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In this paper, we assess the extent of environmental pollution in terms of PM2.5 particulate matter and noise in Tikrit University, located in Tikrit City of Iraq. The geographic information systems (GIS) technology was used for data analysis. Moreover, we built two multiple linear regression models (based on two different data inputs) for the prediction of PM2.5 particulate matter, which were based on the explanatory variables of maximum and minimum noise, temperature, and humidity. Furthermore, the maximum prediction coefficient R2 of the best models was 0.82, with a validated (via testing data) coefficient R2 of 0.94. From the actual total distribution of PM2.5 particulate values ranging from 35–58 μg/m3, our best model managed to predict values between 34.9–60.6 μg/m3. At the end of the study, the overall air quality was determined between moderate and harmful. In addition, the overall detected noise ranged from 49.30–85.79 dB, which inevitably designated the study area to be categorized as a noisy zone, despite being an educational institution.
Mohammed Hashim Ameen; Huda Jamal Jumaah; Bahareh Kalantar; Naonori Ueda; Alfian Abdul Halin; Abdullah Saeb Tais; Sarah Jamal Jumaah. Evaluation of PM2.5 Particulate Matter and Noise Pollution in Tikrit University Based on GIS and Statistical Modeling. Sustainability 2021, 13, 9571 .
AMA StyleMohammed Hashim Ameen, Huda Jamal Jumaah, Bahareh Kalantar, Naonori Ueda, Alfian Abdul Halin, Abdullah Saeb Tais, Sarah Jamal Jumaah. Evaluation of PM2.5 Particulate Matter and Noise Pollution in Tikrit University Based on GIS and Statistical Modeling. Sustainability. 2021; 13 (17):9571.
Chicago/Turabian StyleMohammed Hashim Ameen; Huda Jamal Jumaah; Bahareh Kalantar; Naonori Ueda; Alfian Abdul Halin; Abdullah Saeb Tais; Sarah Jamal Jumaah. 2021. "Evaluation of PM2.5 Particulate Matter and Noise Pollution in Tikrit University Based on GIS and Statistical Modeling." Sustainability 13, no. 17: 9571.
Landslide susceptibility modeling, an essential approach to mitigate natural disasters, has witnessed considerable improvement following advances in machine learning (ML) techniques. However, in most of the previous studies, the distribution of input data was assumed as being, and treated, as normal or Gaussian; this assumption is not always valid as ML is heavily dependent on the quality of the input data. Therefore, we examine the effectiveness of six feature transformations (minimax normalization (Std-X), logarithmic functions (Log-X), reciprocal function (Rec-X), power functions (Power-X), optimal features (Opt-X), and one-hot encoding (Ohe-X) over the 11conditioning factors (i.e., altitude, slope, aspect, curvature, distance to road, distance to lineament, distance to stream, terrain roughness index (TRI), normalized difference vegetation index (NDVI), land use, and vegetation density). We selected the frequent landslide-prone area in the Cameron Highlands in Malaysia as a case study to test this novel approach. These transformations were then assessed by three benchmark ML methods, namely extreme gradient boosting (XGB), logistic regression (LR), and artificial neural networks (ANN). The 10-fold cross-validation method was used for model evaluations. Our results suggest that using Ohe-X transformation over the ANN model considerably improved performance from 52.244 to 89.398 (37.154% improvement).
Husam A. H. Al-Najjar; Biswajeet Pradhan; Bahareh Kalantar; Maher Ibrahim Sameen; M. Santosh; Abdullah Alamri. Landslide Susceptibility Modeling: An Integrated Novel Method Based on Machine Learning Feature Transformation. Remote Sensing 2021, 13, 3281 .
AMA StyleHusam A. H. Al-Najjar, Biswajeet Pradhan, Bahareh Kalantar, Maher Ibrahim Sameen, M. Santosh, Abdullah Alamri. Landslide Susceptibility Modeling: An Integrated Novel Method Based on Machine Learning Feature Transformation. Remote Sensing. 2021; 13 (16):3281.
Chicago/Turabian StyleHusam A. H. Al-Najjar; Biswajeet Pradhan; Bahareh Kalantar; Maher Ibrahim Sameen; M. Santosh; Abdullah Alamri. 2021. "Landslide Susceptibility Modeling: An Integrated Novel Method Based on Machine Learning Feature Transformation." Remote Sensing 13, no. 16: 3281.
This paper proposes a UAV-based PM2.5 air quality and temperature-humidity monitoring system. The system includes an air quality detector comprising four Arduino sensor modules. Specifically, it includes a dust (DSM501A) sensor and a temperature and humidity (DHT11) sensor. The NEO-6M GPS module and DS3231 real-time module are also included for input visualization. A DIY SD card logging shield and memory module is also available for data recording purposes. The Arduino-based board houses multiple sensors and all are programmable using the Arduino integrated development environment (IDE) coding tool. Measurements conducted in a vertical flight path show promise where comparisons with ground truth references data showed good similarity. Overall, the results point to the idea that a light-weight and portable system can be used for accurate and reliable remote sensing data collection (in this case, PM2.5 concentration data and environmental data).
Huda Jumaah; Bahareh Kalantar; Alfian Halin; Shattri Mansor; Naonori Ueda; Sarah Jumaah. Development of UAV-Based PM2.5 Monitoring System. Drones 2021, 5, 60 .
AMA StyleHuda Jumaah, Bahareh Kalantar, Alfian Halin, Shattri Mansor, Naonori Ueda, Sarah Jumaah. Development of UAV-Based PM2.5 Monitoring System. Drones. 2021; 5 (3):60.
Chicago/Turabian StyleHuda Jumaah; Bahareh Kalantar; Alfian Halin; Shattri Mansor; Naonori Ueda; Sarah Jumaah. 2021. "Development of UAV-Based PM2.5 Monitoring System." Drones 5, no. 3: 60.
