Yazar "Abujayyab, S.K.M." seçeneğine göre listele
Listeleniyor 1 - 12 / 12
Sayfa Başına Sonuç
Sıralama seçenekleri
Öğe Application of exploratory spatial techniques in the identification of tourism hotspots in the aegean region of Turkey(International Society for Photogrammetry and Remote Sensing, 2020) Rafique, A.; Karas, I.R.; Abujayyab, S.K.M.; Khan, A.A.; Demiral, E.Exploratory Spatial Analysis Techniques (ESDA) have become popular to identify the spatial association of different variables in many fields of natural and social sciences. The application of Global Moran's I statistics enables us to provide visual insights of spatial data. It helps to detect spatial patterns and hotspots of an activity or process, based on spatial autocorrelation. This study aims to investigate the spatial dependence of domestic and inbound tourist arrivals to 123 cities of all eight provinces of the Aegean Region of Turkey. For analysis, city-level data about domestic and inbound tourist arrivals during 2015-2019 is collected from the Turkish Ministry of Culture and Tourism and is converted to logarithm form to avoid any skewness. The Arc GIS and GeoDa programs are employed for the analysis of spatial autocorrelation and visualization of hotspots of tourist flows in the regions. The results of the study reveal that tourist flows in the region are concentrated in the coastal areas, while inland cities receive an insufficient number of tourists. The hotspots of tourist flow are located mostly in the coastal towns of the provinces of Izmir, Aydin, and Mugla. The study is significant in the provision of useful information regarding resource allocation to the tourism hotspots and the implication of sustainable tourism policy to better utilization of tourism potential. © 2020 International Society for Photogrammetry and Remote Sensing. All rights reserved.Öğe AUTOMATED PREDICTION SYSTEM for VEGETATION COVER BASED on MODIS-NDVI SATELLITE DATA and NEURAL NETWORKS(International Society for Photogrammetry and Remote Sensing, 2019) Abujayyab, S.K.M.; Karas, I.R.Around the world, vegetation cover functioning as shelter to wildlife, clean water, food security as well as treat large part of air pollution problem. Accurate predictive data early warn and provide knowledge for decision makers to reduce the effects of changes in vegetation cover. In this paper, an automated prediction system was developed to forecast vegetation cover. Prediction system based on moderate satellite data spatial resolution and global coverage data. The tools of system automate processing Moderate Resolution Imaging Spectroradiometer (MODIS) images and training neural networks (NN) model based on 60,000 observations to forecast future density of Normalized Difference Vegetation Index (NDVI). Zonguldak data, located in north of Turkey as dense vegetation cover area utilized as case study for system application. This system significantly facilitates predictive process for users than previous long and complex models. © 2020 Authors.Öğe Comparison of the frequency ratio, index of entropy, and artificial neural networks methods for landslide susceptibility mapping: A case study in Pınarbaşı/Kastamonu (North of Turkey)(Elsevier, 2021) Tasoglu, E.; Abujayyab, S.K.M.The selection of a suitable method is crucial for landslide susceptibility mapping (LSM). The main objective of this article was to compare the index of entropy (IoE), frequency ratio (FR), and artificial neural network (ANN) methods utilized in LSM. Landslide conditioning factors such as slope, distance to roads, aspect, curvature, plan curvature, elevation, profile curvature, distance to streams, soil types, topographic wetness index (TWI), and lithology have been used to carry out the LSM of the Pınarbaşı district (Kastamonu-Turkey). All models were compared considering their prediction rates obtained using the Area Under Curve (AUC) method. The models were evaluated with a total of 1000 points, including landslide and non-landslide areas. The findings of this study show that the AUC accuracy of the FR, IoE and ANN models were 0.873, 0.869, and 0.962, correspondingly. The ANN model achieved the highest accuracy. The AUC of both the FR and IoE models showed reasonably good accuracy for producing a landslide susceptibility map. The FR and IoE methods are straightforward and easy to implement compared to ANNs. Therefore, both can be efficiently used for the LSM. © 2022 Elsevier Inc. All rights reserved.Öğe EVALUATION of PARTICULATE MATTER (PM10) DISTRIBUTIONS in AZMIR USING GEOGRAPHIC INFORMATION SYSTEMS for SMART CITIES APPLICATIONS(International Society for Photogrammetry and Remote Sensing, 2021) Ulutas, K.; Abujayyab, S.K.M.; Karas, A.R.In this study, PM10 values from the air quality monitoring station in Izmir was evaluated. 