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Fulltext Factors determining generalization in deep learning models for scoring COVID-CT images

Horry MJ, Chakraborty S, Pradhan B, Fallahpoor M, Chegeni H, Paul M

Math Biosci Eng, 2021 10 27;18(6):9264-9293.
PMID: 34814345 DOI: 10.3934/mbe.2021456

The COVID-19 pandemic has inspired unprecedented data collection and computer vision modelling efforts worldwide, focused on the diagnosis of COVID-19 from medical images. However, these models have found limited, if any, clinical application due in part to unproven generalization to data sets beyond their source training corpus. This study investigates the generalizability of deep learning models using publicly available COVID-19 Computed Tomography data through cross dataset validation. The predictive ability of these models for COVID-19 severity is assessed using an independent dataset that is stratified for COVID-19 lung involvement. Each inter-dataset study is performed using histogram equalization, and contrast limited adaptive histogram equalization with and without a learning Gabor filter. We show that under certain conditions, deep learning models can generalize well to an external dataset with F1 scores up to 86%. The best performing model shows predictive accuracy of between 75% and 96% for lung involvement scoring against an external expertly stratified dataset. From these results we identify key factors promoting deep learning generalization, being primarily the uniform acquisition of training images, and secondly diversity in CT slice position.
Fulltext Generalizability assessment of COVID-19 3D CT data for deep learning-based disease detection

Fallahpoor M, Chakraborty S, Heshejin MT, Chegeni H, Horry MJ, Pradhan B

Comput Biol Med, 2022 Jun;145:105464.
PMID: 35390746 DOI: 10.1016/j.compbiomed.2022.105464

BACKGROUND: Artificial intelligence technologies in classification/detection of COVID-19 positive cases suffer from generalizability. Moreover, accessing and preparing another large dataset is not always feasible and time-consuming. Several studies have combined smaller COVID-19 CT datasets into "supersets" to maximize the number of training samples. This study aims to assess generalizability by splitting datasets into different portions based on 3D CT images using deep learning.
METHOD: Two large datasets, including 1110 3D CT images, were split into five segments of 20% each. Each dataset's first 20% segment was separated as a holdout test set. 3D-CNN training was performed with the remaining 80% from each dataset. Two small external datasets were also used to independently evaluate the trained models.
RESULTS: The total combination of 80% of each dataset has an accuracy of 91% on Iranmehr and 83% on Moscow holdout test datasets. Results indicated that 80% of the primary datasets are adequate for fully training a model. The additional fine-tuning using 40% of a secondary dataset helps the model generalize to a third, unseen dataset. The highest accuracy achieved through transfer learning was 85% on LDCT dataset and 83% on Iranmehr holdout test sets when retrained on 80% of Iranmehr dataset.
CONCLUSION: While the total combination of both datasets produced the best results, different combinations and transfer learning still produced generalizable results. Adopting the proposed methodology may help to obtain satisfactory results in the case of limited external datasets.
Fulltext Data Mining and Statistical Approaches in Debris-Flow Susceptibility Modelling Using Airborne LiDAR Data