Large damages and losses resulting from floods are widely reported across the globe. Thus, the identification of the flood-prone zones on a flood susceptibility map is very essential. To do so, 13 conditioning factors influencing the flood occurrence in Brisbane river catchment in Australia (i.e., topographic, water-related, geological, and land use factors) were acquired for further processing and modeling. In this study, artificial neural networks (ANN), deep learning neural networks (DLNN), and optimized DLNN using particle swarm optimization (PSO) were exploited to predict and estimate the susceptible areas to the future floods. The significance of the conditioning factors analysis for the region highlighted that altitude, distance from river, sediment transport index (STI), and slope played the most important roles, whereas stream power index (SPI) did not contribute to the hazardous situation. The performance of the models was evaluated against the statistical tests such as sensitivity, specificity, the area under curve (AUC), and true skill statistic (TSS). DLNN and PSO-DLNN models obtained the highest values of sensitivity (0.99) for the training stage to compare with ANN. Moreover, the validations of specificity and TSS for PSO-DLNN recorded the highest values of 0.98 and 0.90, respectively, compared with those obtained by ANN and DLNN. The best accuracies by AUC were evaluated in PSO-DLNN (0.99 in training and 0.98 in testing datasets), followed by DLNN and ANN. Therefore, the optimized PSO-DLNN proved its robustness to compare with other methods.
Bahareh Kalantar; Naonori Ueda; Vahideh Saeidi; Saeid Janizadeh; Fariborz Shabani; Kourosh Ahmadi; Farzin Shabani. Deep Neural Network Utilizing Remote Sensing Datasets for Flood Hazard Susceptibility Mapping in Brisbane, Australia. Remote Sensing 2021, 13, 2638 .
AMA StyleBahareh Kalantar, Naonori Ueda, Vahideh Saeidi, Saeid Janizadeh, Fariborz Shabani, Kourosh Ahmadi, Farzin Shabani. Deep Neural Network Utilizing Remote Sensing Datasets for Flood Hazard Susceptibility Mapping in Brisbane, Australia. Remote Sensing. 2021; 13 (13):2638.
Chicago/Turabian StyleBahareh Kalantar; Naonori Ueda; Vahideh Saeidi; Saeid Janizadeh; Fariborz Shabani; Kourosh Ahmadi; Farzin Shabani. 2021. "Deep Neural Network Utilizing Remote Sensing Datasets for Flood Hazard Susceptibility Mapping in Brisbane, Australia." Remote Sensing 13, no. 13: 2638.
The survival of humanity is dependent on the survival of forests and the ecosystems they support, yet annually wildfires destroy millions of hectares of global forestry. Wildfires take place under specific conditions and in certain regions, which can be studied through appropriate techniques. A variety of statistical modeling methods have been assessed by researchers; however, ensemble modeling of wildfire susceptibility has not been undertaken. We hypothesize that ensemble modeling of wildfire susceptibility is better than a single modeling technique. This study models the occurrence of wildfire in the Brisbane Catchment of Australia, which is an annual event, using the index of entropy (IoE), evidential belief function (EBF), and logistic regression (LR) ensemble techniques. As a secondary goal of this research, the spatial distribution of the wildfire risk from different aspects such as urbanization and ecosystem was evaluated. The highest accuracy (88.51%) was achieved using the ensemble EBF and LR model. The outcomes of this study may be helpful to particular groups such as planners to avoid susceptible and risky regions in their planning; model builders to replace the traditional individual methods with ensemble algorithms; and geospatial users to enhance their knowledge of geographic information system (GIS) applications.
Mahyat Shafapour Tehrany; Haluk Özener; Bahareh Kalantar; Naonori Ueda; Mohammad Reza Habibi; Fariborz Shabani; Vahideh Saeidi; Farzin Shabani. Application of an Ensemble Statistical Approach in Spatial Predictions of Bushfire Probability and Risk Mapping. Journal of Sensors 2021, 2021, 1 -31.
AMA StyleMahyat Shafapour Tehrany, Haluk Özener, Bahareh Kalantar, Naonori Ueda, Mohammad Reza Habibi, Fariborz Shabani, Vahideh Saeidi, Farzin Shabani. Application of an Ensemble Statistical Approach in Spatial Predictions of Bushfire Probability and Risk Mapping. Journal of Sensors. 2021; 2021 ():1-31.
Chicago/Turabian StyleMahyat Shafapour Tehrany; Haluk Özener; Bahareh Kalantar; Naonori Ueda; Mohammad Reza Habibi; Fariborz Shabani; Vahideh Saeidi; Farzin Shabani. 2021. "Application of an Ensemble Statistical Approach in Spatial Predictions of Bushfire Probability and Risk Mapping." Journal of Sensors 2021, no. : 1-31.
Particulate matter (PM2.5) concentrations are a serious human health concern and global models are the common methods for PM2.5 particle estimation disregarding the local changes and factors. In this study, a polynomial model for PM2.5 particles prediction was proposed to examine the correlations among PM2.5, PM10, and meteorological parameters. The study was carried out in the north of Iraq including two provinces; Kirkuk and Sulaymaniyah. The data gathered from different sources. Two datasets have been used, collected during July 2019 and February 2020. To test our methodology, the model was applied on a small subset of the study area (5.6 km2) inside the Kirkuk province. Datasets (observation and ground truth) were utilized to examine the model. Based on the July 2019 dataset, the mean local R2 values were estimated at 0.98 and 0.97 in the north part of Iraq, and inside the Kirkuk province (the small subset), respectively. While based on the February 2020 dataset, the mean local R2 values were estimated at 0.98 inside the Kirkuk province. High values of prediction accuracies were obtained by 82% and 96% in July and February, respectively. Moreover, our findings highlighted that the health impacts and air quality varied from moderate to unhealthy in the region.