9 stations could be used in this study, since PM10 data are suitable to evaluate for the years 2020-2019-2018. The 4-season and annual PM10 distribution map for 3 years was prepared using ArcGIS. The benefits of these maps to city managers in the smart city application were expressed. In addition, PM10 data of 9 stations were evaluated according to legal limit values. It was determined that AliaAa and Gaziemir stations exceeded the limit values more than other stations. It has been observed that different sources of air pollution such as industry, traffic and heating affect different districts. When the number of days exceeding the limit value and the number of days without measurement are evaluated together, it is seen that the limit values are exceeded by all stations. © Author(s) 2021. CC BY 4.0 License.Öğe GEOSPATIAL MACHINE LEARNING DATASETS STRUCTURING and CLASSIFICATION TOOL: CASE STUDY for MAPPING LULC from RASAT SATELLITE IMAGES(International Society for Photogrammetry and Remote Sensing, 2019) Abujayyab, S.K.M.; Karas, I.R.Remote sensing satellite images plays a significant role in mapping land use/land cover LULC. Machine learning ML provide robust functions for satellite image classification. The objective of this paper is to extend the capability of GIS specialists in geospatial area with minimum knowledge in computer science to easily perform ML satellite image classification. A framework consisting 7 stages established. Tools of steps developed in two programing environments, which are ArcGIS for geospatial datasets structuring and Anaconda for ML training and classification. During the development, authors constrained to reduce the complexity of big data of satellite images and limited memory of computers to make tools available for implementation in PC. In addition, automation and improving the performance accuracy. TensorFlow-Keras library employed to perform the classification using neural networks. A case study using RASAT satellite image in Ankara-Turkey utilized to perform the analysis. The developed classifier gained 80% performance accuracy. The complete RASAT satellite image processed and smoothly classified based on blocks methods. The developed tools successfully tested and applied in geospatial area and can be effectively execute in PC by GIS specialist. © 2019 S. K. M. Abujayyab.Öğe Handling massive data size issue in buildings footprints extraction from high-resolution satellite images(Springer, 2020) Abujayyab, S.K.M.; Karas, I.R.Building information modelling BIM is relying on plenty of geospatial information such as buildings footprints. Collecting and updating BIM information is a considerable challenge. Recently, buildings footprints automatically extracted from high-resolution satellite images utilizing machine learning algorithms. Constructing required training datasets for machine learning algorithms and testing data is computationally intensive. When the analysis performs in large geographic areas, researchers are struggling from out of memory problems. The requirement of developing improved, fit memory computation methods for accomplishing this computation is urgent. This paper targeting to handling massive data size issue in buildings footprints extraction from high-resolution satellite images. This article established a method to process the spatial raster data based on the chunks computing. Chunk-based decomposition decomposes raster array into several tiny cubes. Cubes supposed to be small enough to fit into available memory and prevent memory overflow. The algorithm of the method developed using Python programming language. Spatial data and developed tool were prepared and processed in ArcGIS software. Matlab software utilized for machine learning. Neural networks implemented for extracting the buildings’ footprints. To demonstrate the performance of our approach, high-resolution Orthoimage located in Tucson, Arizona state in American United States was utilized as a case study. Original image was taken by UltraCamEagle sensor and contained (11888 columns, 11866 rows, cell size 0.5 foot, 564,252,032 pixels in 4 bands). The case image contained (1409 columns, 1346 rows, and 7586056 pixels in 4 bands). The full image is impossible to be handled in the traditional central processing unit CPU. The image divided to 36 chunks using 1000 rows and 1000 columns. Full analysis spent 35 min using Intel Core i7 processor. The output performance accuracy of the neural network is 98.3% for testing dataset. Consequences demonstrate that the chunk computing can solve the memory overflow in personal computers during buildings footprints extraction process, especially in case of processing large files of high-resolution images. The developed method is suitable to be implemented in an affordable lightweight desktop environment. In addition, building footprints extracted effetely and memory overflow problem bypassed. Furthermore, the developed method proved the high quality extracted buildings footprints that can be integrated with BIM applications. © Springer Nature Switzerland AG 2020.