Nawras Shatnawi - Academia.edu (original) (raw)
Papers by Nawras Shatnawi
Resources
Municipal solid waste services in Jordan are being provided by municipalities, where 90% of the g... more Municipal solid waste services in Jordan are being provided by municipalities, where 90% of the generated solid waste finds its way to the landfills and dump sites. All Jordanian municipalities are suffering from low cost recovery where it reaches 50% in its best case. Realizing these facts, recently, the Jordanian government has embarked on an ambitious package of regulations and policies to promote the adoption of circular economy options. The National Solid Waste Management Strategy (NSWMS) of Jordan has called for rationalization, gradual increase of the service fees and adoption of circular economy options through recycling and composting. To assess the impacts of the proposed policies by the NSWMS on the financial sustainability, a system dynamic modeling (SDM) was carried out for the two largest municipalities in the country, namely Greater Amman Municipality (GAM) and Greater Irbid Municipality (GIM). The share of solid waste management cost from the total municipal budget i...
Solid waste is a major issue in all countries around the world. Solid waste classification and se... more Solid waste is a major issue in all countries around the world. Solid waste classification and segregation prior to reuse, recycling or recovery is an important step toward sustainable waste management. Traditional manual sorting of solid waste is a labour intensive process that may pose health risks to the workers. Currently, automated classification of solid waste using machine learning techniques are widely applied. This study is aiming to develop an automated waste classification model by testing traditional and deep machine learning models. To achieve that, both open (Trashnet) and generated datasets were used in the model training and testing. The study results showed relatively low prediction capability of the traditional machine learning models like Random Forest (RF) and Support Vector Machine (SVM) as compared to the deep machine learning Convolutional Neural Network (CNN). The testing of the three models on a combined data set of Trashnet with local garbage data set resul...
International Review of Civil Engineering (IRECE)
The Egyptian Journal of Remote Sensing and Space Science
Decision Science Letters
Fires are devastating risky events in forests, having a negative effect on resources, biodiversit... more Fires are devastating risky events in forests, having a negative effect on resources, biodiversity, economics, animal life, and putting people in danger. The goal of this study is to use geospatial techniques to identify areas in Jordan that are at risk of forest fires. The research area extends 50 kilometers north and 15 kilometers east from the Dead Sea. The forest fire risk zones map was developed using six factors: land cover class, aspect, proximity to settlements, elevation, slope, and proximity to roads. All of the factors have been selected based on their fire sensitivity or capacity to cause fire. In this study, a Turkish model with fuzzy logic and Analytical hierarchy analysis (AHP) was utilized to classify the area into five categories of risk ranging from very low to very high. According to the findings, approximately 12.12% of the study area is classified as very low risk, 25.54 % is classified as medium risk, while 12.84% is classified as very high risk. Over the last ...
مجلة جامعة الحسين بن طلال للبحوث
Transportation Research Record: Journal of the Transportation Research Board
Rutting leads hydroplaning, accidents, poor riding quality, and significant maintenance costs. Th... more Rutting leads hydroplaning, accidents, poor riding quality, and significant maintenance costs. This study assists the development of statistical and Artificial pavement rutting models. The proposed methodology is reliable, time-saving, cost-saving, and comfortable. The suggested technique to anticipate rutting considers traffic volumes, pavement, and geometrical parameters such as lane and shoulder widths. This research modeled 33 main highways' ruts. Most of these roads have serious de-stressing problems with rutted pavement. The developed rutting prediction models demonstrated a medium to high correlation between rut depth and independent variables including annual average daily traffic, truck fleet percentage, pavement thickness, and number of lanes. The correlation coefficients such as R2 were found to be moderate for most of the developed models. The linear models of rutting prediction were statistically significant, with R2 values averaging around 66%, whereas the logistic...