Hussein Habeeb Hamed; Huda Jamal Jumaah; Bahareh Kalantar; Naonori Ueda; Vahideh Saeidi; Shattri Mansor; Zainab Ali Khalaf. Predicting PM2.5 levels over the north of Iraq using regression analysis and geographical information system (GIS) techniques. Geomatics, Natural Hazards and Risk 2021, 12, 1778 -1796.
AMA StyleHussein Habeeb Hamed, Huda Jamal Jumaah, Bahareh Kalantar, Naonori Ueda, Vahideh Saeidi, Shattri Mansor, Zainab Ali Khalaf. Predicting PM2.5 levels over the north of Iraq using regression analysis and geographical information system (GIS) techniques. Geomatics, Natural Hazards and Risk. 2021; 12 (1):1778-1796.
Chicago/Turabian StyleHussein Habeeb Hamed; Huda Jamal Jumaah; Bahareh Kalantar; Naonori Ueda; Vahideh Saeidi; Shattri Mansor; Zainab Ali Khalaf. 2021. "Predicting PM2.5 levels over the north of Iraq using regression analysis and geographical information system (GIS) techniques." Geomatics, Natural Hazards and Risk 12, no. 1: 1778-1796.
Landslide hazard assessment followed by susceptibility map is the primary purposes of predicting the landslides and reducing the risk of landslide occurrences. Such information which can be obtained through detailed analyses of landslide susceptible area is a significant step in developing adequate models in landslide prone zones. In this research, three methods of machine learning, namely Generalized Linear Model (GLM), Boosted Regression Trees (BRT), Support Vector Machine (SVM), and their ensemble model were applied in a clipped region of Sajadrood, Iran. The evaluation of spatial correlations between 14 landslide conditioning factors and classifying their importance have been done, as well to identify the most important cause of landslide in the area. Eventually, accuracy assessment has been carried out using the area under the curve (AUC), correlation coefficient (COR), and total sum of squares (TSS). The results of AUC showed that GLM produced the highest prediction accuracy, with the value of 0.86, followed by SVM (0.84), ensemble model (0.84) and BRT (0.83). Consequently, Stream Power Index (SPI) was the least important factor for landslide prediction. The results also showed that curvatures and distance to road had the most significant effect on the occurrence of landslide in Sajarood.
Bahareh Kalantar; Naonori Ueda; Vahideh Saeidi; Parisa Ahmadi. Application of Machine Learning Algorithms and Their Ensemble for Landslide Susceptibility Mapping. Understanding and Reducing Landslide Disaster Risk 2020, 233 -239.
AMA StyleBahareh Kalantar, Naonori Ueda, Vahideh Saeidi, Parisa Ahmadi. Application of Machine Learning Algorithms and Their Ensemble for Landslide Susceptibility Mapping. Understanding and Reducing Landslide Disaster Risk. 2020; ():233-239.
Chicago/Turabian StyleBahareh Kalantar; Naonori Ueda; Vahideh Saeidi; Parisa Ahmadi. 2020. "Application of Machine Learning Algorithms and Their Ensemble for Landslide Susceptibility Mapping." Understanding and Reducing Landslide Disaster Risk , no. : 233-239.
Large scale developmental projects firstly require the selection of one or more cities to be developed. In Libya, the selection process is done by selected organizations, which is highly influenced by human judgement that can be inconsiderate of socioeconomic and environmental factors. In this study, we propose an automated selection process, which takes into consideration only the important factors for city (cities) selection. Specifically, a geospatial decision-making tool, free of human bias, is proposed based on the fuzzy overlay (FO) and technique for order performance by similarity to ideal solution (TOPSIS) techniques for development projects in Libya. In this work, a dataset of 17 evaluation criteria (GIS factors) across five urban conditioning factors were prepared. The dataset served as input to the FO model to calculate weights (importance) for each criterion. A support vector machine (SVM) classifier was then trained to refine weights from the FO model. TOPSIS was then applied on the refined results to rank the cities for development. Experimental results indicate promising overall accuracy and kappa statistics. Our findings also show that highest and lowest success rates are 0.94 and 0.79, respectively, while highest and lowest prediction rates are 0.884 and 0.673, respectively.
Bahareh Kalantar; Husam A.H. Al-Najjar; Hossein Mojaddadi Rizeei; Maruwan S.A.B. Amazeeq; Mohammed Oludare Idrees; Alfian Abdul Halin; Shattri Mansor. Urban Planning Using a Geospatial Approach: A Case Study of Libya. Sustainability in Urban Planning and Design 2020, 1 .
AMA StyleBahareh Kalantar, Husam A.H. Al-Najjar, Hossein Mojaddadi Rizeei, Maruwan S.A.B. Amazeeq, Mohammed Oludare Idrees, Alfian Abdul Halin, Shattri Mansor. Urban Planning Using a Geospatial Approach: A Case Study of Libya. Sustainability in Urban Planning and Design. 2020; ():1.
Chicago/Turabian StyleBahareh Kalantar; Husam A.H. Al-Najjar; Hossein Mojaddadi Rizeei; Maruwan S.A.B. Amazeeq; Mohammed Oludare Idrees; Alfian Abdul Halin; Shattri Mansor. 2020. "Urban Planning Using a Geospatial Approach: A Case Study of Libya." Sustainability in Urban Planning and Design , no. : 1.