Lay US, Pradhan B, Yusoff ZBM, Abdallah AFB, Aryal J, Park HJ

Sensors (Basel), 2019 Aug 07;19(16).
PMID: 31394777 DOI: 10.3390/s19163451

Cameron Highland is a popular tourist hub in the mountainous area of Peninsular Malaysia. Most communities in this area suffer frequent incidence of debris flow, especially during monsoon seasons. Despite the loss of lives and properties recorded annually from debris flow, most studies in the region concentrate on landslides and flood susceptibilities. In this study, debris-flow susceptibility prediction was carried out using two data mining techniques; Multivariate Adaptive Regression Splines (MARS) and Support Vector Regression (SVR) models. The existing inventory of debris-flow events (640 points) were selected for training 70% (448) and validation 30% (192). Twelve conditioning factors namely; elevation, plan-curvature, slope angle, total curvature, slope aspect, Stream Transport Index (STI), profile curvature, roughness index, Stream Catchment Area (SCA), Stream Power Index (SPI), Topographic Wetness Index (TWI) and Topographic Position Index (TPI) were selected from Light Detection and Ranging (LiDAR)-derived Digital Elevation Model (DEM) data. Multi-collinearity was checked using Information Factor, Cramer's V, and Gini Index to identify the relative importance of conditioning factors. The susceptibility models were produced and categorized into five classes; not-susceptible, low, moderate, high and very-high classes. Models performances were evaluated using success and prediction rates where the area under the curve (AUC) showed a higher performance of MARS (93% and 83%) over SVR (76% and 72%). The result of this study will be important in contingency hazards and risks management plans to reduce the loss of lives and properties in the area.
Fulltext Extraction of Roads Using the Archimedes Tuning Process with the Quantum Dilated Convolutional Neural Network

Khan MJ, Singh PP, Pradhan B, Alamri A, Lee CW

Sensors (Basel), 2023 Oct 28;23(21).
PMID: 37960482 DOI: 10.3390/s23218783

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.
Fulltext A Novel Swarm Intelligence-Harris Hawks Optimization for Spatial Assessment of Landslide Susceptibility

Bui DT, Moayedi H, Kalantar B, Osouli A, Pradhan B, Nguyen H, et al.

Sensors (Basel), 2019 Aug 17;19(16).
PMID: 31426552 DOI: 10.3390/s19163590

In this research, the novel metaheuristic algorithm Harris hawks optimization (HHO) is applied to landslide susceptibility analysis in Western Iran. To this end, the HHO is synthesized with an artificial neural network (ANN) to optimize its performance. A spatial database comprising 208 historical landslides, as well as 14 landslide conditioning factors-elevation, slope aspect, plan curvature, profile curvature, soil type, lithology, distance to the river, distance to the road, distance to the fault, land cover, slope degree, stream power index (SPI), topographic wetness index (TWI), and rainfall-is prepared to develop the ANN and HHO-ANN predictive tools. Mean square error and mean absolute error criteria are defined to measure the performance error of the models, and area under the receiving operating characteristic curve (AUROC) is used to evaluate the accuracy of the generated susceptibility maps. The findings showed that the HHO algorithm effectively improved the performance of ANN in both recognizing (AUROCANN = 0.731 and AUROCHHO-ANN = 0.777) and predicting (AUROCANN = 0.720 and AUROCHHO-ANN = 0.773) the landslide pattern.
Fulltext A spatio-temporal agent-based approach for modeling the spread of zoonotic cutaneous leishmaniasis in northeast Iran

Tabasi M, Alesheikh AA, Sofizadeh A, Saeidian B, Pradhan B, AlAmri A

Parasit Vectors, 2020 Nov 11;13(1):572.
PMID: 33176858 DOI: 10.1186/s13071-020-04447-x

BACKGROUND: Zoonotic cutaneous leishmaniasis (ZCL) is a neglected tropical disease worldwide, especially the Middle East. Although previous works attempt to model the ZCL spread using various environmental factors, the interactions between vectors (Phlebotomus papatasi), reservoir hosts, humans, and the environment can affect its spread. Considering all of these aspects is not a trivial task.
METHODS: An agent-based model (ABM) is a relatively new approach that provides a framework for analyzing the heterogeneity of the interactions, along with biological and environmental factors in such complex systems. The objective of this research is to design and develop an ABM that uses Geospatial Information System (GIS) capabilities, biological behaviors of vectors and reservoir hosts, and an improved Susceptible-Exposed-Infected-Recovered (SEIR) epidemic model to explore the spread of ZCL. Various scenarios were implemented to analyze the future ZCL spreads in different parts of Maraveh Tappeh County, in the northeast region of Golestan Province in northeastern Iran, with alternative socio-ecological conditions.
RESULTS: The results confirmed that the spread of the disease arises principally in the desert, low altitude areas, and riverside population centers. The outcomes also showed that the restricting movement of humans reduces the severity of the transmission. Moreover, the spread of ZCL has a particular temporal pattern, since the most prevalent cases occurred in the fall. The evaluation test also showed the similarity between the results and the reported spatiotemporal trends.
CONCLUSIONS: This study demonstrates the capability and efficiency of ABM to model and predict the spread of ZCL. The results of the presented approach can be considered as a guide for public health management and controlling the vector population .
An interpretable (explainable) model based on machine learning and SHAP interpretation technique for mapping wind erosion hazard

Gholami H, Darvishi E, Moradi N, Mohammadifar A, Song Y, Li Y, et al.