International Journal of Healthcare Management
The Egyptian Journal of Remote Sensing and Space Science
Sustainability: Science, Practice and Policy, 2021
Air Quality, Atmosphere & Health, 2021
This study deals with the simulation and prediction of air pollutants in Irbid city (north of Jor... more This study deals with the simulation and prediction of air pollutants in Irbid city (north of Jordan) before and during the spread of the COVID-19 virus pandemic by using an artificial neural network (ANN). Based on the data obtained from the air quality monitoring station for the year 2019 and the first quarter of the year 2020, it was possible to develop an ANN model to simulate and predict the concentrations of three air pollutants, namely nitrogen dioxide (NO 2), sulfur dioxide (SO 2), and particulate matter with diameter less than 10 μm (PM 10). Several ANN model configurations were tested to select the best model that could predict the concentration of the three air pollutants with meteorological parameters being used as input to the model. The results showed that the concentration of the pollutants during the coronavirus lockdown was declined by various percentages (from 29% for PM 10 to 72% for NO 2) as compared to their concentration before the pandemic period. Furthermore, the developed ANN model could simulate and predict the concentration of the pollutants during the pandemic period with sufficient accuracy as judged by the values of the coefficient of determination and the mean square error. The study results indicate that properly trained and structured ANN can be a useful tool to predict air quality parameters with adequate accuracy.
This study aims to improve the discovery of potential groundwater occurrences by a new combinatio... more This study aims to improve the discovery of potential groundwater occurrences by a new combination of remote sensing and analytical techniques.
Procedia Manufacturing, 2020
Optimization of public transportation network in terms of reducing travel time and providing acce... more Optimization of public transportation network in terms of reducing travel time and providing access to areas currently without sufficient access to the service facility would certainly motivate private car owners to use public transport. The reduction of vehicle numbers on the roads will undoubtedly lead to minimizing traffic congestion and reducing air pollution due to lesser exhaust emissions. This study used Geographic Information System (GIS), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) to model the location of bus stops in Amman city in Jordan to find optimal travel time and serviceability of stops. The GIS modeling provided significant reduction of travel time for streets with redundant bus stops located at irregular distances, e,g. Zahran Street, where travel time was reduced by (23.25%) during off-peak hours and by 19.74% during peak hours, while PSO provided (28.95%) and 39% reductions, respectively. GA also provided significant reduction in travel time (47.96 %) for Zahran Street. For streets with insufficient number of bus stops, travel time went up due to increase in the number of stops, e.g. Al-Quds Street, where travel time increased from 12.25 min per route to 50.71 min due to increase in the number of stops from 6 to 25. In contrast, this increase in travel time significantly reduced the walking distance to the bus stop, from over 2000 m to approximately 400 m. Moreover, demand for bus stops serviceability significantly more than capacity. Average demand per bus stop on Al-Quds street, for example, was 10456 persons with a capacity of 1465.Comparison of the models by applying them to roads not included in the study showed consistent results that further confirmed the reliability of the models. The developed PSO algorithm and GA are easy to use for urban planning, where they can be applied to any existing network or planned development.
Applied Geomatics, 2021
This research work was anticipated to quantify pavement rut depths for one of the main roads in J... more This research work was anticipated to quantify pavement rut depths for one of the main roads in Jordan. New techniques using mobile and terrestrial laser scanning systems were used in order to detect, assess, and evaluate the surface measured rutting values. A study area located to the south of Amman, the capital city of Jordan, was used for data collection purpose. Accuracy assessment was carried out with reference to ground measurements using differential global position system (GPS). GPS static measurements were used to have accurate and precise rutting locations and depths. Captured images were rectified, enhanced, and processed using threshold values and noise removal filters. Pavement rut depths were measured for different severity levels for the three mentioned different methods using digital surface models (DSM) extracted from the mobile and terrestrial laser scanning systems point clouds. Statistical analysis of the extracted surfaces showed that the mean difference of measured rut depths between mobile laser scanning and GPS was 24 mm, while it was 45 mm for the terrestrial laser scanning system. Results showed consistent accuracy and preference for terrestrial laser scanner measurements associated with least commission errors; however, mobile laser scanning system had lowest omission errors, whereas the potential accuracy measured in terms of root mean square error (RMSE) was 74 mm for the mobile laser scanning system and 93 mm of the static terrestrial laser scanner system, respectively. On the other hand, the consistency of accuracy of measurements was slightly better for the static terrestrial laser system with a mean average error (MAE) of 66 mm, while it was 97 mm for the mobile system. High correlation does exist between mobile laser scanner and GPS measurements with R2 of 0.92, while it was 0.89 between static terrestrial laser system and GPS systems. These results and potential accuracies of rut depth measurements of the new used techniques would open the door to adapt them in different micro and macro measurements in numerous transportation engineering applications.