This study predicts forest fire susceptibility in Chaloos Rood watershed in Iran using three machine learning (ML) models—multivariate adaptive regression splines (MARS), support vector machine (SVM), and boosted regression tree (BRT). The study utilizes 14 set of fire predictors derived from vegetation indices, climatic variables, environmental factors, and topographical features. To assess the suitability of the models and estimating the variance and bias of estimation, the training dataset obtained from the Natural Resources Directorate of Mazandaran province was subjected to resampling using cross validation (CV), bootstrap, and optimism bootstrap techniques. Using variance inflation factor (VIF), weight indicating the strength of the spatial relationship of the predictors to fire occurrence was assigned to each contributing variable. Subsequently, the models were trained and validated using the receiver operating characteristics (ROC) area under the curve (AUC) curve. Results of the model validation based on the resampling techniques (none, 5- and 10-fold, bootstrap and optimism bootstrap) produced AUC values of 0.78, 0.88, 0.90, 0.86 and 0.83 for the MARS model; 0.82, 0.82, 0.89, 0.87, 0.84 for the SVM and 0.87, 0.90, 0.90, 0.90, 0.91 for the BRT model. Across the individual model, the 10-fold CV performed best in MARS and SVM with AUC values of 0.90 and 0.89. Overall, the BRT outperformed the other models in all ramification with highest AUC value of 0.91 using optimism bootstrap resampling algorithm. Generally, the resampling process enhanced the prediction performance of all the models.
Bahareh Kalantar; Naonori Ueda; Mohammed Idrees; Saeid Janizadeh; Kourosh Ahmadi; Farzin Shabani. Forest Fire Susceptibility Prediction Based on Machine Learning Models with Resampling Algorithms on Remote Sensing Data. Remote Sensing 2020, 12, 3682 .
AMA StyleBahareh Kalantar, Naonori Ueda, Mohammed Idrees, Saeid Janizadeh, Kourosh Ahmadi, Farzin Shabani. Forest Fire Susceptibility Prediction Based on Machine Learning Models with Resampling Algorithms on Remote Sensing Data. Remote Sensing. 2020; 12 (22):3682.
Chicago/Turabian StyleBahareh Kalantar; Naonori Ueda; Mohammed Idrees; Saeid Janizadeh; Kourosh Ahmadi; Farzin Shabani. 2020. "Forest Fire Susceptibility Prediction Based on Machine Learning Models with Resampling Algorithms on Remote Sensing Data." Remote Sensing 12, no. 22: 3682.
In recent years, remote-sensing (RS) technologies have been used together with image processing and traditional techniques in various disaster-related works. Among these is detecting building damage from orthophoto imagery that was inflicted by earthquakes. Automatic and visual techniques are considered as typical methods to produce building damage maps using RS images. The visual technique, however, is time-consuming due to manual sampling. The automatic method is able to detect the damaged building by extracting the defect features. However, various design methods and widely changing real-world conditions, such as shadow and light changes, cause challenges to the extensive appointing of automatic methods. As a potential solution for such challenges, this research proposes the adaption of deep learning (DL), specifically convolutional neural networks (CNN), which has a high ability to learn features automatically, to identify damaged buildings from pre- and post-event RS imageries. Since RS data revolves around imagery, CNNs can arguably be most effective at automatically discovering relevant features, avoiding the need for feature engineering based on expert knowledge. In this work, we focus on RS imageries from orthophoto imageries for damaged-building detection, specifically for (i) background, (ii) no damage, (iii) minor damage, and (iv) debris classifications. The gist is to uncover the CNN architecture that will work best for this purpose. To this end, three CNN models, namely the twin model, fusion model, and composite model, are applied to the pre- and post-orthophoto imageries collected from the 2016 Kumamoto earthquake, Japan. The robustness of the models was evaluated using four evaluation metrics, namely overall accuracy (OA), producer accuracy (PA), user accuracy (UA), and F1 score. According to the obtained results, the twin model achieved higher accuracy (OA = 76.86%; F1 score = 0.761) compare to the fusion model (OA = 72.27%; F1 score = 0.714) and composite (OA = 69.24%; F1 score = 0.682) models.
Bahareh Kalantar; Naonori Ueda; Husam A. H. Al-Najjar; Alfian Abdul Halin. Assessment of Convolutional Neural Network Architectures for Earthquake-Induced Building Damage Detection based on Pre- and Post-Event Orthophoto Images. Remote Sensing 2020, 12, 3529 .
AMA StyleBahareh Kalantar, Naonori Ueda, Husam A. H. Al-Najjar, Alfian Abdul Halin. Assessment of Convolutional Neural Network Architectures for Earthquake-Induced Building Damage Detection based on Pre- and Post-Event Orthophoto Images. Remote Sensing. 2020; 12 (21):3529.
Chicago/Turabian StyleBahareh Kalantar; Naonori Ueda; Husam A. H. Al-Najjar; Alfian Abdul Halin. 2020. "Assessment of Convolutional Neural Network Architectures for Earthquake-Induced Building Damage Detection based on Pre- and Post-Event Orthophoto Images." Remote Sensing 12, no. 21: 3529.
The estimation and mapping of forest stand characteristics are vital because this information is necessary for sustainable forest management. The present study considers the use of a Bayesian additive regression trees (BART) algorithm as a non-parametric classifier using Sentinel-2A data and topographic variables to estimate the forest stand characteristics, namely the basal area (m2/ha), stem volume (m3/ha), and stem density (number/ha). These results were compared with those of three other popular machine learning (ML) algorithms, such as generalised linear model (GLM), K-nearest neighbours (KNN), and support vector machine (SVM). A feature selection was done on 28 variables including the multi-spectral bands on Sentinel-2 satellite, related vegetation indices, and ancillary data (elevation, slope, and topographic solar-radiation index derived from digital elevation model (DEM)) and then the most insignificant variables were removed from the datasets by recursive feature elimination (RFE). The study area was a mountainous forest with high biodiversity and an elevation gradient from 26 to 1636 m. An inventory dataset of 1200 sample plots was provided for training and testing the algorithms, and the predictors were fed into the ML models to compute and predict the forest stand characteristics. The accuracies and certainties of the ML models were assessed by their root mean square error (RMSE), mean absolute error (MAE), and R-squared (R2) values. The results demonstrated that BART generated the best basal area and stem volume predictions, followed by GLM, SVM, and KNN. The best RMSE values for both basal area (8.12 m2/ha) and stem volume (29.28 m3/ha) estimation were obtained by BART. Thus, the ability of the BART model for forestry application was established. On the other hand, KNN exhibited the highest RMSE values for all stand variable predictions, thereby exhibiting the least accuracy for this specific application. Moreover, the effectiveness of the narrow Sentinel-2 bands around the red edge and elevation was highlighted for predicting the forest stand characteristics. Therefore, we concluded that the combination of the Sentinel-2 products and topographic variables derived from the PALSAR data used in this study improved the estimation of the forest attributes in temperate forests.
