A comprehensive review of urban growth studies and predictions using the Sleuth model
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2024.15.2.48Keywords:
Urban growth, Urban growth prediction, SLEUTH, CA algorithm, Spatial analysisDimensions Badge
Issue
Section
License
Copyright (c) 2024 The Scientific Temper

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Urban growth is a complex phenomenon It has been the subject of in-depth research for the last few years. There are various models used to measure and simulate urban growth. Most of these methods are founded on GIS & RS techniques coupled with the CA algorithm, as only these tools and techniques have the capabilities to conduct spatiotemporal studies, manage spatiotemporal dynamics, and provide a detailed depiction and modeling from the bottom-up tactic. Recently, the slope, land use, exclusion, urbanization, transportation, and hill shade (SLEUTH) model has been the most commonly used model. It is easily accessible because it is open source; moreover, its source code is also easily accessible. The SLEUTH model’s name alludes to the necessary inputs —slope, land use, excluded area, urban extension, transportation, network and hillshade. The model has been used in many cities and has proven to be efficient. The present review paper reviews the past literature pertaining to urban development and prediction to further support the research on urban planning, urban growth and prediction.Abstract
How to Cite
Downloads
Similar Articles
- Chirag Darji, Rajesh Chauhan, Views of undergraduates on Vikshit Bharat@2047 , The Scientific Temper: Vol. 15 No. spl-2 (2024): The Scientific Temper
- S. Aasha, R. Sugumar, Lightweight Feature Selection Method using Quantum Statistical Ranking and Hybrid Beetle-Bat Optimization for Smart Farming , The Scientific Temper: Vol. 16 No. 09 (2025): The Scientific Temper
- Milindkumar N. Dandale, Amar P. Yadav, P. S. K. Reddy, Seema G. Kadu, Madhusudana T, Manthan S. Manavadaria, Deep learning enhanced drug discovery for novel biomaterials in regenerative medicine utilizing graph neural network approach for predicting cellular responses , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
- Shivani Tank, Isolation, Characterization and Exploring the Biotechnological Potential of Halophiles , The Scientific Temper: Vol. 16 No. Spl-1 (2025): The Scientific Temper
- Jyoti Vishwakarma, Sunil Kumar, Navigating the Skies: An Analysis of ESG Practices in the Airline Industry , The Scientific Temper: Vol. 17 No. 01 (2026): The Scientific Temper
- Jayalakshmi K., M. Prabakaran, Feature selection in HR analytics: A hybrid optimization approach with PSO and GSO , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Payal Saxena, Sustainable finance – A master key to sustainable development , The Scientific Temper: Vol. 16 No. Spl-2 (2025): The Scientific Temper
- Tara K. Sharma, Problems and prospects of tourism financing in Sikkim , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Divya R., Vanathi P. T., Harikumar R., An optimized cardiac risk levels classifier based on GMM with min- max model from photoplethysmography signals , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Surendra Singh Bisht, Saurabh Charaya, Rachna Mehta, A Comparative and Hybrid Machine Learning Framework for IoT-Based Predictive Maintenance of Rotating Machinery , The Scientific Temper: Vol. 17 No. 02 (2026): The Scientific Temper
<< < 15 16 17 18 19 20 21 22 23 24 > >>
You may also start an advanced similarity search for this article.