Environ Sci Pollut Res Int, 2024 Nov 15.
PMID: 39546243 DOI: 10.1007/s11356-024-35521-x

Soil erosion by wind poses a significant threat to various regions across the globe, such as drylands in the Middle East and Iran. Wind erosion hazard maps can assist in identifying the regions of highest wind erosion risk and are a valuable tool for the mitigation of its destructive consequences. This study aims to map wind erosion hazards by developing an interpretable (explainable) model based on machine learning (ML) and Shapley additive exPlanation (SHAP) interpretation techniques. Four ML models, namely random forest (RF), support vector machine (SVM), extreme gradient boosting (XGB), and quadratic discriminant analysis (QDA) were used. Thirteen features associated with wind erosion were mapped spatially and then subjected to a multivariate adaptive regression spline (MARS) feature selection algorithm, and then, tolerance coefficient (TC) and variance inflation factor (VIF) statistical tests were used to explore multicollinearity among the variables. MARS analysis shows that eight features consisting of elevation (or DEM), soil bulk density, precipitation, aspect, slope, soil sand content, vegetation cover (or NDVI), and lithology were the most effective for wind erosion, while no collinearity existed among these variables. The ML models were used for ranking the effective features, and the research introduces the application of an interpretable ML model for the interpretation of predictive model's output. The ranking of effective features by RF-as the most typical ML model-revealed that elevation and soil bulk density were the two most important features. According to the area under the receiver operating characteristic curve (AUROC) (with a value > 90%) and precision-recall (PR) (with a value > 90%) curves, all four ML models performed with great accuracy. According to the PR curve, the SVM model performed slightly better than others, and its results revealed that 20.9%, 23%, and 16.6% of the total area in Hormozgan Province is characterized by moderate, high, and very high hazard classes to wind erosion, respectively. SHAP revealed that soil sand content and elevation are the most important variables contributing to the predictive model output. Overall, our research is one of the pioneering applications of interpretable ML models in mapping wind erosion hazards in Southern Iran. We recommend that future research should address the aspect of interpretability in order to better understand predictive model outputs.
Fulltext Development of Debiasing Technique for Lung Nodule Chest X-ray Datasets to Generalize Deep Learning Models

Horry MJ, Chakraborty S, Pradhan B, Paul M, Zhu J, Loh HW, et al.

Sensors (Basel), 2023 Jul 21;23(14).
PMID: 37514877 DOI: 10.3390/s23146585

Screening programs for early lung cancer diagnosis are uncommon, primarily due to the challenge of reaching at-risk patients located in rural areas far from medical facilities. To overcome this obstacle, a comprehensive approach is needed that combines mobility, low cost, speed, accuracy, and privacy. One potential solution lies in combining the chest X-ray imaging mode with federated deep learning, ensuring that no single data source can bias the model adversely. This study presents a pre-processing pipeline designed to debias chest X-ray images, thereby enhancing internal classification and external generalization. The pipeline employs a pruning mechanism to train a deep learning model for nodule detection, utilizing the most informative images from a publicly available lung nodule X-ray dataset. Histogram equalization is used to remove systematic differences in image brightness and contrast. Model training is then performed using combinations of lung field segmentation, close cropping, and rib/bone suppression. The resulting deep learning models, generated through this pre-processing pipeline, demonstrate successful generalization on an independent lung nodule dataset. By eliminating confounding variables in chest X-ray images and suppressing signal noise from the bone structures, the proposed deep learning lung nodule detection algorithm achieves an external generalization accuracy of 89%. This approach paves the way for the development of a low-cost and accessible deep learning-based clinical system for lung cancer screening.
Fulltext Mapping groundwater potential zone in the subarnarekha basin, India, using a novel hybrid multi-criteria approach in Google earth Engine

