2023
DOI: 10.1080/10095020.2023.2184729
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Artificial intelligence for sustainable development of smart cities and urban land-use management

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Cited by 11 publications
(4 citation statements)
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“…[ 7 ] for a bibliometric analysis). They particularly highlight the crucial contributions of AI and AIoT in enhancing operational efficiency mechanisms and strategic planning methods [ 8 , 19 , 21 , 22 , 24 , 27 , 104 , 105 ]. In light of this, the conventional approaches to urban planning, which have long grappled with the dynamic properties and behaviors of complex systems [ [78] , [79] , [80] , [81] ], have served as a fertile ground and significant opportunity for the integration of AI and AIoT to deepen our understanding of the complexities inherent in urban environments.…”
Section: Results: Analysis and Synthesismentioning
confidence: 99%
“…[ 7 ] for a bibliometric analysis). They particularly highlight the crucial contributions of AI and AIoT in enhancing operational efficiency mechanisms and strategic planning methods [ 8 , 19 , 21 , 22 , 24 , 27 , 104 , 105 ]. In light of this, the conventional approaches to urban planning, which have long grappled with the dynamic properties and behaviors of complex systems [ [78] , [79] , [80] , [81] ], have served as a fertile ground and significant opportunity for the integration of AI and AIoT to deepen our understanding of the complexities inherent in urban environments.…”
Section: Results: Analysis and Synthesismentioning
confidence: 99%
“…Cooperate health requires clear health management from companies for employees' physical and mental health maintenance. Feeling good increases productivity and sufficient creativity relaxation and is targeted at implementation, leading to a competitive advantage over other companies ( 39 , 40 ).…”
Section: Challenges and Developments In The Forestrymentioning
confidence: 99%
“…A study by ref. [49] assessed the performance of a nondominated sorting genetic algorithm II (NSGA-II), PSO, and a multi-objective evolutionary algorithm (EA/D) based on solution dispersion, the diversity of the solution space, and the number of dominant solutions in Pareto-front parameters. Reportedly, PSO exhibited the best diversity of solutions, while the EA/D outperformed the other two in terms of computational time.…”
Section: Value and Frontiersmentioning
confidence: 99%
“…The authors declare no conflict of interest. Application of SA to high dimensional non-linear multi-objective multisite land allocation [39] Improved knowledge-informed GA for multi-objective land use allocation BLI-Heuristic algorithms [29] Modified NSGA-II BLI-Sustainable development [108] Probabilistic-based gradient multi-objective land use optimization BLI-Gradient methods in optimization [50] Validity and accuracy comparison b/n various algorithms in land use allocation (including SA) CRC-What SA it is and its application [49] Application of particle swarm optimization for multi-objective urban land use optimization BLI-Heuristic algorithm [104] Application of an improved artificial immune system for multi-objective land use allocation BLI-Heuristic algorithms [109] Application of hybrid heuristic algorithms to multi-objective land use suitability assessment of the quadratic assignment problem BLI-Heuristic algorithms [110] Multi-objective optimization model to consider transportation, formulated as mixed-integer programing BLI-Integer programing [80] Improved artificial bee colony algorithm to solve spatial problems BLI-Heuristic algorithms [97] Application of GA and game theory to solve land allocation problems BLI-Heuristic algorithms [36] Simulating optimal multi-objective land use Applying multi-agent system and particle swarm [111] Urban growth boundary determination based on a multi-objective land use optimization applying a Pareto-front degradation searching strategy where lands were defined as agents CRC-Application of agent in land use optimization [112] Collaborative optimal allocation of urban land to determine the growth boundary of urban agglomeration BLI-The difficulty of transforming optimal land use structures into spatial layout [113] An agent-based optimization of water allocation (market) wherein farmers were represented as an agricultural agent CRC-Application of agent in land use optimization [114] Linking agent-based modeling with the territorial life cycle assessment in land use planning BLI-Complexity of spatial and temporal dynamics of territorial transformation [115] Optimizing deep underground infrastructure layouts based on a multi-agent system where each DUI is represented by an agent CRC-The SE of multi-agent systems [116] Land use simulation (optimization) using CLUMondo mode BLI-Complexity of quantifying conflicting interests; Use of fractal dimension; Sensitivity of complex landscape patch boundary to human disturbance [117] Use of gray multi-objective optimization and Patch generating land use simulation in land use optimization (hybrid methods) BLI-The relationship of land use structure optimization and sustainable development…”
Section: Conflicts Of Interestmentioning
confidence: 99%