[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124975-en":3,"doc-seo-124975-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124975,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","2D, 3D Noise Modelling on Mobile GIS Application Through Machine Learning Based Models - Doctor of Philosophy Thesis","Doctoral research develops machine learning–based modeling approaches for 2D traffic noise prediction and 3D noise propagation mapping using mobile GIS workflows. The thesis positions geospatial information as key input, integrates neural and ensemble learning strategies, and evaluates modelling performance for vehicular traffic noise. It supports the research through prior publication outputs, including neural network and GIS-based prediction, land-use regression combined with machine learning, and deep learning trend modeling. The study is presented within a University of Technology Sydney PhD thesis framework.","2D, 3D NOISE MODELLING ON MOBILE GIS APPLICATION THROUGH MACHINE LEARNING BASED MODELS  \nby Ahmed Abdulkareem Ahmed Aldulaimi  \nThesis submitted in fulfilment of the requirements for the degree of  \nDoctor of Philosophy  \nunder the supervision of Professor Biswajeet Pradhan and Dr. Osama Sohaib  \nUniversity of Technology Sydney  \nFaculty of Civil and Environmental Engineering  \nCertificate of Original Authorship  \nI, Ahmed Abdulkareem Ahmed Aldulaimi, declare that this thesis, is submitted in fulfilment of the requirements for the award of Doctor of Philosophy in Faculty of Engineering and Information Technology at the University of Technology Sydney.  \nThis thesis is wholly my own work unless otherwise referenced or acknowledged. In addition, I certify that all information sources and literature used are indicated in the thesis. This document has not been submitted for qualifications at any other academic institution. This research is supported by the Australian Government Research Training Program.  \nProduction Note:  \nSignature: X Signature removed prior to publication.  \nAhmed Aldulaimi  \nAhmed Aldulaimi Date: 01/05/2023  \nACKNOWLEDGEMENTS  \n“In the name of Allah, the most beneficent and the most merciful”  \nI praise ALLAH for his magnificent loving generosity, that has brought all of us to encourage and tell each other and who has pulled us from the darkness to the light. All respect for our holy prophet (Peace be upon him), who guided us to identify our creator. I also thank all my brothers and sister who answered ALLAH's call and have made their choice to be in the straight path of ALLAH.  \nAs always it is impossible to mention everybody who had an impact to this work, however, there are those whose spiritual support is, even more, important. I sense a deep emotion of gratefulness for my father and mother, who taught me good things and established part of my vision that truly affair in life. Their effective support and love have constantly been my strength. Their sacrifice and patience will stay my revelation throughout my life. I am also very much grateful to all my family members for their constant inspiration and encouragement.  \nMy heartfelt thanks to my wife for her moral support. She always helped me out when I got any difficulties regarding all the aspect of life. Again, I thank her for standing by my side.  \nI also take this occasion to express my deep acknowledgement and profound regards tomy guide Prof Dr. Biswajeet Pradhan for his ideal guidance, monitoring and continuous motivation during the course of this thesis. The help, blessing and guidance offered by him from time to time will support me a long way in the life journey on which I am about to embark. He formed an atmosphere that motivated innovation and shared his remarkable experiences throughout the work. Without his unflinching encouragement, it would have been impossible for me to finish this research.  \nLastly, I would be remiss in not mentioning my friends who are Professor Odey Z. Jasim, Haider Ali (Chief Executive Officer of In2Networks), Mohammed Alshakly (Chief Executive Officer of Allware Technology) and Doctor Omer Saud Azeez. Their belief in me has kept my spirits and motivation high during this process.  \nList Of Publications  \nPublished journal papers:  \n1. Ahmed, A. A., & Pradhan, B. (2019) . Vehicular traffic noise prediction and propagation modelling using neural networks and geospatial information system. Environmental monitoring and assessment, 191(3), 1-17.  \n[https://link.springer.com/article/10.1007/s10661-019-7333-3](https://link.springer.com/article/10.1007/s10661-019-7333-3)  \n2. Pradhan, B., Ahmed, A. A., Chakraborty, S., Alamri, A., & Lee, C. W. (2021) . Orthorectification of WorldView-3 Satellite Image Using Airborne Laser Scanning Data. Journal of Sensors, 2021.  \n[https://www.hindawi.com/journals/js/2021/5273549/](https://www.hindawi.com/journals/js/2021/5273549/)  \n3. Adulaimi, A. A. A., Pradhan, B., Chakraborty, S., & Al","cbCaibh3gqfSDBvw","https://ap.wps.com/l/cbCaibh3gqfSDBvw","pdf",23405531,1,221,"English","en",105,"# Acknowledgements\n# List of Publications\n## Published journal papers\n## Papers under review\n# Table of Contents\n# List of Tables\n# List of Figures\n# List of Abbreviations","[{\"question\":\"What is the main research focus of the thesis?\",\"answer\":\"The thesis focuses on modelling environmental (vehicular traffic) noise using 2D prediction and 3D propagation mapping supported by mobile GIS and machine learning–based models.\"},{\"question\":\"Which geospatial and machine learning elements are emphasized?\",\"answer\":\"The research emphasizes geospatial information system inputs and machine learning methods such as neural networks, deep learning, ensemble algorithms, and land-use regression combined with statistical regression approaches.\"},{\"question\":\"How is the thesis supported in terms of academic outputs?\",\"answer\":\"It includes a list of published journal papers and additional papers under review, covering topics like noise prediction and propagation modelling, satellite image orthorectification, and optimized deep neural network trend modelling.\"}]","2D, 3D Noise Modelling on Mobile GIS Application Through Machine Learning Based Models - 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