Kourosh Ahmadi; Bahareh Kalantar; Vahideh Saeidi; Elaheh K. G. Harandi; Saeid Janizadeh; Naonori Ueda. Comparison of Machine Learning Methods for Mapping the Stand Characteristics of Temperate Forests Using Multi-Spectral Sentinel-2 Data. Remote Sensing 2020, 12, 3019 .
AMA StyleKourosh Ahmadi, Bahareh Kalantar, Vahideh Saeidi, Elaheh K. G. Harandi, Saeid Janizadeh, Naonori Ueda. Comparison of Machine Learning Methods for Mapping the Stand Characteristics of Temperate Forests Using Multi-Spectral Sentinel-2 Data. Remote Sensing. 2020; 12 (18):3019.
Chicago/Turabian StyleKourosh Ahmadi; Bahareh Kalantar; Vahideh Saeidi; Elaheh K. G. Harandi; Saeid Janizadeh; Naonori Ueda. 2020. "Comparison of Machine Learning Methods for Mapping the Stand Characteristics of Temperate Forests Using Multi-Spectral Sentinel-2 Data." Remote Sensing 12, no. 18: 3019.
Predicting landslide occurrences can be difficult. However, failure to do so can be catastrophic, causing unwanted tragedies such as property damage, community displacement, and human casualties. Research into landslide susceptibility mapping (LSM) attempts to alleviate such catastrophes through the identification of landslide prone areas. Computational modelling techniques have been successful in related disaster scenarios, which motivate this work to explore such modelling for LSM. In this research, the potential of supervised machine learning and ensemble learning is investigated. Firstly, the Flexible Discriminant Analysis (FDA) supervised learning algorithm is trained for LSM and compared against other algorithms that have been widely used for the same purpose, namely Generalized Logistic Models (GLM), Boosted Regression Trees (BRT or GBM), and Random Forest (RF). Next, an ensemble model consisting of all four algorithms is implemented to examine possible performance improvements. The dataset used to train and test all the algorithms consists of a landslide inventory map of 227 landslide locations. From these sources, 13 conditioning factors are extracted to be used in the models. Experimental evaluations are made based on True Skill Statistic (TSS), the Receiver Operation characteristic (ROC) curve and kappa index. The results show that the best TSS (0.6986), ROC (0.904) and kappa (0.6915) were obtained by the ensemble model. FDA on its own seems effective at modelling landslide susceptibility from multiple data sources, with performance comparable to GLM. However, it slightly underperforms when compared to GBM (BRT) and RF. RF seems most capable compared to GBM, GLM, and FDA, when dealing with all conditioning factors.
Bahareh Kalantar; Naonori Ueda; Vahideh Saeidi; Kourosh Ahmadi; Alfian Abdul Halin; Farzin Shabani. Landslide Susceptibility Mapping: Machine and Ensemble Learning Based on Remote Sensing Big Data. Remote Sensing 2020, 12, 1737 .
AMA StyleBahareh Kalantar, Naonori Ueda, Vahideh Saeidi, Kourosh Ahmadi, Alfian Abdul Halin, Farzin Shabani. Landslide Susceptibility Mapping: Machine and Ensemble Learning Based on Remote Sensing Big Data. Remote Sensing. 2020; 12 (11):1737.
Chicago/Turabian StyleBahareh Kalantar; Naonori Ueda; Vahideh Saeidi; Kourosh Ahmadi; Alfian Abdul Halin; Farzin Shabani. 2020. "Landslide Susceptibility Mapping: Machine and Ensemble Learning Based on Remote Sensing Big Data." Remote Sensing 12, no. 11: 1737.
Invasive weed species (IWS) threaten ecosystems, the distribution of specific plant species, as well as agricultural productivity. Predicting the impact of climate change on the current and future distributions of these unwanted species forms an important category of ecological research. Our study investigated 32 globally important IWS to assess whether climate alteration may lead to spatial changes in the overlapping of specific IWS globally. We utilized the versatile species distribution model MaxEnt, coupled with Geographic Information Systems, to evaluate the potential alterations (gain/loss/static) in the number of potential ecoregion invasions by IWS, under four Representative Concentration Pathways, which differ in terms of predicted year of peak greenhouse gas emission. We based our projection on a forecast of climatic variables (extracted from WorldClim) from two global circulation models (CCSM4 and MIROC-ESM). Initially, we modeled current climatic suitability of habitat, individually for each of the 32 IWS, identifying those with a common spatial range of suitability. Thereafter, we modeled the suitability of all 32 species under the projected climate for 2050, incorporating each of the four Representative Concentration Pathways (2.6, 4.5, 6.0, and 8.5) in separate models, again examining the common spatial overlaps. The discrimination capacity and accuracy of the model were assessed for all 32 IWS individually, using the area under the curve and true skill statistic rate, with results averaging 0.87 and 0.75 respectively, indicating a high level of accuracy. Our final methodological step compared the extent of the overlaps and alterations under the current and future projected climates. Our results mainly predicted decrease on a global scale, in areas of habitat suitable for most IWS, under future climatic conditions, excluding European countries, northern Brazil, eastern US, and south-eastern Australia. The following should be considered when interpreting these results: there are many inherent assumptions and limitations in presence-only data of this type, as well as with the modeling techniques projecting climate conditions, and the envelopes themselves, such as scale and resolution mismatches, dispersal barriers, lack of documentation on potential disturbances, and unknown or unforeseen biotic interactions.