Applied Geomatics, 2020
Rutting is one of the severe pavement distresses. It is defined as longitudinal depression under ... more Rutting is one of the severe pavement distresses. It is defined as longitudinal depression under wheel path, caused by repeated traffic load, and it is an indicator of the structural failure as well as having effect on road user's safety and riding quality. The objectives of this study are to use geomatics techniques such as terrestrial laser scanner, precise positioning system (RTK) and cellular phone for rutting measurements in minor roads in northern part of Jordan, in addition to develop rutting and lateral displacement models using different parameters such as annual average daily traffic (AADT), truck percent, lane width, pavement age and pavement thickness. Manual measurement of rutting was used as reference method for verifying the results obtained from laser scanning, RTK and cellular phone. The study showed that the used methods produced accurate and reliable results compared with the manual method based on root-mean-square (RMSE) which was 0.557 cm for the RTK measurements, 0.577 cm for cellular phone measurements and 0.592 cm of the laser scanner system, respectively. On the other hand, the consistency of accuracy of measurements was slightly better for the cellular phone measurements with a mean average error (MAE) of 0.415 cm, while it was 0.422 cm for the RTK system and 0.442 cm for the laser scanner measurements. The finding of this research will support the development of using geomatics techniques for the measurement of pavement rutting which facilitate the processes and give reliable surface measurement in short time.
International Journal of Advanced Computer Science and Applications, 2018
Feature extraction methods and subsequent neural network performances were used in this research ... more Feature extraction methods and subsequent neural network performances were used in this research to impose proper assessment for distressed roads for a case study area in the North of Jordan. Object recognition method was used to extract roads cracks from airborne images acquired by drones. After images has been thresholded and the noise removed, digital image processing algorithms were applied to detect the presence of different crack types in the surface of pavement. In addition to that, the process was capable to automatically determine the length and the orientation of the cracks which were used as input for a neural network pattern recognition function designed for this purpose. Artificial Neural Network was used, tested and verified for cracks extraction. Different patterns and numbers of hidden layers were also investigated. The results revealed that using image processing techniques and neural network could detect pavement cracks with high accuracy.
Applied Geomatics, 2019
The purpose of the current study is to predict accident hot spots in different locations using Ge... more The purpose of the current study is to predict accident hot spots in different locations using Geographic Information System (GIS) and fuzzy logic. The data used contained accident types and occurrence time. Fatality and injury were also studied with spatialtemporal analysis. Moreover, accident hot spots were predicted performing Weighted Overlay Method (WOM) and Fuzzy Overlay Method (FOM), which are widely used in decision making and alternatives analysis based on the results obtained from Analytic Hierarchy Process (AHP). Point Density (PD) method was used to verify hot spots in urban region that resulted from the mentioned two methods. Traffic accidents' hot spots were predicted for Irbid City in Jordan using the data of the accidents that occurred between 2013 and 2015. Both WOM and FOM proved to be successful in identifying hot spots in parts of study area when verified to PD surface. Final results showed that eight hot spots were pointed out; three are road sections and five are major intersections, which were analyzed to get accident-contributing factors and suggest the proper remedies.
International Journal of Remote Sensing, 2019
Assessing and monitoring of the landfill temperature is of great importance, so as to assess envi... more Assessing and monitoring of the landfill temperature is of great importance, so as to assess environmental impacts of the landfill and to prevent fires that may lead to failure of the landfill operations. In this study, satellite images were used in mapping the landfill surface temperature (LFST) of Al Akeeder landfill site in Northern Jordan. Artificial Neural Network (ANN) model was developed to simulate and predict the LFST. Fifty-four Landsat satellite images on different dates covering the period 2000-2016 were collected and utilized after being subjected to correction of the thermal band. Multi-temporal thematic maps were developed for the landfill site and the LFST trends, patterns and magnitude were evaluated. Correlating LFST with the amount of the landfilled solid waste has resulted in a good correlation coefficient (r = 0.884), which implies that LFST can serve as an indicator of the amount of solid waste buried in the landfill. Solid waste amount, methane emitted as well as meteorological parameters, such as temperature, humidity, wind speed and evaporation, were used to simulate and predict the value of landfill LFST using ANN. Validation of the ANN has resulted in a good correlation between the predicted and calculated values of the LFST.