Singha C, Swain KC, Pradhan B, Rusia DK, Moghimi A, Ranjgar B

Heliyon, 2024 Jan 30;10(2):e24308.
PMID: 38293330 DOI: 10.1016/j.heliyon.2024.e24308

Assessing groundwater potential for sustainable resource management is critically important. In addressing this concern, this study aims to advance the field by developing an innovative approach for Groundwater potential zone (GWPZ) mapping using advanced techniques, such as FuzzyAHP, FuzzyDEMATEL, and Logistic regression (LR) models. GWPZ was carried out by integrating various primary factors, such as hydrologic, soil permeability, morphometric, terrain distribution, and anthropogenic influences, incorporating twenty-seven individual criteria using multi-criteria decision models along with a hybrid approach for the Subarnarekha River basin, India, in Google earth engine (GEE). The predictive capability of the model was evaluated using a Multi-Collinearity test (VIF <10.0), followed by applying a random forest model, considering the weighted impact of the five primary factors. The hybrid model for GWPZ classification showed that 21.97 % (4256.3 km2) of the area exhibited very high potential, while 11.37 % (2202.1 km2) indicated very low potential for GW in this area. Validation of the groundwater level data from 72 observation wells, performed by the Area under receiver operating characteristic (AUROC) curve technique, yielded values ranging between 75 % and 78 % for different models, underscoring the robust predictability of GWPZ. The hybrid and LR-FuzzyAHP models demonstrated remarkable effectiveness in GWPZ mapping, indicating that the downstream and southern regions boast substantial groundwater potential attributed to alluvial soil and favorable recharge conditions. Conversely, the central part grapples with a scarcity of groundwater. It holds the potential to assist planners and managers in formulating strategies for managing groundwater levels and alleviating the impacts of future droughts.
Deep learning techniques in PET/CT imaging: A comprehensive review from sinogram to image space

Fallahpoor M, Chakraborty S, Pradhan B, Faust O, Barua PD, Chegeni H, et al.

Comput Methods Programs Biomed, 2024 Jan;243:107880.
PMID: 37924769 DOI: 10.1016/j.cmpb.2023.107880

Positron emission tomography/computed tomography (PET/CT) is increasingly used in oncology, neurology, cardiology, and emerging medical fields. The success stems from the cohesive information that hybrid PET/CT imaging offers, surpassing the capabilities of individual modalities when used in isolation for different malignancies. However, manual image interpretation requires extensive disease-specific knowledge, and it is a time-consuming aspect of physicians' daily routines. Deep learning algorithms, akin to a practitioner during training, extract knowledge from images to facilitate the diagnosis process by detecting symptoms and enhancing images. This acquired knowledge aids in supporting the diagnosis process through symptom detection and image enhancement. The available review papers on PET/CT imaging have a drawback as they either included additional modalities or examined various types of AI applications. However, there has been a lack of comprehensive investigation specifically focused on the highly specific use of AI, and deep learning, on PET/CT images. This review aims to fill that gap by investigating the characteristics of approaches used in papers that employed deep learning for PET/CT imaging. Within the review, we identified 99 studies published between 2017 and 2022 that applied deep learning to PET/CT images. We also identified the best pre-processing algorithms and the most effective deep learning models reported for PET/CT while highlighting the current limitations. Our review underscores the potential of deep learning (DL) in PET/CT imaging, with successful applications in lesion detection, tumor segmentation, and disease classification in both sinogram and image spaces. Common and specific pre-processing techniques are also discussed. DL algorithms excel at extracting meaningful features, and enhancing accuracy and efficiency in diagnosis. However, limitations arise from the scarcity of annotated datasets and challenges in explainability and uncertainty. Recent DL models, such as attention-based models, generative models, multi-modal models, graph convolutional networks, and transformers, are promising for improving PET/CT studies. Additionally, radiomics has garnered attention for tumor classification and predicting patient outcomes. Ongoing research is crucial to explore new applications and improve the accuracy of DL models in this rapidly evolving field.
Fulltext An explainable Bi-LSTM model for winter wheat yield prediction

Joshi A, Pradhan B, Chakraborty S, Varatharajoo R, Alamri A, Gite S, et al.

