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Smart city article details
| Title | Sustainable Smart City Application Based On Machine Learning: A Case Study Example From The Province Of Tekirdağ, Turkey |
|---|---|
| ID_Doc | 54038 |
| Authors | Yılmaz S.; Oral H.V.; Saygın H. |
| Year | 2024 |
| Published | Urban Sustainability, Part F3698 |
| DOI | http://dx.doi.org/10.1007/978-981-99-9014-6_6 |
| Abstract | This study focuses on the city and risk-hazard interaction, one of the most significant issues of the twenty-first century. Today’s cities have evolved into sizable risk pools due to the urbanization trend, which intensified particularly following the Industrial Revolution and persisted, with more than half of the world’s population residing in urban areas in 2011. Due to this, the theoretical basis of the research is that a machine learning-based strategy for building smart cities can minimize or eliminate current and future potential risks and hazards in urban areas. Tekirdağ in Turkey, which is affected by natural risks and hazards like earthquakes, floods, and tsunamis, as well as human and technological risks and hazards because of population movement, industrialization, and its location on major transportation lines, has been selected as the pilot city to test the hypothesis. The study’s methodology is focused on machine learning, smart cities, and participatory approaches. Data sets will first be compiled through historical and institutional archives, field research, and in-person interviews with representatives of pertinent institutions. Then, a digital system built on machine learning and in accordance with project-specific smart city components will be created. The data sets will be uploaded to the established digital system, where it will be possible to calculate the likelihood that a risk will evolve into a hazard and the potential effects that existing hazards may have. These chances that the digital system will offer as an output will be assessed in light of the obligations of the pertinent institutions and organizations at the pilot province level regarding risk reduction and vulnerability minimization. Thus, the study seeks to accomplish two key goals. First and foremost, it aims to address all environmental risks and hazards at the level of the pilot province with an integrated strategy and to efficiently monitor the performance of local institutions’ and organizations’ obligations. In case comparable circumstances arise, the machine learning-based system is hoped to offer warning information for future hazards. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. |
| Author Keywords | Digitalization; Environmental sustainability; Machine learning; Participant Approach; Smart City |
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