Farzin Shabani; Mohsen Ahmadi; Lalit Kumar; Samaneh Solhjouy-Fard; Mahyat Shafapour Tehrany; Fariborz Shabani; Bahareh Kalantar; Atefeh Esmaeili. Invasive weed species’ threats to global biodiversity: Future scenarios of changes in the number of invasive species in a changing climate. Ecological Indicators 2020, 116, 106436 .
AMA StyleFarzin Shabani, Mohsen Ahmadi, Lalit Kumar, Samaneh Solhjouy-Fard, Mahyat Shafapour Tehrany, Fariborz Shabani, Bahareh Kalantar, Atefeh Esmaeili. Invasive weed species’ threats to global biodiversity: Future scenarios of changes in the number of invasive species in a changing climate. Ecological Indicators. 2020; 116 ():106436.
Chicago/Turabian StyleFarzin Shabani; Mohsen Ahmadi; Lalit Kumar; Samaneh Solhjouy-Fard; Mahyat Shafapour Tehrany; Fariborz Shabani; Bahareh Kalantar; Atefeh Esmaeili. 2020. "Invasive weed species’ threats to global biodiversity: Future scenarios of changes in the number of invasive species in a changing climate." Ecological Indicators 116, no. : 106436.
Considering the high-level details in an ultrahigh-spatial-resolution (UHSR) unmanned aerial vehicle (UAV) dataset, detailed mapping of heterogeneous urban landscapes is extremely challenging because of the spectral similarity between classes. In this study, adaptive hierarchical image segmentation optimization, multilevel feature selection, and multiscale (MS) supervised machine learning (ML) models were integrated to accurately generate detailed maps for heterogeneous urban areas from the fusion of the UHSR orthomosaic and digital surface model (DSM). The integrated approach commenced through a preliminary MS image segmentation parameter selection, followed by the application of three supervised ML models, namely, random forest (RF), support vector machine (SVM), and decision tree (DT). These models were implemented at the optimal MS levels to identify preliminary information, such as the optimal segmentation level(s) and relevant features, for extracting 12 land use/land cover (LULC) urban classes from the fused datasets. Using the information obtained from the first phase of the analysis, detailed MS classification was iteratively conducted to improve the classification accuracy and derive the final urban LULC maps. Two UAV-based datasets were used to develop and assess the effectiveness of the proposed framework. The hierarchical classification of the pilot study area showed that the RF was superior with an overall accuracy (OA) of 94.40% and a kappa coefficient (K) of 0.938, followed by SVM (OA = 92.50% and K = 0.917) and DT (OA = 91.60% and K = 0.908). The classification results of the second dataset revealed that SVM was superior with an OA of 94.45% and K of 0.938, followed by RF (OA = 92.46% and K = 0.916) and DT (OA = 90.46% and K = 0.893). The proposed framework exhibited an excellent potential for the detailed mapping of heterogeneous urban landscapes from the fusion of UHSR orthophoto and DSM images using various ML models.
Mohamed Barakat A. Gibril; Bahareh Kalantar; Rami Al-Ruzouq; Naonori Ueda; Vahideh Saeidi; Abdallah Shanableh; Shattri Mansor; Helmi Z. M. Shafri. Mapping Heterogeneous Urban Landscapes from the Fusion of Digital Surface Model and Unmanned Aerial Vehicle-Based Images Using Adaptive Multiscale Image Segmentation and Classification. Remote Sensing 2020, 12, 1081 .
AMA StyleMohamed Barakat A. Gibril, Bahareh Kalantar, Rami Al-Ruzouq, Naonori Ueda, Vahideh Saeidi, Abdallah Shanableh, Shattri Mansor, Helmi Z. M. Shafri. Mapping Heterogeneous Urban Landscapes from the Fusion of Digital Surface Model and Unmanned Aerial Vehicle-Based Images Using Adaptive Multiscale Image Segmentation and Classification. Remote Sensing. 2020; 12 (7):1081.
Chicago/Turabian StyleMohamed Barakat A. Gibril; Bahareh Kalantar; Rami Al-Ruzouq; Naonori Ueda; Vahideh Saeidi; Abdallah Shanableh; Shattri Mansor; Helmi Z. M. Shafri. 2020. "Mapping Heterogeneous Urban Landscapes from the Fusion of Digital Surface Model and Unmanned Aerial Vehicle-Based Images Using Adaptive Multiscale Image Segmentation and Classification." Remote Sensing 12, no. 7: 1081.
Two novel hybrid predictors are suggested as the combination of artificial neural network (ANN), coupled with spotted hyena optimizer (SHO) and ant lion optimization (ALO) metaheuristic techniques, to simulate soil shear strength (SSS). These algorithms were applied to the ANN for counteracting the computational drawbacks of this model. As a function of ten key factors of the soil (including depth of the sample, percentage of sand, percentage of loam, percentage of clay, percentage of moisture content, wet density, liquid limit, plastic limit, plastic Index, and liquidity index), the SSS was considered as the response variable. Followed by development of the ALO–ANN and SHO–ANN ensembles, the best-fitted structures were determined by a trial and error process. The results demonstrated the efficiency of both applied algorithms, as the prediction error of the ANN was reduced by around 35% and 18% by the ALO and SHO, respectively. A comparison between the results revealed that the ALO–ANN (Error = 0.0619 and Correlation = 0.9348) performs more efficiently than the SHO–ANN (Error = 0.0874 and Correlation = 0.8866). Finally, an SSS predictive formula is presented for use as an alternative to the difficult traditional methods.
