IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i16p7203-d1461320.html
   My bibliography  Save this article

Ada-XG-CatBoost: A Combined Forecasting Model for Gross Ecosystem Product (GEP) Prediction

Author

Listed:
  • Yang Liu

    (College of Information Engineering, Shenyang University, Shenyang 110044, China)

  • Tianxing Yang

    (College of Information Engineering, Shenyang University, Shenyang 110044, China)

  • Liwei Tian

    (College of Information Engineering, Shenyang University, Shenyang 110044, China)

  • Bincheng Huang

    (College of Information Engineering, Shenyang University, Shenyang 110044, China)

  • Jiaming Yang

    (College of Information Engineering, Shenyang University, Shenyang 110044, China)

  • Zihan Zeng

    (Zhou Enlai School of Government and Management, Nankai University, Tianjin 300350, China)

Abstract

The degradation of the ecosystem and the loss of natural capital have seriously threatened the sustainable development of human society and economy. Currently, most research on Gross Ecosystem Product (GEP) is based on statistical modeling methods, which face challenges such as high modeling difficulty, high costs, and inaccurate quantitative methods. However, machine learning models are characterized by high efficiency, fewer parameters, and higher accuracy. Despite these advantages, their application in GEP research is not widespread, particularly in the area of combined machine learning models. This paper includes both a GEP combination model and an explanatory analysis model. This paper is the first to propose a combined GEP prediction model called Ada-XGBoost-CatBoost (Ada-XG-CatBoost), which integrates the Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost) algorithms, and SHapley Additive exPlanations (SHAP) model. This approach overcomes the limitations of single-model evaluations and aims to address the current issues of inaccurate and incomplete GEP assessments. It provides new guidance and methods for enhancing the value of ecosystem services and achieving regional sustainable development. Based on the actual ecological data of a national city, data preprocessing and feature correlation analysis are carried out using XGBoost and CatBoost algorithms, AdaGrad optimization algorithm, and the Bayesian hyperparameter optimization method. By selecting the 11 factors that predominantly influence GEP, training the model using these selected feature datasets, and optimizing the Bayesian parameters, the error gradient is then updated to adjust the weights, achieving a combination model that minimizes errors. This approach reduces the risk of overfitting in individual models and enhances the predictive accuracy and interpretability of the model. The results indicate that the mean squared error (MSE) of the Ada-XG-CatBoost model is reduced by 65% and 70% compared to the XGBoost and CatBoost, respectively. Additionally, the mean absolute error (MAE) is reduced by 4.1% and 42.6%, respectively. Overall, the Ada-XG-CatBoost combination model has a more accurate and stable predictive performance, providing a more accurate, efficient, and reliable reference for the sustainable development of the ecological industry.

Suggested Citation

  • Yang Liu & Tianxing Yang & Liwei Tian & Bincheng Huang & Jiaming Yang & Zihan Zeng, 2024. "Ada-XG-CatBoost: A Combined Forecasting Model for Gross Ecosystem Product (GEP) Prediction," Sustainability, MDPI, vol. 16(16), pages 1-19, August.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:16:p:7203-:d:1461320
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/16/7203/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/16/7203/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Madhar M. Taamneh & Salah Taamneh & Ahmad H. Alomari & Musab Abuaddous, 2023. "Analyzing the Effectiveness of Imbalanced Data Handling Techniques in Predicting Driver Phone Use," Sustainability, MDPI, vol. 15(13), pages 1-20, July.
    2. Hamza Bouguerra & Salah Eddine Tachi & Hamza Bouchehed & Gordon Gilja & Nadir Aloui & Yacine Hasnaoui & Abdelmalek Aliche & Saâdia Benmamar & Jose Navarro-Pedreño, 2023. "Integration of High-Accuracy Geospatial Data and Machine Learning Approaches for Soil Erosion Susceptibility Mapping in the Mediterranean Region: A Case Study of the Macta Basin, Algeria," Sustainability, MDPI, vol. 15(13), pages 1-23, June.
    3. Hasna Hissou & Said Benkirane & Azidine Guezzaz & Mourade Azrour & Abderrahim Beni-Hssane, 2023. "A Novel Machine Learning Approach for Solar Radiation Estimation," Sustainability, MDPI, vol. 15(13), pages 1-21, July.
    4. Costanza, Robert & de Groot, Rudolf & Braat, Leon & Kubiszewski, Ida & Fioramonti, Lorenzo & Sutton, Paul & Farber, Steve & Grasso, Monica, 2017. "Twenty years of ecosystem services: How far have we come and how far do we still need to go?," Ecosystem Services, Elsevier, vol. 28(PA), pages 1-16.
    5. Nur Mohamed Muse & Gokmen Tayfur & Mir Jafar Sadegh Safari, 2023. "Meteorological Drought Assessment and Trend Analysis in Puntland Region of Somalia," Sustainability, MDPI, vol. 15(13), pages 1-23, July.
    6. Xiaobo Zhu & Honglin He & Mingguo Ma & Xiaoli Ren & Li Zhang & Fawei Zhang & Yingnian Li & Peili Shi & Shiping Chen & Yanfen Wang & Xiaoping Xin & Yaoming Ma & Yu Zhang & