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Öğe Classification of RASAT Satellite Images Using Machine Learning Algorithms(Springer International Publishing Ag, 2022) Abujayyab, Sohaib K. M.; Yucer, Emre; Karas, I. R.; Gultekin, I. H.; Abali, O.; Bektas, A. G.The development in the remote sensing and geographic information systems facilitated the monitoring processes of changes in land cover and use. This article aimed to evaluate the classification accuracy of five supervised classification methods: Neural Network, Naive Bayes, K-nearest neighbors, discriminant analysis and Decision Tree using the Turkish RASAT satellite images. The Bursa area in Turkey was taken as a study area to examine the RASAT satellite images. MATLAB and Python programming languages were employed to develop the training dataset and generated the five classifiers. According to the performance analysis using confusion matrix metric, the best overall accuracy was achieved by K-nearest neighbors. the K-nearest neighbors method produced 100% performance accuracy using RASAT satellite image. This comparative analysis showed that the K-nearest neighbors can be used as a trusted method for satellite image classification.Öğe The effect of air quality parameters on new COVID-19 cases between two different climatic and geographical regions in Turkey(Springer Wien, 2023) Ulutas, Kadir; Abujayyab, Sohaib K. M.; Abu Amr, Salem S. S.; Alkarkhi, Abbas F. M.; Duman, SibelDifferent health management strategies may need to be implemented in different regions to cope with diseases. The current work aims to evaluate the relationship between air quality parameters and the number of new COVID-19 cases in two different geographical locations, namely Western Anatolia and Western Black Sea in Turkey. Principal component analysis (PCA) and regression model were utilized to describe the effect of environmental parameters (air quality and meteorological parameters) on the number of new COVID-19 cases. A big difference in the mean values for all air quality parameters has appeared between the two areas. Two regression models were developed and showed a significant relationship between the number of new cases and the selected environmental parameters. The results showed that wind speed, SO2, CO, NOX, and O-3 are not influential variable and does not affect the number of new cases of COVID-19 in the Western Black Sea area, while only wind speed, SO2, CO, NOX, and O-3 are influential parameters on the number of new cases in Western Anatolia. Although the environmental parameters behave differently in each region, these results revealed that the relationship between the air quality parameters and the number of new cases is significant.Öğe Effects of Meteorological Parameters on Surface Water Loss in Burdur Lake, Turkey over 34 Years Landsat Google Earth Engine Time-Series(Mdpi, 2021) Abujayyab, Sohaib K. M.; Almotairi, Khaled H.; Alswaitti, Mohammed; Amr, Salem S. Abu; Alkarkhi, Abbas F. M.; Tasoglu, Enes; Hussein, Ahmad MohdAzizThe current work aims to examine the effect of meteorological parameters as well as the temporal variation on the Burdur Lake surface water body areas in Turkey. The data for the surface area of Burdur Lake was collected over 34 years between 1984 and 2019 by Google Earth Engine. The seasonal variation in the water bodies area was collected using our proposed extraction method and 570 Landsat images. The reduction in the total area of the lake was observed between 206.6 km(2) in 1984 to 125.5 km(2) in 2019. The vegetation cover and meteorological parameters collected that affect the observed variation of surface water bodies for the same area include precipitation, evapotranspiration, albedo, radiation, and temperature. The selected meteorological variables influence the reduction in lake area directly during various seasons. Correlations showed a strong positive or negative significant relationship between the reduction and the selected meteorological variables. A factor analysis provided three components that explain 82.15% of the total variation in the data. The data provide valuable references for decision makers to develop sustainable agriculture and industrial water use policies to preserve water resources as well as cope with potential climate changes.