A whale optimization algorithm-based cellular automata model for urban expansion simulation

TL;DR: This study uses machine learning algorithms to forecast urban land expansion and its impacts on the thermal environment in the Pearl River Delta, predicting increased land surface temperatures, urban heat island effect, and thermal field variance index by 2030.

TL;DR: In this article , a novel hermit crab optimization algorithm (the HCOA) is introduced, which combines the optimal search and historical path search to balance the depth and breadth searches.

TL;DR: This study proposes a novel framework for urban growth boundary (UGB) delineation in urban agglomerations by coupling urban interactions and ecological conservation, improving UGBs' efficiency and practicality in rapidly developing areas.

TL;DR: This paper analyzes a particle's trajectory as it moves in discrete time, then progresses to the view of it in continuous time, leading to a generalized model of the algorithm, containing a set of coefficients to control the system's convergence tendencies.

TL;DR: A meta-analysis of 326 studies that have used remotely sensed images to map urban land conversion suggests that contemporary urban expansion is related to a variety of factors difficult to observe comprehensively at the global level, including international capital flows, the informal economy, land use policy, and generalized transport costs.

TL;DR: This paper investigates the accuracy of CNNs under constrained time cost, and presents an architecture that achieves very competitive accuracy in the ImageNet dataset, yet is 20% faster than “AlexNet” [14] (16.0% top-5 error, 10-view test).

TL;DR: A cellular automaton is developed to model the spatial structure of urban land use over time and to link the results directly to general theories of structural evolution.