Öğe LOW DATA REQUIREMENTS FRAMEWORK for LANDSLIDE SUSCEPTIBILITY PREDICTION USING 3D ALOS PALSAR IMAGES and NEURAL NETWORKS(International Society for Photogrammetry and Remote Sensing, 2019) Abujayyab, S.K.M.; Karas, I.R.Development of landslides susceptibility (LS) predictors based on 3D data is an active area of research in the recent years. Predicting landslides susceptibility maps help to secure human lives and maintaining infrastructures from this risk. Several advanced frameworks proposed with high input data to improve the predictors. The aim of this paper is to develop low data requirement framework for LS predictors development. This framework is only using one input 3D ALOS PALSAR image. The framework has three stages. (A) data pre-processing, (B) deriving explanatory factors, and (C) neural networks training and testing. Exactly. 22 input spatial factors were extracted from ALOS PALSAR image. Extracted factors were utilized to develop the FFNN predictor. The structure of the predictor is 22 factors (input layer) × 150 neurons (hidden layer) × 1 (output layer). Furthermore, 5829 sample points utilized during the training stage, while 745810 points sent to the trained predictor to create LS map. Based on confusion matrix metric, performance accuracy (89.3% training and 82.3 testing), While (95.22% training and 84.7% testing) based on Receiver Operating Characteristic curve. Out of the study area in Karabuk, 3.53 km2 (3.03%) were located in very high susceptibility category. Lastly, the application of the proposed framework showed that it is capable develop low data requirement predictors with high accuracy. Framework provide guideline data for future development in taxing topographic circumstances and large scale of data coverage. In addition, the framework handled the inconsistency in data quality and data updating problem. © 2019 S. K. M. Abujayyab.Öğe Measuring the spatial readiness of ambulance facilities for natural disasters using GIS networks analysis(International Society for Photogrammetry and Remote Sensing, 2020) Awad, A.; Ali, H.; Abujayyab, S.K.M.; Karas, I.R.; Sumunar, D.R.S.The massive disasters that arise by nature and humanity are significantly leads to several losses in lives and infrastructures. Disasters such as chemical explosions, flash floods and volcanoes. The high level of preparedness from the governments and administration authorities and ambulance services can significantly reduce the losses in lives. The aim of this paper is to measure the spatial readiness of ambulance facilities for natural disasters using GIS networks analysis. The measurement performed based on three standards, the area covered by the ambulance service, speed of service and the proportion to the population. ArcGIS spatial analysis and network analysis tools employed to develop the coverage maps of the three measured standards. According to the analysis, 94.4% from the study area appeared within the standard distance (20 km) from the ambulance stations, while 91% from the study area appeared within the time response standard (15 minutes) from the ambulance stations. The study area has a deficit of 256,714 people and needs 5 additional ambulances to achieve the demographic standard. The main recommendation of this study is to apply this methodology regularly in the study area to avoid any weakness before the disasters and to increase the level of preparedness. © 2020 International Society for Photogrammetry and Remote Sensing. All rights reserved.Öğe Multivariate Analysis for Air Contamination and Meteorological Parameters in Zonguldak, Turkey(Jordan University of Science and Technology, 2022) Ulutas, K.; Alkarkhi, A.F.M.; Abujayyab, S.K.M.; Abu, Amr, S.S.This study evaluates the concentration of PM10, PM2.5, NOx, NO2, CO and SO2 parameters and the four climatological parameters (temperature, wind speed, humidity and net radiation flux) during the four seasons. Various statistical techniques were utilized to study the behavior of the selected parameters during the seasons. Descriptive statistics exhibited that the studied parameters have high concentrations in winter, except for NO2 (which has a high concentration in autumn), while the concentrations of those parameters were the lowest in summer, except for NO2 and NOX (which have high concentrations in spring). Factor analysis (FA) showed that more than 80% of the total variation belongs to two factors, where 19.47% of the variation was due to wind speed and humidity, while other parameters were responsible for 62.90% of the total variation. Cluster analysis (CA) evaluated the similarity and dissimilarity between various elements through identifying four clusters representing the seasons; cluster 1: autumn, cluster 2: winter, cluster 3: spring and cluster 4: summer. This clustering indicates that the four seasons are entirely different. The highest dissimilarity was reported between summer and the other seasons. CA also classified all parameters into five statistically different clusters; cluster 1: PM10, PM 2.5 and CO; cluster 2: SO2, NOX and NO2; cluster 3: humidity; cluster 4: temperature and radiation and cluster 5: wind speed. This study illustrates the benefits of using multivariate techniques for the evaluation and interpretation of the total variation to get a better picture of the pollution sources/factors and understand the behaviors of the parameters in the air. © 2022 JUST. All Rights Reserved.