Front Plant Sci, 2024;15:1491493.
PMID: 39898259 DOI: 10.3389/fpls.2024.1491493

Accurate, reliable and transparent crop yield prediction is crucial for informed decision-making by governments, farmers, and businesses regarding food security as well as agricultural business and management. Deep learning (DL) methods, particularly Long Short-Term Memory networks, have emerged as one of the most widely used architectures in yield prediction studies, providing promising results. Although other sequential DL methods like 1D Convolutional Neural Networks (1D-CNN) and Bidirectional long short-term memory (Bi-LSTM) have shown high accuracy for various tasks, including crop yield prediction, their application in regional scale crop yield prediction remains largely unexplored. Interpretability is another pressing and challenging issue in DL-based crop yield prediction, a factor that ensures the reliability of the model. Thus, this study aims to develop and implement an explainable DL model capable of accurately predicting crop yield and providing explanations for the predictions. To achieve this, we developed three state-of-the-art sequential DL models: LSTM, 1D CNN, and Bi-LSTM. We then employed three popular interpretability techniques: Local interpretable model-agnostic explanations (LIME), Integrated Gradient (IG) and Shapley Additive Explanation (SHAP) to understand the decision-making process of the models. The Bi-LSTM model outperformed other models in terms of predictive performance (R2 up to 0.88) and generalizability across locations and ranges of yield data. Explainability analysis reveals that enhanced vegetation index (EVI), temperature and precipitation at later stages of crop growth are most important in determining Winter wheat yield. Further, we demonstrated that XAI methods can also be used to understand the decision-making process of the models, to understand instances such as high- and low-yield samples, to find possible explanations for erroneous predictions, and to identify regions impacted by particular stress. By employing advanced DL techniques along with an innovative approach to explainability, this study achieves highly accurate yield prediction while providing intuitive insights into the model's decision-making process.
Fulltext Temporal associations between microclimate, adult Aedes mosquito indices, and dengue cases at the residence level in Malaysia: Implications for targeted interventions

Mohd Hardy Abdullah NA, Dom NC, Pradhan B, Salleh SA, Dapari R

PLoS One, 2025;20(2):e0316564.
PMID: 39899560 DOI: 10.1371/journal.pone.0316564

INTRODUCTION: Dengue continues to be a major public health concern in Malaysia, as evidenced by the significant surge in cumulative dengue case numbers and deaths in 2023 compared to the previous year. While previous studies have explored the interplay of abiotic and biotic factors of mosquito density and dengue cases on a local scale in Malaysia, there is a notable gap in the research focusing on adult Aedes mosquito populations.
AIMS: This study aims to contribute to the existing knowledge by investigating the association and time lags (TLs) between daily microclimate (DM), mosquito indices (MIs), and dengue cases at the residence level.
METHODS: In this longitudinal study, field data were collected over 26 weeks using data loggers, gravid oviposit sticky (GOS) traps, and non-structural 1 (NS1) test kits in both non-dengue hotspot (NDH) and dengue hotspots (DH). The collected data encompassed DM variables, vegetation cover (VC), MIs, and number of dengue cases. An autocorrelation analysis was conducted to determine the TLs between MIs and their preceding values, while a cross-correlation analysis revealed the TLs between MIs and DM variables.
RESULTS: The study indicated there are positive correlations between the adult index (AI) of Ae. albopictus, their preceding values and rainfall at an NDH. Conversely, the AIs of total Aedes at the DH exhibited positive correlations with their preceding values, temperature, rainfall, and maximum relative humidity (RH), but negative correlations with the mean and maximum RH. The dengue-positive trap index (DPTI) of total Aedes at DHs demonstrated positive associations with their preceding values, mean temperature, minimum temperature, maximum RH, and rainfall, with negative correlations observed for the maximum temperature, mean RH, and minimum RH. Similar trends were identified for the Ae. aegypti and Ae. albopictus at DHs. The association between dengue cases, DM, and MIs was inconclusive due to underreported cases.
CONCLUSIONS: This study highlighted the DM and TLs of dengue virus-infected and non-infected adult female Aedes mosquitoes using onsite data collection. Furthermore, this study presents a replicable methodology that can be adopted by researchers worldwide for investigating the dynamics of dengue transmission in similar settings. The findings offer valuable insights for decision-makers, providing them with evidence-based information to implement targeted interventions and strategies aimed at controlling Aedes mosquito populations and mitigating the spread of dengue virus infections.
Modeling flood susceptibility using data-driven approaches of naïve Bayes tree, alternating decision tree, and random forest methods