Hossein Moayedi; Dieu Tien Bui; Dounis Anastasios; Bahareh Kalantar; Bui. Spotted Hyena Optimizer and Ant Lion Optimization in Predicting the Shear Strength of Soil. Applied Sciences 2019, 9, 4738 .
AMA StyleHossein Moayedi, Dieu Tien Bui, Dounis Anastasios, Bahareh Kalantar, Bui. Spotted Hyena Optimizer and Ant Lion Optimization in Predicting the Shear Strength of Soil. Applied Sciences. 2019; 9 (22):4738.
Chicago/Turabian StyleHossein Moayedi; Dieu Tien Bui; Dounis Anastasios; Bahareh Kalantar; Bui. 2019. "Spotted Hyena Optimizer and Ant Lion Optimization in Predicting the Shear Strength of Soil." Applied Sciences 9, no. 22: 4738.
This paper focuses on the prediction of soil shear strength (SSS), which is one of the most fundamental parameters in geotechnical engineering. Consisting of 12 influential factors, namely depth of sample, percentage of sand, percentage of loam, percentage of clay, percentage of moisture content, wet density, dry density, void ratio, liquid limit, plastic limit, plastic Index, and liquidity index as input variables, as well as the shear strength as the desired output, the dataset is provided through a field survey in Vietnam. Thereafter, as for used intelligent techniques, the main focus of the current study is on evaluating the efficiency of three novel optimization techniques for optimizing an artificial neural network (ANN) in predicting the SSS. To this end, the dragonfly algorithm (DA), whale optimization algorithm (WOA), and invasive weed optimization (IWO) are synthesized with ANN to prevail its computational drawbacks. The complexity of the models is optimized by sensitivity analysis. The results confirmed the effectiveness of all three applied algorithms, as the learning error was reduced by nearly 17%, 27%, and 32%, respectively by functioning the DA, WOA, and IWO. As for the testing phase, the IWO and DA achieved a close prediction accuracy. Overall, due to the superiority of the IWO-ANN ensemble, this model could be a promising alternative to traditional methods of shear strength determination.
Hossein Moayedi; Dieu Tien Bui; Anastasios Dounis; Loke Kok Foong; Bahareh Kalantar. Novel Nature-Inspired Hybrids of Neural Computing for Estimating Soil Shear Strength. Applied Sciences 2019, 9, 4643 .
AMA StyleHossein Moayedi, Dieu Tien Bui, Anastasios Dounis, Loke Kok Foong, Bahareh Kalantar. Novel Nature-Inspired Hybrids of Neural Computing for Estimating Soil Shear Strength. Applied Sciences. 2019; 9 (21):4643.
Chicago/Turabian StyleHossein Moayedi; Dieu Tien Bui; Anastasios Dounis; Loke Kok Foong; Bahareh Kalantar. 2019. "Novel Nature-Inspired Hybrids of Neural Computing for Estimating Soil Shear Strength." Applied Sciences 9, no. 21: 4643.
In this paper, the authors investigated the applicability of combining machine-learning-based models toward slope stability assessment. To do this, several well-known machine-learning-based methods, namely multiple linear regression (MLR), multi-layer perceptron (MLP), radial basis function regression (RBFR), improved support vector machine using sequential minimal optimization algorithm (SMO-SVM), lazy k-nearest neighbor (IBK), random forest (RF), and random tree (RT), were selected to evaluate the stability of a slope through estimating the factor of safety (FOS). In the following, a comparative classification was carried out based on the five stability categories. Based on the respective values of total scores (the summation of scores obtained for the training and testing stages) of 15, 35, 48, 15, 50, 60, and 57, acquired for MLR, MLP, RBFR, SMO-SVM, IBK, RF, and RT, respectively, it was concluded that RF outperformed other intelligent models. The results of statistical indexes also prove the excellent prediction from the optimized structure of the ANN and RF techniques.
Hossein Moayedi; Dieu Tien Bui; Bahareh Kalantar; Loke Kok Foong. Machine-Learning-Based Classification Approaches toward Recognizing Slope Stability Failure. Applied Sciences 2019, 9, 4638 .
AMA StyleHossein Moayedi, Dieu Tien Bui, Bahareh Kalantar, Loke Kok Foong. Machine-Learning-Based Classification Approaches toward Recognizing Slope Stability Failure. Applied Sciences. 2019; 9 (21):4638.
Chicago/Turabian StyleHossein Moayedi; Dieu Tien Bui; Bahareh Kalantar; Loke Kok Foong. 2019. "Machine-Learning-Based Classification Approaches toward Recognizing Slope Stability Failure." Applied Sciences 9, no. 21: 4638.
In the present work, we employed artificial neural network (ANN) that is optimized with two hybrid models, namely imperialist competition algorithm (ICA) as well as particle swarm optimization (PSO) in the case of the problem of bearing capacity of shallow circular footing systems. Many types of research have shown that ANNs are valuable techniques for estimating the bearing capacity of the soils. However, most ANN training models have some drawbacks. This study aimed to focus on the application of two well-known hybrid ICA–ANN and PSO–ANN models to the estimation of bearing capacity of the circular footing lied in layered soils. In order to provide the training and testing datasets for the predictive network models, extensive finite element (FE) modelling (a database includes 2810 training datasets and 703 testing datasets) are performed on 16 soil layer sets (weaker soil rested on stronger soil and vice versa). Note that all the independent variables of ICA and PSO algorithms are optimized utilizing a trial and error method. The input includes upper layer thickness/foundation width (h/B) ratio, footing width (B), top and bottom soil layer properties (e.g., six of the most critical soil characteristics), vertical settlement of circular footing (s), where the output was taken ultimate bearing capacity of the circular footing (Fult). Based on coefficient of determination (R2) and Root Mean Square Error (RMSE), amounts of (0.979, 0.076) and (0.984, 0.066) predicted for training dataset and amounts of (0.978, 0.075) and (0.983, 0.066) indicated in the case of the testing dataset of proposed PSO–ANN and ICA–ANN models of prediction network, respectively. It demonstrates a higher reliability of the presented PSO–ANN model for predicting ultimate bearing capacity of circular footing located on double sandy layer soils.
