learning frameworks to extract and classify features using state-of-the-artmachine learning classifiers. The transfer learning frameworks such as VGGNet, Xception, InceptionResV2, Densenet, and ResNet are employed as feature extractors. The extracted features are given to RF and XGBoost classifiers for the binary and multi-class classification of leukemia cells. For the experimentation, a very popular ALL-IDB dataset is used, approaching a maximum accuracy of 100%. A private real images dataset with three subclasses of leukemia images, including Acute Myloid Leukemia (AML), Chronic Lymphocytic Leukemia (CLL), and Chronic Myloid Leukemia (CML), is also employed to generalize the system. This dataset achieves an impressive multi-class classification accuracy of 97.08%. The proposed approach is robust and generalized by a standardized dataset and the real image dataset with a limited sample size (520 images). Hence, thismethod can be explored further for leukemia diagnosis having a limited number of dataset samples. [ABSTRACT FROM AUTHOR]