Integration of Boosted Regression Trees and Cellular Automata—Markov Model to Predict the Land Use Spatial Pattern in Hotan Oasis

Wang, Shufang; Jiao, Xiyun; Wang, Liping; Gong, Aimin; Sang, Honghui; Salahou, Mohamed Khaled; Zhang, Liudong

doi:10.3390/su12041396

Cited by 12 publications

(9 citation statements)

References 31 publications

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“…The Kappa Coefficient (KC) is a statistical measure that compares observed and expected accuracy, which would be the accuracy if predictions were made randomly. It accounts for the possibility of a correct prediction occurring by chance, providing a more robust view of the model's performance, especially in cases where class distribution is imbalanced [77,78]. Sensitivity and specificity are essential metrics for evaluating binary classification models.…”

Section: Model Evaluation and Accuracy Assessmentmentioning

confidence: 99%

Towards Improving Sustainable Water Management in Geothermal Fields: SVM and RF Land Use Monitoring

Utama,

Indriani,

Hermana

et al. 2024

J. Hum. Earth Future

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The management and monitoring of land use in geothermal fields are crucial for the sustainable utilization of water resources, as well as for striking a balance between the production of renewable energy and the preservation of the environment. This study primarily compared Support Vector Machine (SVM) and Random Forest (RF) machine learning methods, using satellite imagery from Landsat 8 and Sentinel 2 between 2021 and 2023, to monitor land use in the Patuha geothermal area. The objective is to improve sustainable water management practices by accurately categorizing different land cover types. This comparative analysis assessed the efficacy of these techniques in upholding water sustainability in geothermal regions. This study examined the application of SVM and RF machine learning techniques, with particular emphasis on parameter refinement and model assessment, to enhance land use classification accuracy. By employing Kernlab and e1071 for algorithm comparison, the research sought to produce a precise Land Use Model Map, which underscores the significance of advanced analytical techniques in environmental management. This approach was of utmost importance in improving land use monitoring and reinforcing sustainable practices. The comparative evaluation of SVM and RF methods for land use classification demonstrates the superiority of RF in terms of accuracy, stability, and precision, particularly in intricate urban settings, hence establishing it as the preferred model for tasks demanding high reliability. The application of SVM and RF for monitoring land use in geothermal areas is in alignment with Sustainable Development Goals (SDGs) 6 and 15, as it fosters sustainable water management and the conservation of ecosystems. Doi: 10.28991/HEF-2024-05-02-06 Full Text: PDF

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Section: Model Evaluation and Accuracy Assessmentmentioning

confidence: 99%

Towards Improving Sustainable Water Management in Geothermal Fields: SVM and RF Land Use Monitoring

Utama,

Indriani,

Hermana

et al. 2024

J. Hum. Earth Future

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“…Modeling and geo-visualization help visualize likely land cover transitions in advance and hence emerged as indispensable tools to ensure effective landscape management [18]. The endeavor helps identify areas likely to undergo changes, which helps in understanding potential environmental impacts [19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Environmental Consequences in the Neighbourhood of Rapid Unplanned Urbanisation in Bangalore City

Ramachandra,

Mondal,

Settur

et al. 2023

Adv Environ Eng Res

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The knowledge of landscape dynamics aids in evolving strategies for the prudent management of natural resources to sustain ecosystem services. The availability of spatiotemporal remote sensing data with advancements in artificial intelligence (AI) and machine learning (ML) algorithms has aided in assessing the ecological status in urban environments, markedly revealing complex patterns and interactions. The current communication presents landscape dynamics in the Bengaluru Urban district from 1973 to 2022 using a supervised machine learning technique based on the Random Forest algorithm with temporal Landsat data, which showed a 51.86% increase in the built-up area and a 26.28% decrease in the green cover. Rapid unplanned urbanization after globalization and the opening up of Indian markets (in Bengaluru city) has witnessed erosion in the natural surface (waterbodies and green cover) in the neighborhood, which has been impacting the health of the environment and people. Computation of fragmentation indices showed a decline of the native green cover by 177.2 sq. km. in the southern part of the district. Likely land use changes are predicted using the Cellular Automata Markov model considering the base case scenario. The analyses revealed a further possible increase in built-up to 1536.08 sq. km, a decrease in green cover by 14.32 sq. km by 2038, and the disappearance of water bodies, which highlights the need to mitigate the adverse impacts of land use changes through planned urbanization considering the environment and livelihood of local communities. The decline of heat sinks such as water bodies and green cover would contribute to an increase in the land surface temperature (LST), which would affect the microclimate of Bengaluru, highlighting the need to sustain ecosystem services to support the livelihood of local communities. Understanding the ecological significance of diverse habitat characteristics of the urban region and the prediction of likely changes in a high degree of spatial heterogeneity would assist the decision-makers in framing appropriate policies.

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“…This issue causes a more serious problem to emerge, that is, the accelerating rate of urban expansion in most parts of the world, insofar as it is predicted that the world's urban areas will have tripled between 2000 and 2030 (Khamis et al 2018;McDonnell and Hahs 2015;Southon et al 2017). The rapid urban expansion has led to a variety of complex problems, such as increasing informal settlements, air pollution, deforestation, soil erosion, and climate change (Kamusoko and Gamba 2015;Wang et al 2020;Xu et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Urban Growth Modeling Considering Educational Institutions In Qazvin, Iran: An Investigation Into The Performance of Three ANFIS Methods

Ghobadiha

Motieyan

2021

Preprint

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Due to increasing urbanization, the rapid expansion of urban spaces has become a major environmental concern over the last few decades. Therefore, modeling the urban expansion as a complex system has been scrutinized in recent years; however, determining the rules that lead to the expansion of urban areas has always been a challenging factor in this field, especially for disaggregated models like cellular automata (CA). To overcome this issue, in this research, an Adaptive Network-based Fuzzy Inference System (ANFIS) is proposed to enhance the simulation of urban growth through the automatic production of transition rules. The ANFIS can be associated with several inputs division methods, such as ANFIS accompanied by grid partitioning (ANFIS-GP), subtractive clustering (ANFIS-SC), and fuzzy c-means clustering (ANFIS-FCM). Hence, twenty-two ANFIS models based on Landsat images for the time interval from 2000 to 2010 and using different division methods were trained to investigate their effect on the efficiency of ANFIS in urban growth modeling. To examine the efficiency, the Cellular Automata-based Markov Chain (CA-MC) as a popular method was developed, and the simulation accuracy of CA-MC and the most accurate ANFIS models were obtained through comparison with observed data. The most accurate ANFIS-SC model had a Kappa of 0.76 and an overall accuracy of 93.41% for the 2019 simulated map. The results from this study reveal that the ANFIS model is effective at simulating urban expansion and the ANFIS-SC is superior to CA-MC, ANFIS-GP, and ANFIS-FCM models in urban expansion modeling.

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Integration of Boosted Regression Trees and Cellular Automata—Markov Model to Predict the Land Use Spatial Pattern in Hotan Oasis

Cited by 12 publications

References 31 publications

Towards Improving Sustainable Water Management in Geothermal Fields: SVM and RF Land Use Monitoring

Towards Improving Sustainable Water Management in Geothermal Fields: SVM and RF Land Use Monitoring

Environmental Consequences in the Neighbourhood of Rapid Unplanned Urbanisation in Bangalore City

Urban Growth Modeling Considering Educational Institutions In Qazvin, Iran: An Investigation Into The Performance of Three ANFIS Methods

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