Predicting the impacts of urban land change on LST and carbon storage using InVEST, CA-ANN and WOA-LSTM models in Guangzhou, China

Wang, Ao; Zhang, Maomao; Kafy, Abdulla Al; Tong, Bin; Hao, Daoqing; Feng, Yuanjing

doi:10.1007/s12145-022-00875-8

Cited by 30 publications

(5 citation statements)

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“…The InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) model was developed jointly by Stanford University, the World-Wide Fund for Nature, and The Nature Conservancy. This modeling system supports environmental decision-making by simulating changes in ecosystem services under different land-cover scenarios [26,27]. In this study, the habitat quality module of the InVEST model is used to integrate the sensitivity of each land-cover type to threat factors in order to provide a quantitative assessment of habitat quality across the Bosten Lake Basin region (Figure 2).…”

Section: Invest Habitat Quality Modelmentioning

confidence: 99%

Characterization and Multi-Scenario Prediction of Habitat Quality Evolution in the Bosten Lake Watershed Based on the InVEST and PLUS Models

Wang,

Sheng,

Wang

et al. 2024

Sustainability

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Habitat quality is an important basis for human well-being and the achievement of sustainable development. Based on land-use data for the Bosten Lake Basin in 2000, 2005, 2010, 2015, and 2022, the PLUS and InVEST models are applied in this study to predict and analyze land-use changes and explore the spatial and temporal evolution characteristics of the region’s habitat quality. Additionally, we use a geographic detector model to reveal the drivers of spatial variation in habitat quality. The results show that: (1) Land use in Bosten Lake Basin is dominated by grassland and bare land, with an area share of 93.21%. Habitat quality shows a trend of degradation followed by improvement, with a spatial pattern of high in the northwest and low in the southeast. (2) Habitat quality in 2030 increased from 2022 in all cases, with a mean of 0.354 for the natural development scenario, a maximum of 0.355 for the ecological development scenario, and a minimum of 0.353 for the economic development scenario. (3) The main drivers affecting habitat quality in the Bosten Lake watershed are DEM, mean annual precipitation (MAP), and GDP per capita. X1∩X4 (0.50) and X4∩X10 (0.51) are the interaction factors with the largest dominant effect in 2000, 2010 and 2020, respectively.

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Section: Invest Habitat Quality Modelmentioning

confidence: 99%

Characterization and Multi-Scenario Prediction of Habitat Quality Evolution in the Bosten Lake Watershed Based on the InVEST and PLUS Models

Wang,

Sheng,

Wang

et al. 2024

Sustainability

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“…The impact of urban land change on land surface temperature (LST) and carbon storage (CS) in Guangzhou from 1989 to 2021 was conducted using the integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, the existing scenario of CS from 1989 to 2021 was assessed. The Cellular Automata-Artificial Neural Network (CA-ANN) and Long Short-Term Memory [6].…”

Section: Literature Reviewsmentioning

confidence: 99%

Exploring the Effects of Land Use/Land Cover (LULC) Modifications and Land Surface Temperature (LST) in Pune, Maharashtra with Anticipated LULC for 2030

2024

IJG

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Land surface temperature (LST) is a crucial parameter influencing the thermal environment of urban and natural areas. Land use/land cover (LULC) patterns significantly impact LST, highlighting the need for a comprehensive understanding of their relationship. This study delves into the intricate interplay between LULC and LST in Pune city, India, employing a spatiotemporal analysis approach. The study utilizes satellite imagery from 2000, 2010, and 2022 to map LULC changes with Maximum likelihood, support vector machine and Random tree classification and their evaluation with accuracy and Kappa coefficient. Additionally, projected LULC for 2030 is prepared to assess future LST scenarios. Winter and summer LST data are analyzed to capture the seasonal dynamics of LST-LULC interactions. Pune has undergone significant LULC transformations, with the proportion of built-up areas increasing from 7% in 2000 to 47% in 2022, while barren land has decreased from 41% to 37%. Built-up areas exhibit consistently lower LST values during winter, while barren land tends to have the highest LST. In contrast, during summer, built-up and barren areas experience higher LST values, while water bodies and vegetation display relatively lower LST values due to their cooling effects. Mean LST in Pune has increased from 34.59°C in 2000 to 39.38°C in 2022, reflecting the changing thermal dynamics associated with LULC alterations. The expansion of built-up areas has contributed to temperature increases of up to 5.5°C in specific locations, highlighting the urban heat island effect. The study's findings emphasize the need for sustainable land use planning and development strategies to mitigate the adverse impacts of urbanization on LST. Policymakers, urban planners, and environmentalists can utilize these insights to develop informed land use policies, zoning regulations, and climate-smart urban development strategies.

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“…Support vector machine (SVM) regression is also a well-known and typical ML method for determining the link between features and targets [32,[35][36][37]. Artifcial neural network (ANN), one of the most popular ML methods, has been used to solve many ML problems in diferent areas [38][39][40][41]. It is capable of identifying nonlinear patterns in the functional connection between features and targets.…”

Section: Introductionmentioning

confidence: 99%

Subseasonal Prediction of Summer Temperature in West Africa Using Artificial Intelligence: A Case Study of Senegal

Kenne,

Toure,

Logamou Seknewna

et al. 2024

International Journal of Intelligent Systems

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Despite the rapid growth of machine learning (ML) and its far-reaching applications in various fields such as healthcare, finance, and urban heat management, there are still some unresolved challenges in the field of climate change. Reliable subseasonal forecasts of summer temperatures would be a great benefit to society. Although numerical weather prediction (NWP) models are better at capturing relevant sources of predictability, such as temperatures, land, and sea surface conditions, the subseasonal potential is not fully exploited. One such challenge is accurate subseasonal temperature forecasting using cutting-edge ML technology. This study aims to assess and predict the changes in subseasonal temperature during the summer season (from March to June) in Senegal on 2-weeks time scales. Six ML techniques, including linear regression (LR), decision tree (DT), support vector machine (SVM), artificial neural network (ANN), long short-term memory (LSTM), and gated recurrent units (GRU), are used. The experiments utilize a multivariate approach by incorporating variables of the ERA-5 dataset from 1981 to 2022. The results compared all the performances of the methods to assess their overall effectiveness in forecasting air temperature (t2m) values over 2 weeks. Our analysis demonstrates that the GRU model outperforms the other ML models, achieving a Nash–Sutcliffe efficiency (NSE) score of 74.68% and a mean absolute percentage error (MAPE) of 2.51%. The GRU model effectively captures long-term dependencies and exhibits superior performance in temperature forecasting. Furthermore, a comparison between the observed and predicted values confirms the accuracy of the GRU model in aligning with actual temperature trends. Overall, this study contributes an impactful deep learning model to the field of subseasonal temperature forecasting in West Africa (Senegal), which offers local authorities the capability to anticipate climatic events and enact preventive measures accordingly.

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Predicting the impacts of urban land change on LST and carbon storage using InVEST, CA-ANN and WOA-LSTM models in Guangzhou, China

Cited by 30 publications

References 50 publications

Characterization and Multi-Scenario Prediction of Habitat Quality Evolution in the Bosten Lake Watershed Based on the InVEST and PLUS Models

Characterization and Multi-Scenario Prediction of Habitat Quality Evolution in the Bosten Lake Watershed Based on the InVEST and PLUS Models

Exploring the Effects of Land Use/Land Cover (LULC) Modifications and Land Surface Temperature (LST) in Pune, Maharashtra with Anticipated LULC for 2030

Subseasonal Prediction of Summer Temperature in West Africa Using Artificial Intelligence: A Case Study of Senegal

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