International Journal of Remote Sensing, 2019
The objective of the present study is to monitor and predict the changes in land surface temperat... more The objective of the present study is to monitor and predict the changes in land surface temperature (LST) in the North of Jordan during the Period 2000 to 2016. Due to political instability in the nearby countries Syria and Iraq, Jordan has witnessed increased influxes of refugees, starting from the year 2003, which has been led to the urban expansion in the area that reflected on the climatic conditions and affected the LST values. Satellite images were used for providing LST, the acquired images represented two seasons of each year, namely summer and winter. Simulation and prediction of LST values for the next 10 years were carried out using nonlinear autoregressive exogenous (NARX) artificial neural network (ANN) model. The inputs to the model consist of meteorological data collected from eight stations in the study area, population, and land use and land cover (LULC). In fact, LULC was expressed in terms of normalized difference building index (NDBI) and normalized difference vegetation index (NDVI) that were obtained from satellite images. The model showed a high correlation between these parameters and the values of simulated LST, where the correlation coefficient for the training set, validation set, testing set and for the entire data ranged from 0.91 to 0.92. Based on the predicted LST values, LST maps for the next 10 years were developed and compared with the present actual LST maps for the year 2016. The comparison has shown an average increase of 1.1°C in the average LST values, which is considered a significant increase compared with previous studies.
Renewable Energy, 2012
ABSTRACT The joint challenge of global pollution and depletion of fossil fuels is driving intense... more ABSTRACT The joint challenge of global pollution and depletion of fossil fuels is driving intense search into alternative renewable sources. This paper reports the modeling and optimization of biogas production on mixed substrates of saw dust, cow dung, banana stem, rice bran and paper waste using Artificial Neural Network (ANN) coupling Genetic Algorithm (GA). Data from twenty five mini-pilot biogas fermentations were used to train and validate a structured ANN with a topology of 5-2-1. The model served as fitness function for GA optimization process. An optimized substrate profile emerged with a predicted biogas performance of 10.144L. Evaluation of the optimal profile gave a biogas production of 10.280L, thus an increase of 8.64%, and an early biogas production initiated on the 3rd day of fermentation against the 8th day in non-optimized system. ANN coupling GA efficiently modeled the non-linear behavior of the process. A recipe for an optimum biogas production using the above co-substrates has been elucidated.
Resources
Municipal solid waste services in Jordan are being provided by municipalities, where 90% of the g... more Municipal solid waste services in Jordan are being provided by municipalities, where 90% of the generated solid waste finds its way to the landfills and dump sites. All Jordanian municipalities are suffering from low cost recovery where it reaches 50% in its best case. Realizing these facts, recently, the Jordanian government has embarked on an ambitious package of regulations and policies to promote the adoption of circular economy options. The National Solid Waste Management Strategy (NSWMS) of Jordan has called for rationalization, gradual increase of the service fees and adoption of circular economy options through recycling and composting. To assess the impacts of the proposed policies by the NSWMS on the financial sustainability, a system dynamic modeling (SDM) was carried out for the two largest municipalities in the country, namely Greater Amman Municipality (GAM) and Greater Irbid Municipality (GIM). The share of solid waste management cost from the total municipal budget i...
Solid waste is a major issue in all countries around the world. Solid waste classification and se... more Solid waste is a major issue in all countries around the world. Solid waste classification and segregation prior to reuse, recycling or recovery is an important step toward sustainable waste management. Traditional manual sorting of solid waste is a labour intensive process that may pose health risks to the workers. Currently, automated classification of solid waste using machine learning techniques are widely applied. This study is aiming to develop an automated waste classification model by testing traditional and deep machine learning models. To achieve that, both open (Trashnet) and generated datasets were used in the model training and testing. The study results showed relatively low prediction capability of the traditional machine learning models like Random Forest (RF) and Support Vector Machine (SVM) as compared to the deep machine learning Convolutional Neural Network (CNN). The testing of the three models on a combined data set of Trashnet with local garbage data set resul...