Öğe Employing Neural Networks Algorithm for LULC Mapping(Univ Latvia, 2020) Abujayyab, Sohaib K. M.; Karas, Ismail RakipLand use/land cover (LULC) maps represent a primary requirement for several geospatial applications around the world such as change detection, time series analysis, environment, and urban researches. Mapping LULC from remotely sensed data based on satellite image classification handle the rapid changes in extensive geographical areas. Several effective and efficient mechanisms suggested for supervised satellite image classification. The neural networks machine learning algorithm became a major method in supervised satellite image classification. The objective of this article is to employ neural networks as a machine learning algorithm for LULC mapping. The study applied in Ankara area, which is the capital city of Turkey. This work utilized a free Landsat 8 satellite image with the Operational Land Imager OLI sensor to implement the analysis. The image was obtained and processed in ArcGIS software. Then, the machine learning data set developed using Python scripting language. Every band out of 8 bands from Landsat 8 image considered as an explanatory variable, while the output variable defined based on visual interpretation. The training dataset built based on the signature file and random sample points. The training dataset divided into three sections, for training, for validation and the last section for testing. The training and testing processes were implemented using Google-Tensor Flow Keres library from Anaconda distribution. Feedforward neural network structure implemented with 500 neurons in the hidden layer. Confusion matrix used as accuracy assessment metrics to measure the performance of the developed model. The overall accuracy of the developed model was 92%. In terms of overall accuracy and robustness, the neural networks algorithm was effectively implemented and the LULC map produces. The model gained high accuracy that it is satisfied with the geospatial accuracy target. The consequence showed the competence of neural networks algorithm to generating LULC maps from Landsat 8 satellite images.Öğe Flash flood susceptibility mapping in urban area using genetic algorithm and ensemble method(Taylor & Francis Ltd, 2022) Saleh, Azlan; Yuzir, Ali; Sabtu, Nuridah; Abujayyab, Sohaib K. M.; Bunmi, Mudashiru Rofiat; Quoc Bao PhamFlooding is the main recurring natural disaster in Sungai Pinang catchment, Malaysia. Flash flood susceptibility mapping (FFSM) explains a key component of flood risk analysis and enables efficient estimation of the spatial extent of flood characteristics. The current study applied four machine learning models (i.e. Logistic Regression (LR), K-Nearest Neighbors (KNN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)) ensembled with the Statistical Index (SI) to develop flash flood susceptibility mapping (FFSM). 110 flash flood locations in the Sungai Pinang catchment were used in this study. Genetic algorithm (GA) was combined with Fuzzy Unordered Rules Induction Algorithm (FURIA), Rotation Forest, and Random Subspace for the feature selection method (FSM). The results showed that GA-FURIA outperformed the other two models in terms of accuracy based on the FSM. Twelve flash flood variables were selected by GA-FURIA. The FFSM results showed that the SI-RF model has the highest area under the receiver operating characteristics (AUROC) curve of success rate (0.978), whereas the SI-XGB has the best AUROC in terms of validation rate (0.997). The findings suggest that the twelve ideal conditioning variables may be used to optimize FFSM development.Öğe Integrating object-based and pixel-based segmentation for building footprint extraction from satellite images(Elsevier, 2023) Abujayyab, Sohaib K. M.; Almajalid, Rania; Wazirali, Raniyah; Ahmad, Rami; Tasoglu, Enes; Karas, Ismail R.; Hijazi, IhabAccurately delineating building footprints from optical satellite imagery presents a formidable challenge, particularly in urban settings characterized by intricate and diverse structures. Consequently, enhancing the utility of these images for geospatial data updates demands meticulous refinement. Machine learning algorithms have made notable contributions in this context, yet the pursuit of precision remains an ongoing challenge. This paper aims to enhance the accuracy of building footprint extraction through the integration of object-based and pixel-based segmentation techniques. Additionally, it evaluates the performance of machine learning methodologies, specifically LightGBM, XGBoost, and Neural Network (NN) approaches. The model's evaluation employed low spectral resolution optical images, widely accessible and cost-effective for acquisition. The study's outcomes demonstrate a substantial enhancement in extraction accuracy compared to extant literature. The proposed methodology attains an overall accuracy of 99.39%, an F1 measurement of 0.9935, and a Cohen Kappa index of 0.9870. Thus, the proposed approach signifies a noteworthy advancement over existing techniques for building footprint extraction from high-resolution optical imagery.