Enhancing agent-based models with artificial intelligence for complex decision making

Abdulkareem, Shaheen A.

doi:10.3990/1.9789036547482

Cited by 2 publications

(3 citation statements)

References 164 publications

(225 reference statements)

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“…This route can utilize several ML methods sequentially for different tasks (Kaack, Chen, & Morgan, 2019;Liu et al, 2018). ML and other modelling can also be combined (Abdulkareem, 2019). Simple back-of-the-envelope models relying on identified mechanical relations, regression techniques (Jean et al, 2016;Liu et al, 2018), as well as fully-fledged systems dynamics or agent-based models (Toole et al, 2015), use the intermediary metrics as inputs.…”

Section: Generating Climate Semanticsmentioning

confidence: 99%

Machine learning for geographically differentiated climate change mitigation in urban areas

Milojevic-Dupont

Creutzig

2021

Sustainable Cities and Society

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Artificial intelligence and machine learning are transforming scientific disciplines, but their full potential for climate change mitigation remains elusive. Here, we conduct a systematic review of applied machine learning studies that are of relevance for climate change mitigation, focusing specifically on the fields of remote sensing, urban transportation, and buildings. The relevant body of literature spans twenty years and is growing exponentially. We show that the emergence of big data and machine learning methods enables climate solution research to overcome generic recommendations and provide policy solutions at urban, street, building and household scale, adapted to specific contexts, but scalable to global mitigation potentials. We suggest a meta-algorithmic architecture and framework for using machine learning to optimize urban planning for accelerating, improving and transforming urban infrastructure provision. climate change mitigation a distant goal, but also making comparisons between local-scale studies difficult (Lamb, Callaghan, Creutzig, Khosla, & Minx, 2018). The IPCC's AR5 reports knowledge gaps on urban climate action (IPCC, 2014): there is too little understanding of the magnitude of the emissions reductions from altering urban form, and emissions savings from integrated infrastructure and land use planning. New analyses are required both to understand relationships (

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Section: Generating Climate Semanticsmentioning

confidence: 99%

Machine learning for geographically differentiated climate change mitigation in urban areas

Milojevic-Dupont

Creutzig

2021

Sustainable Cities and Society

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“…Recommendations like broadening primary channels, grassing of yards, capturing rainwater, and building water retention ponds, were made from this exercise and other studies (Githinji, 2014;Nadraiqere, 2014;Pérez Molina, 2014;Sliuzas et al, 2013). KCCA treats these issues with much urgency that they feature under the 'City Resilience and Drainage Management' strategic plan -2019(KCCA, 2014. They are also in line with Sustainable Development Goal number 11 -"to make cities and human settlements inclusive, safe, resilient and sustainable" (Sustainable Development Solutions Network (SDSN), 2014).…”

Section: Questionnairementioning

confidence: 99%

“…These are powerful visualisation, negotiation, and awareness building tools that can be used in flood risk management multistakeholder meetings, for example, settlement fora in the case of Uganda. Using the PMT framework, recent work (Abdulkareem, 2019) has focused on this but applied it to disease spread.…”

Section: Limitations and Future Workmentioning

confidence: 99%

Understanding flood risk perceptions and motivations for damage mitigation : the case of Kampala, Uganda

Chereni¹

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This work was not going to be possible without the help of local contacts in Kampala. Thank you, Professor Shuaib Lwasa, for connecting me to a great team of research assistants and helping in the first briefing session in which we discussed translation and logistic issues. I would also like to appreciate my team of research assistants in Kampala -Hakimu, Teddy, Isaac K, Isaac N, Susan, Juliet, Daniel, and Lilian. Thank you for the sterling job you did in administering questionnaires that needed translation 'on the fly,' in places that either had poor accessibility or required walking for long distances on foot. Hakimu, thank you once more for sharing with me the Uganda shapefiles.The KCCA deserves a big applause for allowing me to do this study and also because some of their employees participated in my interviews. This work would not have succeeded without gaining access to the case study areas. Special thanks to Harriet, Isaac, Jude, and Abel. Flavia, thank you so much for the Uganda shapefiles which you shared with us in 2015 -they were again very helpful in this project.Community leaders in Bwaise III, Natete, and Ntinda permitted us to carry out the study in their areas and I am grateful for their cooperation and understanding. Special mention goes to the Slum Dwellers Federation committee in Bwaise III for introducing research assistants to the community and helping them to find walkways in the area. In Natete, the Chairperson for Central B helped in the same manner and I would like to thank him for the fantastic job! ii My sincere appreciation also goes to my wife, Emerita who, over and above the usual support and encouragement, had to work to keep us afloat financially, when changes in the economy in Zimbabwe resulted in a drastic reduction in our income in value terms. Words fail to adequately express my appreciation for you Emerita.The PhD journey can be very lonely, but that was not the case with me, thanks to my colleagues who kept me company in diverse ways. Special mention goes to my officemates -Adish, Lydia, Rosa, Zaid, Deepshika, Sven, Sergio, and Vicky. Thank you, guys, for sharing ideas and resources, and for the encouragement. I would also like to appreciate Alice -having graduated with the same MSc. Class and going ahead to PhD studies in the same department meant that we exchanged notes at various stages of our PhD research projects.To reach this level, there are many stages that I accomplished. I would, therefore, like to appreciate all the people who contributed to my entire education career to date. I would like to thank my Brother, Dr Admire, for being an inspiration and for encouraging me throughout my studies. Special mention goes to my parents who sacrificed their resources and energy to send me to school. I still vividly remember how my mother Mary sold her only goat when I was sent away from school because of school fees, in my 4th year in secondary school. I will also not forget how my father Jameson Alex, would travel distances, buying and selling, even though he was slowly losin...

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Enhancing agent-based models with artificial intelligence for complex decision making

Cited by 2 publications

References 164 publications

Machine learning for geographically differentiated climate change mitigation in urban areas

Machine learning for geographically differentiated climate change mitigation in urban areas

Understanding flood risk perceptions and motivations for damage mitigation : the case of Kampala, Uganda

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