Öğe Selection of Suitable Site for Solid Waste Landfill : a case study in Kirkuk City, Iraq(Institute of Physics Publishing, 2020) Omar, N.Q.; Shawkat, I.A.; Ali, S.H.; Abujayyab, S.K.M.Selection of a landfill site for solid wastes is considered one of the challenges for local governments and municipalities' directorates in particular all over the world, simply because every single city produces huge amount of solid wastes (garbage's) each day that come from homes, hospitals, schools, industry, offices and centers of markets. In this study, Kirkuk city has been selected as a case study to figure out the solutions for trash, bad odors in most of the neighbourhoods, groundwater pollution, harmful toxic wastes, etc. So, proper criteria selection is very important as a starting point of landfills to preclude unwanted long-standing effects. Otherwise the traditional process of site selection process is difficult because its time consuming and costly. An integration of Geographical Information System (GIS) and Expert Choices Process (ECP) method is best combined technique to solve complex decision making and to select based on the study area condition. Six criteria have been used to select potential suitable site landfill in Kirkuk city. It turned out that the highest criteria is the built up area and the lowest one is bearing capacity of soil (BCS). Moreover, the final produced map shows the suitable area for landfill siting The work purports to present methodology of this inquiry and objective can make impressive solve in global environmental change and its regularly supplies an efficacious operation to select the suitable site to landfill the solid wastes. © Published under licence by IOP Publishing Ltd.Öğe Sustainable solutions for environmental pollutants from solid waste landfills(wiley, 2021) Amr, S.S.A.; Bashir, M.J.K.; Abujayyab, S.K.M.; Ahmad, W.Over the last few decades and due to the rapid population growth, solid waste generation is increasing rabidly. The landfilling process is considered as the main disposal method for municipal solid waste (MSW) due to the simple and applicable technique, and economic features. However, different environmental problems resulting from solid waste landfills have been reported. Landfills contain high levels of organic materials, ammonia, and heavy metals are producing leachate, which causes a possible future pollution of soil, ground and surface water. Moreover, the mismanagement of non-sanitary landfills and dumping sites may contribute to more complicated problems in the environment. In this chapter, the main environmental problems related to landfills including soil, water and air contamination are discussed and evaluated. The sustainable solutions and management for the environmental problems related to solid waste landfills including landfill design and location, leachate management and treatment, and gas emission control are highlighted and discussed. Moreover, the effect of unsuitable locations of landfills on the environment is discussed and evaluated. © 2021 Scrivener Publishing LLC.Öğe Wildfire Detection from Sentinel Imagery Using Convolutional Neural Network (CNN)(Springer Science and Business Media Deutschland GmbH, 2024) Abujayyab, S.K.M.; Karas, I.R.; Hashempour, J.; Emircan, E.; Orçun, K.; Ahmet, G.Wildfires are a significant threat to the environment and human life, and early detection is crucial for effective wildfire management. The aim of this research work is to develop a deep learning model using CNN method for detecting wildfire using satellite imagery. The CNN model development process involves splitting the dataset into training and testing sets, pre-processing the data using ImageDataGenerator function, building a deep learning model using Sequential function, compiling the model using Adam optimizer, categorical cross-entropy loss function, and accuracy metric, and training the model using the fit generator function. The CNN model architecture includes Conv2D, MaxPooling2D, and Dense layers. The dataset was collected from Sentinel-2 L1C, including 159 fire images and 149 non-fire images from different places in the Mediterranean region of Turkey. The CNN model was developed using the Keras library and trained for 200 epochs using the Adam optimizer. The model achieved an accuracy of 92.5% and a loss of 0.22 on the test set, outperforming existing methods for wildfire detection. The research work contributes to the field of wildfire science and management by providing a deep learning CNN detection model that can accurately predict wildfire behavior using satellite imagery. The outcomes of the research work can enable more effective and efficient wildfire mitigation efforts, improving the safety and well-being of communities affected by wildfires. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