International Review of Civil Engineering (IRECE)
The Egyptian Journal of Remote Sensing and Space Science
Decision Science Letters
Fires are devastating risky events in forests, having a negative effect on resources, biodiversit... more Fires are devastating risky events in forests, having a negative effect on resources, biodiversity, economics, animal life, and putting people in danger. The goal of this study is to use geospatial techniques to identify areas in Jordan that are at risk of forest fires. The research area extends 50 kilometers north and 15 kilometers east from the Dead Sea. The forest fire risk zones map was developed using six factors: land cover class, aspect, proximity to settlements, elevation, slope, and proximity to roads. All of the factors have been selected based on their fire sensitivity or capacity to cause fire. In this study, a Turkish model with fuzzy logic and Analytical hierarchy analysis (AHP) was utilized to classify the area into five categories of risk ranging from very low to very high. According to the findings, approximately 12.12% of the study area is classified as very low risk, 25.54 % is classified as medium risk, while 12.84% is classified as very high risk. Over the last ...
مجلة جامعة الحسين بن طلال للبحوث
Transportation Research Record: Journal of the Transportation Research Board
Rutting leads hydroplaning, accidents, poor riding quality, and significant maintenance costs. Th... more Rutting leads hydroplaning, accidents, poor riding quality, and significant maintenance costs. This study assists the development of statistical and Artificial pavement rutting models. The proposed methodology is reliable, time-saving, cost-saving, and comfortable. The suggested technique to anticipate rutting considers traffic volumes, pavement, and geometrical parameters such as lane and shoulder widths. This research modeled 33 main highways' ruts. Most of these roads have serious de-stressing problems with rutted pavement. The developed rutting prediction models demonstrated a medium to high correlation between rut depth and independent variables including annual average daily traffic, truck fleet percentage, pavement thickness, and number of lanes. The correlation coefficients such as R2 were found to be moderate for most of the developed models. The linear models of rutting prediction were statistically significant, with R2 values averaging around 66%, whereas the logistic...
International Journal of Healthcare Management
The Egyptian Journal of Remote Sensing and Space Science
Sustainability: Science, Practice and Policy, 2021
Air Quality, Atmosphere & Health, 2021
This study deals with the simulation and prediction of air pollutants in Irbid city (north of Jor... more This study deals with the simulation and prediction of air pollutants in Irbid city (north of Jordan) before and during the spread of the COVID-19 virus pandemic by using an artificial neural network (ANN). Based on the data obtained from the air quality monitoring station for the year 2019 and the first quarter of the year 2020, it was possible to develop an ANN model to simulate and predict the concentrations of three air pollutants, namely nitrogen dioxide (NO 2), sulfur dioxide (SO 2), and particulate matter with diameter less than 10 μm (PM 10). Several ANN model configurations were tested to select the best model that could predict the concentration of the three air pollutants with meteorological parameters being used as input to the model. The results showed that the concentration of the pollutants during the coronavirus lockdown was declined by various percentages (from 29% for PM 10 to 72% for NO 2) as compared to their concentration before the pandemic period. Furthermore, the developed ANN model could simulate and predict the concentration of the pollutants during the pandemic period with sufficient accuracy as judged by the values of the coefficient of determination and the mean square error. The study results indicate that properly trained and structured ANN can be a useful tool to predict air quality parameters with adequate accuracy.
This study aims to improve the discovery of potential groundwater occurrences by a new combinatio... more This study aims to improve the discovery of potential groundwater occurrences by a new combination of remote sensing and analytical techniques.
Procedia Manufacturing, 2020
Optimization of public transportation network in terms of reducing travel time and providing acce... more Optimization of public transportation network in terms of reducing travel time and providing access to areas currently without sufficient access to the service facility would certainly motivate private car owners to use public transport. The reduction of vehicle numbers on the roads will undoubtedly lead to minimizing traffic congestion and reducing air pollution due to lesser exhaust emissions. This study used Geographic Information System (GIS), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) to model the location of bus stops in Amman city in Jordan to find optimal travel time and serviceability of stops. The GIS modeling provided significant reduction of travel time for streets with redundant bus stops located at irregular distances, e,g. Zahran Street, where travel time was reduced by (23.25%) during off-peak hours and by 19.74% during peak hours, while PSO provided (28.95%) and 39% reductions, respectively. GA also provided significant reduction in travel time (47.96 %) for Zahran Street. For streets with insufficient number of bus stops, travel time went up due to increase in the number of stops, e.g. Al-Quds Street, where travel time increased from 12.25 min per route to 50.71 min due to increase in the number of stops from 6 to 25. In contrast, this increase in travel time significantly reduced the walking distance to the bus stop, from over 2000 m to approximately 400 m. Moreover, demand for bus stops serviceability significantly more than capacity. Average demand per bus stop on Al-Quds street, for example, was 10456 persons with a capacity of 1465.Comparison of the models by applying them to roads not included in the study showed consistent results that further confirmed the reliability of the models. The developed PSO algorithm and GA are easy to use for urban planning, where they can be applied to any existing network or planned development.