Chen W, Li Y, Xue W, Shahabi H, Li S, Hong H, et al.

Sci Total Environ, 2020 Jan 20;701:134979.
PMID: 31733400 DOI: 10.1016/j.scitotenv.2019.134979

Floods are one of the most devastating types of disasters that cause loss of lives and property worldwide each year. This study aimed to evaluate and compare the prediction capability of the naïve Bayes tree (NBTree), alternating decision tree (ADTree), and random forest (RF) methods for the spatial prediction of flood occurrence in the Quannan area, China. A flood inventory map with 363 flood locations was produced and partitioned into training and validation datasets through random selection with a ratio of 70/30. The spatial flood database was constructed using thirteen flood explanatory factors. The probability certainty factor (PCF) method was used to analyze the correlation between the factors and flood occurrences. Consequently, three flood susceptibility maps were produced using the NBTree, ADTree, and RF methods. Finally, the area under the curve (AUC) and statistical measures were used to validate the flood susceptibility models. The results indicated that the RF method is an efficient and reliable model in flood susceptibility assessment, with the highest AUC values, positive predictive rate, negative predictive rate, sensitivity, specificity, and accuracy for the training (0.951, 0.892, 0.941, 0.945, 0.886, and 0.915, respectively) and validation (0.925, 0.851, 0.938, 0.945, 0.835, and 0.890, respectively) datasets.
Multi-air pollution risk assessment in Southeast Asia region using integrated remote sensing and socio-economic data products

Sakti AD, Anggraini TS, Ihsan KTN, Misra P, Trang NTQ, Pradhan B, et al.

Sci Total Environ, 2023 Jan 01;854:158825.
PMID: 36116660 DOI: 10.1016/j.scitotenv.2022.158825

Air pollution has massive impacts on human life and poor air quality results in three million deaths annually. Air pollution can result from natural causes, including volcanic eruptions and extreme droughts, or human activities, including motor vehicle emissions, industry, and the burning of farmland and forests. Emission sources emit multiple pollutant types with diverse characteristics and impacts. However, there has been little research on the risk of multiple air pollutants; thus, it is difficult to identify multi-pollutant mitigation processes, particularly in Southeast Asia, where air pollution moves dynamically across national borders. In this study, the main objective was to develop a multi-air pollution risk index product for CO, NO2, and SO2 based on Sentinel-5P remote sensing data from 2019 to 2020. The risk index was developed by integrating hazard, vulnerability, and exposure analyses. Hazard analysis considers air pollution data from remote sensing, vulnerability analysis considers the air pollution sources, and exposure analysis considers the population density. The novelty of this study lies in its development of a multi-risk model that considers the weights obtained from the relationship between the hazard and vulnerability parameters. The highest air pollution risk index values were observed in urban areas, with a high exposure index that originates from pollution caused by human activity. Multi-risk analysis of the three air pollutants revealed that Singapore, Vietnam, and the Philippines had the largest percentages of high-risk areas, while Indonesia had the largest total high-risk area (4361 km2). Using the findings of this study, the patterns and characteristics of the risk distribution of multiple air pollutants in Southeast Asia can be identified, which can be used to mitigate multi-pollutant sources, particularly with respect to supporting the clean air targets in the Sustainable Development Goals.
Fulltext Land Subsidence Susceptibility Mapping in South Korea Using Machine Learning Algorithms

Tien Bui D, Shahabi H, Shirzadi A, Chapi K, Pradhan B, Chen W, et al.

