Assessing the accuracy of sensitivity analysis: an application for a cellular automata model of Bogota’s urban wetland changes

Cuellar, Yenny; Pérez, Liliana

doi:10.1080/10106049.2023.2186491

Cited by 6 publications

(4 citation statements)

References 32 publications

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“…The cell neighborhood is determined by a filter and the closer the distance between the nuclear cell and the neighbor, the higher the influence weight. In the CA implementation, a contiguity filter of 5 × 5 pixels with 30 m × 30 m spatial resolution is adopted as suggested by [55]. By combining the weight with the transition probability, the next potential state of the adjacent cells is derived.…”

Section: Cellular Automata (Ca) Lulc Change Prediction Modelmentioning

confidence: 99%

“…Similar accuracy of 84% was obtained by [54] using ANN-CA for spatiotemporal analysis and simulations of biophysical indicators under urbanization and climate change scenarios. In another study, ref [55] predicted urban land-use change in Bogota up to 2034 using the MC-ACC with an average validation value of 0.85. In comparing different machine learning models, ref [49] compared different machine learning models, ANN, SVR, RF, decision tree (DT), LR and multivariate adaptive regression splines (MARS), for the prediction of urban growth with the following results.…”

Section: Introductionmentioning

confidence: 99%

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Land-Use Change Prediction in Dam Catchment Using Logistic Regression-CA, ANN-CA and Random Forest Regression and Implications for Sustainable Land–Water Nexus

Ouma,

Nkwae,

Odirile

et al. 2024

Sustainability

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For sustainable water resource management within dam catchments, accurate knowledge of land-use and land-cover change (LULCC) and the relationships with dam water variability is necessary. To improve LULCC prediction, this study proposes the use of a random forest regression (RFR) model, in comparison with logistic regression–cellular automata (LR-CA) and artificial neural network–cellular automata (ANN-CA), for the prediction of LULCC (2019–2030) in the Gaborone dam catchment (Botswana). RFR is proposed as it is able to capture the existing and potential interactions between the LULC intensity and their nonlinear interactions with the change-driving factors. For LULCC forecasting, the driving factors comprised physiographic variables (elevation, slope and aspect) and proximity-neighborhood factors (distances to water bodies, roads and urban areas). In simulating the historical LULC (1986–2019) at 5-year time steps, RFR outperformed ANN-CA and LR-CA models with respective percentage accuracies of 84.9%, 62.1% and 60.7%. Using the RFR model, the predicted LULCCs were determined as vegetation (−8.9%), bare soil (+8.9%), built-up (+2.49%) and cropland (−2.8%), with water bodies exhibiting insignificant change. The correlation between land use (built-up areas) and water depicted an increasing population against decreasing dam water capacity. The study approach has the potential for deriving the catchment land–water nexus, which can aid in the formulation of sustainable catchment monitoring and development strategies.

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Section: Cellular Automata (Ca) Lulc Change Prediction Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Land-Use Change Prediction in Dam Catchment Using Logistic Regression-CA, ANN-CA and Random Forest Regression and Implications for Sustainable Land–Water Nexus

Ouma,

Nkwae,

Odirile

et al. 2024

Sustainability

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“…In 2018, Liang et al further expanded the model by adding the UGB (urban growth boundary) module [44], which enables effective land use simulation and UDB delineation. Due to the advantages of higher accuracy, faster processing speed, and ease of use, the FLUS model has gained widespread use in many countries such as China [45], Myanmar [46], Turkey [47], and Colombia [48]. The FLUS model consists of three components: (1) Probability-of-occurrence estimation using artificial neural network; (2) Cellular automata based on self-adaptive inertia and competition mechanism; (3) Boundary delineation based on morphological erosion and dilation.…”

Section: Flus Modelmentioning

confidence: 99%

Delineation of Urban Development Boundary and Carbon Emission Effects in Xuzhou City, China

Ji,

Li,

Geng

et al. 2023

Land

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Urban development boundary (UDB) has always served as a crucial aspect of urban sprawl research. The objective of this paper is to investigate boundary delineation and carbon emission effects. Firstly, we examined the patterns of land use changes. Additionally, this paper utilized the FLUS model and land use carbon emission calculation model to delineate UDB and calculate carbon emission effects within UDB under typical scenarios. The research results are as follows: (1) Xuzhou city witnessed a significant increase in both forest land and construction land from 2010 to 2020. (2) Under the CPS, the area of cultivated land increased by 217.05 km2 compared to the NDS. The UDB area under the NDS and CPS was 971.50 km2 and 968.99 km2, respectively. (3) Compared to the NDS, the CPS led to a net carbon emission increase of 4759.93 t within the UDB. Therefore, we should enhance the carbon sequestration and emission reduction capacity of the agricultural system. This study is beneficial for expanding the depth of research on the UDB and guiding the low-carbon urban development. We sincerely encourage readers to download this paper to improve this paper.

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“…Quantity-based models, like system dynamics models [9], predict wetland area and change rate, but lack spatial analysis. In contrast, spatial models, such as cellular automaton [10], CLUE-S [11], and multi-agent models [12], offer valuable spatial evolution simulations and geographical process visualization.…”

Section: Introductionmentioning

confidence: 99%

Dynamic monitoring and evolution analysis of wetland in Guangdong-Hong Kong-Macao Greater Bay area from 2005 to 2020

Lou,

He,

Lai

et al. 2023

International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023)

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The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) encompasses diverse and widespread wetlands, which have undergone significant changes in recent years due to various anthropogenic and natural factors. This paper investigated the dynamic changes of diverse wetlands in GBA from 2005-2020 using the Google Earth Engine (GEE). By integrating suitability atlas, the CA-Markov model was employed to predict wetland evolution in GBA. Findings revealed rapid urbanization between 2005-2015 led to swift degradation of natural wetlands. Conversely, from 2015- 2020, there was a recovery trend in natural wetlands, despite the emergence of new urban areas. By 2025, predictions suggest further recovery of natural wetlands, reduction in farmland and paddy fields, and continued urban expansion.

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Assessing the accuracy of sensitivity analysis: an application for a cellular automata model of Bogota’s urban wetland changes

Cited by 6 publications

References 32 publications

Land-Use Change Prediction in Dam Catchment Using Logistic Regression-CA, ANN-CA and Random Forest Regression and Implications for Sustainable Land–Water Nexus

Land-Use Change Prediction in Dam Catchment Using Logistic Regression-CA, ANN-CA and Random Forest Regression and Implications for Sustainable Land–Water Nexus

Delineation of Urban Development Boundary and Carbon Emission Effects in Xuzhou City, China

Dynamic monitoring and evolution analysis of wetland in Guangdong-Hong Kong-Macao Greater Bay area from 2005 to 2020

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