Applied Geomatics, 2021
This research work was anticipated to quantify pavement rut depths for one of the main roads in J... more This research work was anticipated to quantify pavement rut depths for one of the main roads in Jordan. New techniques using mobile and terrestrial laser scanning systems were used in order to detect, assess, and evaluate the surface measured rutting values. A study area located to the south of Amman, the capital city of Jordan, was used for data collection purpose. Accuracy assessment was carried out with reference to ground measurements using differential global position system (GPS). GPS static measurements were used to have accurate and precise rutting locations and depths. Captured images were rectified, enhanced, and processed using threshold values and noise removal filters. Pavement rut depths were measured for different severity levels for the three mentioned different methods using digital surface models (DSM) extracted from the mobile and terrestrial laser scanning systems point clouds. Statistical analysis of the extracted surfaces showed that the mean difference of measured rut depths between mobile laser scanning and GPS was 24 mm, while it was 45 mm for the terrestrial laser scanning system. Results showed consistent accuracy and preference for terrestrial laser scanner measurements associated with least commission errors; however, mobile laser scanning system had lowest omission errors, whereas the potential accuracy measured in terms of root mean square error (RMSE) was 74 mm for the mobile laser scanning system and 93 mm of the static terrestrial laser scanner system, respectively. On the other hand, the consistency of accuracy of measurements was slightly better for the static terrestrial laser system with a mean average error (MAE) of 66 mm, while it was 97 mm for the mobile system. High correlation does exist between mobile laser scanner and GPS measurements with R2 of 0.92, while it was 0.89 between static terrestrial laser system and GPS systems. These results and potential accuracies of rut depth measurements of the new used techniques would open the door to adapt them in different micro and macro measurements in numerous transportation engineering applications.
Applied Geomatics, 2020
Rutting is one of the severe pavement distresses. It is defined as longitudinal depression under ... more Rutting is one of the severe pavement distresses. It is defined as longitudinal depression under wheel path, caused by repeated traffic load, and it is an indicator of the structural failure as well as having effect on road user's safety and riding quality. The objectives of this study are to use geomatics techniques such as terrestrial laser scanner, precise positioning system (RTK) and cellular phone for rutting measurements in minor roads in northern part of Jordan, in addition to develop rutting and lateral displacement models using different parameters such as annual average daily traffic (AADT), truck percent, lane width, pavement age and pavement thickness. Manual measurement of rutting was used as reference method for verifying the results obtained from laser scanning, RTK and cellular phone. The study showed that the used methods produced accurate and reliable results compared with the manual method based on root-mean-square (RMSE) which was 0.557 cm for the RTK measurements, 0.577 cm for cellular phone measurements and 0.592 cm of the laser scanner system, respectively. On the other hand, the consistency of accuracy of measurements was slightly better for the cellular phone measurements with a mean average error (MAE) of 0.415 cm, while it was 0.422 cm for the RTK system and 0.442 cm for the laser scanner measurements. The finding of this research will support the development of using geomatics techniques for the measurement of pavement rutting which facilitate the processes and give reliable surface measurement in short time.
International Journal of Advanced Computer Science and Applications, 2018
Feature extraction methods and subsequent neural network performances were used in this research ... more Feature extraction methods and subsequent neural network performances were used in this research to impose proper assessment for distressed roads for a case study area in the North of Jordan. Object recognition method was used to extract roads cracks from airborne images acquired by drones. After images has been thresholded and the noise removed, digital image processing algorithms were applied to detect the presence of different crack types in the surface of pavement. In addition to that, the process was capable to automatically determine the length and the orientation of the cracks which were used as input for a neural network pattern recognition function designed for this purpose. Artificial Neural Network was used, tested and verified for cracks extraction. Different patterns and numbers of hidden layers were also investigated. The results revealed that using image processing techniques and neural network could detect pavement cracks with high accuracy.