Sensors (Basel), 2018 Jul 31;18(8).
PMID: 30065216 DOI: 10.3390/s18082464

In this study, land subsidence susceptibility was assessed for a study area in South Korea by using four machine learning models including Bayesian Logistic Regression (BLR), Support Vector Machine (SVM), Logistic Model Tree (LMT) and Alternate Decision Tree (ADTree). Eight conditioning factors were distinguished as the most important affecting factors on land subsidence of Jeong-am area, including slope angle, distance to drift, drift density, geology, distance to lineament, lineament density, land use and rock-mass rating (RMR) were applied to modelling. About 24 previously occurred land subsidence were surveyed and used as training dataset (70% of data) and validation dataset (30% of data) in the modelling process. Each studied model generated a land subsidence susceptibility map (LSSM). The maps were verified using several appropriate tools including statistical indices, the area under the receiver operating characteristic (AUROC) and success rate (SR) and prediction rate (PR) curves. The results of this study indicated that the BLR model produced LSSM with higher acceptable accuracy and reliability compared to the other applied models, even though the other models also had reasonable results.
Fulltext Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping

Shirzadi A, Soliamani K, Habibnejhad M, Kavian A, Chapi K, Shahabi H, et al.

Sensors (Basel), 2018 Nov 05;18(11).
PMID: 30400627 DOI: 10.3390/s18113777

The main objective of this research was to introduce a novel machine learning algorithm of alternating decision tree (ADTree) based on the multiboost (MB), bagging (BA), rotation forest (RF) and random subspace (RS) ensemble algorithms under two scenarios of different sample sizes and raster resolutions for spatial prediction of shallow landslides around Bijar City, Kurdistan Province, Iran. The evaluation of modeling process was checked by some statistical measures and area under the receiver operating characteristic curve (AUROC). Results show that, for combination of sample sizes of 60%/40% and 70%/30% with a raster resolution of 10 m, the RS model, while, for 80%/20% and 90%/10% with a raster resolution of 20 m, the MB model obtained a high goodness-of-fit and prediction accuracy. The RS-ADTree and MB-ADTree ensemble models outperformed the ADTree model in two scenarios. Overall, MB-ADTree in sample size of 80%/20% with a resolution of 20 m (area under the curve (AUC) = 0.942) and sample size of 60%/40% with a resolution of 10 m (AUC = 0.845) had the highest and lowest prediction accuracy, respectively. The findings confirm that the newly proposed models are very promising alternative tools to assist planners and decision makers in the task of managing landslide prone areas.
Fulltext A Novel Ensemble Artificial Intelligence Approach for Gully Erosion Mapping in a Semi-Arid Watershed (Iran)

Tien Bui D, Shirzadi A, Shahabi H, Chapi K, Omidavr E, Pham BT, et al.

Sensors (Basel), 2019 May 29;19(11).
PMID: 31146336 DOI: 10.3390/s19112444

In this study, we introduced a novel hybrid artificial intelligence approach of rotation forest (RF) as a Meta/ensemble classifier based on alternating decision tree (ADTree) as a base classifier called RF-ADTree in order to spatially predict gully erosion at Klocheh watershed of Kurdistan province, Iran. A total of 915 gully erosion locations along with 22 gully conditioning factors were used to construct a database. Some soft computing benchmark models (SCBM) including the ADTree, the Support Vector Machine by two kernel functions such as Polynomial and Radial Base Function (SVM-Polynomial and SVM-RBF), the Logistic Regression (LR), and the Naïve Bayes Multinomial Updatable (NBMU) models were used for comparison of the designed model. Results indicated that 19 conditioning factors were effective among which distance to river, geomorphology, land use, hydrological group, lithology and slope angle were the most remarkable factors for gully modeling process. Additionally, results of modeling concluded the RF-ADTree ensemble model could significantly improve (area under the curve (AUC) = 0.906) the prediction accuracy of the ADTree model (AUC = 0.882). The new proposed model had also the highest performance (AUC = 0.913) in comparison to the SVM-Polynomial model (AUC = 0.879), the SVM-RBF model (AUC = 0.867), the LR model (AUC = 0.75), the ADTree model (AUC = 0.861) and the NBMU model (AUC = 0.811).
Fulltext Retraction Note: Weather indicators and improving air quality in association with COVID-19 pandemic in India

Chakrabortty R, Pal SC, Ghosh M, Arabameri A, Saha A, Roy P, et al.