Hossein Moayedi; Bahareh Kalantar; Anastasios Dounis; Dieu Tien Bui; Loke Kok Foong. Development of Two Novel Hybrid Prediction Models Estimating Ultimate Bearing Capacity of the Shallow Circular Footing. Applied Sciences 2019, 9, 4594 .
AMA StyleHossein Moayedi, Bahareh Kalantar, Anastasios Dounis, Dieu Tien Bui, Loke Kok Foong. Development of Two Novel Hybrid Prediction Models Estimating Ultimate Bearing Capacity of the Shallow Circular Footing. Applied Sciences. 2019; 9 (21):4594.
Chicago/Turabian StyleHossein Moayedi; Bahareh Kalantar; Anastasios Dounis; Dieu Tien Bui; Loke Kok Foong. 2019. "Development of Two Novel Hybrid Prediction Models Estimating Ultimate Bearing Capacity of the Shallow Circular Footing." Applied Sciences 9, no. 21: 4594.
Slump is a workability-related characteristic of concrete mixture. This paper investigates the efficiency of a novel optimizer, namely ant lion optimization (ALO), for fine-tuning of a neural network (NN) in the field of concrete slump prediction. Two well-known optimization techniques, biogeography-based optimization (BBO) and grasshopper optimization algorithm (GOA), are also considered as benchmark models to be compared with ALO. Considering seven slump effective factors, namely cement, slag, water, fly ash, superplasticizer (SP), fine aggregate (FA), and coarse aggregate (CA), the mentioned algorithms are synthesized with a neural network to determine the best-fitted neural parameters. The most appropriate complexity of each ensemble is also found by a population-based sensitivity analysis. The findings revealed that the proposed ALO-NN model acquires a good approximation of concrete slump, regarding the calculated root mean square error (RMSE = 3.7788) and mean absolute error (MAE = 3.0286). It also outperformed both BBO-NN (RMSE = 4.1859 and MAE = 3.3465) and GOA-NN (RMSE = 4.9553 and MAE = 3.8576) ensembles.
Hossein Moayedi; Bahareh Kalantar; Loke Kok Foong; Dieu Tien Bui; Alireza Motevalli; Tien Bui. Application of Three Metaheuristic Techniques in Simulation of Concrete Slump. Applied Sciences 2019, 9, 4340 .
AMA StyleHossein Moayedi, Bahareh Kalantar, Loke Kok Foong, Dieu Tien Bui, Alireza Motevalli, Tien Bui. Application of Three Metaheuristic Techniques in Simulation of Concrete Slump. Applied Sciences. 2019; 9 (20):4340.
Chicago/Turabian StyleHossein Moayedi; Bahareh Kalantar; Loke Kok Foong; Dieu Tien Bui; Alireza Motevalli; Tien Bui. 2019. "Application of Three Metaheuristic Techniques in Simulation of Concrete Slump." Applied Sciences 9, no. 20: 4340.
This study investigates the effectiveness of using groundwater inventory data for groundwater spring potential mapping in the Haraz watershed located in Norther Iran. From a total of 917 groundwater inventory dataset, six random inventory scenarios of 917, 690, 450, 230, 92, and 46 were generated. We trained two learning classifiers, namely the Support Vector Machine (SVM) and Random Forest (RF) based on each scenario to determine which one(s) would be more suitable for spring potential mapping. In each of the scenarios, 70% of the dataset was used for training whereas 30% was used for testing. The end results (classified maps) for each classifier and their respective dataset were quantitatively assessed based on the Area under Curve (AUC) metric. The prediction accuracies for the spring potential maps being produced for each scenario ranged from 0.693 to 0.736 using the SVM, and 0.608 to 0.895 for RF. Our findings indicate that 46 random points of inventory data did not produce a desirable outcome. On the contrary, more points yield better results, i.e. 450 random points produced the highest ROC when using SVM (0.736) followed by 917 and 690 random points using RF (0.895 and 0.877, respectively).
Bahareh Kalantar; Naonori Ueda; Husam Abdulrasool Hammadeh Al-Najjar; Alfian Abdul Halin; Parisa Ahmadi; Mohamed Barakat Gibril. On the effects of different groundwater inventory scenarios for spring potential mapping in Haraz, northern Iran. Earth Resources and Environmental Remote Sensing/GIS Applications X 2019, 11156, 1115612 .
AMA StyleBahareh Kalantar, Naonori Ueda, Husam Abdulrasool Hammadeh Al-Najjar, Alfian Abdul Halin, Parisa Ahmadi, Mohamed Barakat Gibril. On the effects of different groundwater inventory scenarios for spring potential mapping in Haraz, northern Iran. Earth Resources and Environmental Remote Sensing/GIS Applications X. 2019; 11156 ():1115612.
Chicago/Turabian StyleBahareh Kalantar; Naonori Ueda; Husam Abdulrasool Hammadeh Al-Najjar; Alfian Abdul Halin; Parisa Ahmadi; Mohamed Barakat Gibril. 2019. "On the effects of different groundwater inventory scenarios for spring potential mapping in Haraz, northern Iran." Earth Resources and Environmental Remote Sensing/GIS Applications X 11156, no. : 1115612.