Applied Geomatics, 2019
The purpose of the current study is to predict accident hot spots in different locations using Ge... more The purpose of the current study is to predict accident hot spots in different locations using Geographic Information System (GIS) and fuzzy logic. The data used contained accident types and occurrence time. Fatality and injury were also studied with spatialtemporal analysis. Moreover, accident hot spots were predicted performing Weighted Overlay Method (WOM) and Fuzzy Overlay Method (FOM), which are widely used in decision making and alternatives analysis based on the results obtained from Analytic Hierarchy Process (AHP). Point Density (PD) method was used to verify hot spots in urban region that resulted from the mentioned two methods. Traffic accidents' hot spots were predicted for Irbid City in Jordan using the data of the accidents that occurred between 2013 and 2015. Both WOM and FOM proved to be successful in identifying hot spots in parts of study area when verified to PD surface. Final results showed that eight hot spots were pointed out; three are road sections and five are major intersections, which were analyzed to get accident-contributing factors and suggest the proper remedies.
International Journal of Remote Sensing, 2019
Assessing and monitoring of the landfill temperature is of great importance, so as to assess envi... more Assessing and monitoring of the landfill temperature is of great importance, so as to assess environmental impacts of the landfill and to prevent fires that may lead to failure of the landfill operations. In this study, satellite images were used in mapping the landfill surface temperature (LFST) of Al Akeeder landfill site in Northern Jordan. Artificial Neural Network (ANN) model was developed to simulate and predict the LFST. Fifty-four Landsat satellite images on different dates covering the period 2000-2016 were collected and utilized after being subjected to correction of the thermal band. Multi-temporal thematic maps were developed for the landfill site and the LFST trends, patterns and magnitude were evaluated. Correlating LFST with the amount of the landfilled solid waste has resulted in a good correlation coefficient (r = 0.884), which implies that LFST can serve as an indicator of the amount of solid waste buried in the landfill. Solid waste amount, methane emitted as well as meteorological parameters, such as temperature, humidity, wind speed and evaporation, were used to simulate and predict the value of landfill LFST using ANN. Validation of the ANN has resulted in a good correlation between the predicted and calculated values of the LFST.
International Journal of Remote Sensing, 2019
The objective of the present study is to monitor and predict the changes in land surface temperat... more The objective of the present study is to monitor and predict the changes in land surface temperature (LST) in the North of Jordan during the Period 2000 to 2016. Due to political instability in the nearby countries Syria and Iraq, Jordan has witnessed increased influxes of refugees, starting from the year 2003, which has been led to the urban expansion in the area that reflected on the climatic conditions and affected the LST values. Satellite images were used for providing LST, the acquired images represented two seasons of each year, namely summer and winter. Simulation and prediction of LST values for the next 10 years were carried out using nonlinear autoregressive exogenous (NARX) artificial neural network (ANN) model. The inputs to the model consist of meteorological data collected from eight stations in the study area, population, and land use and land cover (LULC). In fact, LULC was expressed in terms of normalized difference building index (NDBI) and normalized difference vegetation index (NDVI) that were obtained from satellite images. The model showed a high correlation between these parameters and the values of simulated LST, where the correlation coefficient for the training set, validation set, testing set and for the entire data ranged from 0.91 to 0.92. Based on the predicted LST values, LST maps for the next 10 years were developed and compared with the present actual LST maps for the year 2016. The comparison has shown an average increase of 1.1°C in the average LST values, which is considered a significant increase compared with previous studies.
Renewable Energy, 2012
ABSTRACT The joint challenge of global pollution and depletion of fossil fuels is driving intense... more ABSTRACT The joint challenge of global pollution and depletion of fossil fuels is driving intense search into alternative renewable sources. This paper reports the modeling and optimization of biogas production on mixed substrates of saw dust, cow dung, banana stem, rice bran and paper waste using Artificial Neural Network (ANN) coupling Genetic Algorithm (GA). Data from twenty five mini-pilot biogas fermentations were used to train and validate a structured ANN with a topology of 5-2-1. The model served as fitness function for GA optimization process. An optimized substrate profile emerged with a predicted biogas performance of 10.144L. Evaluation of the optimal profile gave a biogas production of 10.280L, thus an increase of 8.64%, and an early biogas production initiated on the 3rd day of fermentation against the 8th day in non-optimized system. ANN coupling GA efficiently modeled the non-linear behavior of the process. A recipe for an optimum biogas production using the above co-substrates has been elucidated.