Soft comput, 2023 May 29.
PMID: 37362259 DOI: 10.1007/s00500-023-08596-w

[This retracts the article DOI: 10.1007/s00500-021-06012-9.].
Fulltext Weather indicators and improving air quality in association with COVID-19 pandemic in India

Chakrabortty R, Pal SC, Ghosh M, Arabameri A, Saha A, Roy P, et al.

Soft comput, 2023;27(6):3367-3388.
PMID: 34276248 DOI: 10.1007/s00500-021-06012-9

The COVID-19 pandemic enforced nationwide lockdown, which has restricted human activities from March 24 to May 3, 2020, resulted in an improved air quality across India. The present research investigates the connection between COVID-19 pandemic-imposed lockdown and its relation to the present air quality in India; besides, relationship between climate variables and daily new affected cases of Coronavirus and mortality in India during the this period has also been examined. The selected seven air quality pollutant parameters (PM10, PM2.5, CO, NO2, SO2, NH3, and O3) at 223 monitoring stations and temperature recorded in New Delhi were used to investigate the spatial pattern of air quality throughout the lockdown. The results showed that the air quality has improved across the country and average temperature and maximum temperature were connected to the outbreak of the COVID-19 pandemic. This outcomes indicates that there is no such relation between climatic parameters and outbreak and its associated mortality. This study will assist the policy maker, researcher, urban planner, and health expert to make suitable strategies against the spreading of COVID-19 in India and abroad.
SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00500-021-06012-9.
Fulltext Effects of the COVID-19 lockdown and recovery on People's mobility and air quality in the United Arab Emirates using satellite and ground observations

Shanableh A, Al-Ruzouq R, Hamad K, Gibril MBA, Khalil MA, Khalifa I, et al.

Remote Sens Appl, 2022 Apr;26:100757.
PMID: 36281297 DOI: 10.1016/j.rsase.2022.100757

The stringent COVID-19 lockdown measures in 2020 significantly impacted people's mobility and air quality worldwide. This study presents an assessment of the impacts of the lockdown and the subsequent reopening on air quality and people's mobility in the United Arab Emirates (UAE). Google's community mobility reports and UAE's government lockdown measures were used to assess the changes in the mobility patterns. Time-series and statistical analyses of various air pollutants levels (NO2, O3, SO2, PM10, and aerosol optical depth-AOD) obtained from satellite images and ground monitoring stations were used to assess air quality. The levels of pollutants during the initial lockdown (March to June 2020) and the subsequent gradual reopening in 2020 and 2021 were compared with their average levels during 2015-2019. During the lockdown, people's mobility in the workplace, parks, shops and pharmacies, transit stations, and retail and recreation sectors decreased by about 34%-79%. However, the mobility in the residential sector increased by up to 29%. The satellite-based data indicated significant reductions in NO2 (up to 22%), SO2 (up to 17%), and AOD (up to 40%) with small changes in O3 (up to 5%) during the lockdown. Similarly, data from the ground monitoring stations showed significant reductions in NO2 (49% - 57%) and PM10 (19% - 64%); however, the SO2 and O3 levels showed inconsistent trends. The ground and satellite-based air quality levels were positively correlated for NO2, PM10, and AOD. The data also demonstrated significant correlations between the mobility and NO2 and AOD levels during the lockdown and recovery periods. The study documents the impacts of the lockdown on people's mobility and air quality and provides useful data and analyses for researchers, planners, and policymakers relevant to managing risk, mobility, and air quality.

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