Öğe Landslides Risk Prediction Using Cascade Neural Networks Model at Mus In Turkey(Iop Publishing Ltd, 2020) Abujayyab, Sohaib K. M.; Saleh, AzlanGlobally, landslides risk represent a challenging issue that negatively affecting the infrastructure and human and neutral life. Among the former studies, the methods of predicting landslides risk maps found to be need further experiments. The aim of this study was to predict the landslide risk map at Mus in Turkey using cascade neural networks model. In this article, Trainlm function used to train 9954 sample points in the dataset using Matlab software. ArcGIS employed to prepare the explanatory variables, inventory landslides map, data sampling and producing the final landslides risk map. The developed model achieved the best performance accuracy by implanting an optimizer for the used number of neurons. After 60 training experiments, 52 neurons found the best number in this model. Chunks computing using Python programing in ArcGIS implemented to solve the intensive computing and data restructuring issues. Although the implementation at regional scale with 14015 km2, the final landslides risk map was successfully produced. The best-achieved performance accuracy was 80% based on receiver operating characteristic curve (ROC) and area under the curve (AUC). To summarize, the cascade neural networks model can reliably be implement in predicting landslides risk maps at regional-scale with the aid of chunks computing.Öğe Multivariate Analysis for Air Contamination and Meteorological Parameters in Zonguldak, Turkey(Jordan Univ Science & Technology, 2022) Uluta, Kadir; Alkarkhi, Abbas F. M.; Abujayyab, Sohaib K. M.; Abu Amr, Salem 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.Öğe Productivity modelling of an inclined stepped solar still for seawater desalination using boosting algorithms based on experimental data(Desalination Publ, 2022) Wazirali, Raniyah; Abujazar, Mohammed Shadi S.; Abujayyab, Sohaib K. M.; Ahmad, Rami; Fatihah, Suja; Kabeel, A. E.; Karaagac, Sakine UgurluSolar energy has recently become a viable option for desalinating seawater, primarily in arid regions. However, increasing the productivity of solar still by integrating experimental base and modelling methods is still subject to prediction errors; therefore, the main objective of this research is to postulate and test boosting algorithms for predicting the efficiency and productivity of the system. Five boosting regressors were deployed and evaluated: categorical boosting, adaptive boosting, extreme gradient boosting, gradient boosting machine, and gradient boosting machine (LightGBM). The proposed regressors are implemented based on the system's actual recorded dataset (consisting of 720 observations). The dataset consists of input variables, which are the wind speed (V), cloud cover, humidity, ambient temperature (T), solar radiation (SR), (T-io), (T-w), (T-v), and (T-t). Also, the output variable is represented by the productivity of the system. The dataset was separated into training (70%) and testing (30%) sets. In order to decrease regressors errors, hyperparameter optimization was employed. GradientBoosting approach provided the best prediction, with 95% R-2 accuracy and 39.57 root mean square error (RMSE) error. The LightGBM technique achieved 94% R-2 accuracy and 40.07 RMSE error in the testing dataset. The results reveal that GradientBoosting outperforms the cascaded forward neural network in predicting system productivity (CFNN).Öğe Wildfire Susceptibility Mapping Using Five Boosting Machine Learning Algorithms: The Case Study of the Mediterranean Region of Turkey(Hindawi Ltd, 2022) Abujayyab, Sohaib K. M.; Kassem, Moustafa Moufid; Khan, Ashfak Ahmad; Wazirali, Raniyah; Ozturk, Ahmet; Toprak, FerhatForest fires caused by different environmental and human factors are responsible for the extensive destruction of natural and economic resources. Modern machine learning techniques have become popular in developing very accurate and precise susceptibility maps of various natural disasters to help reduce the occurrence of such calamities. The present study has applied and tested multiple algorithms to map the areas susceptible to wildfire in the Mediterranean Region of Turkey. Besides, the performance of XGBoost, CatBoost, Gradient Boost, AdaBoost, and LightGBM methods for wildfire susceptibility mapping is also examined. The results have revealed the higher testing accuracy of CatBoost (95.47%) algorithm, followed by LightGBM (94.70%), XGBoost (88.8%), AdaBoost (86.0%), and GBM (84.48%) algorithms. Resultant wildfire susceptibility maps provide proper inventories for forest engineers, planners, and local governments for future policies regarding disaster management in Turkey.
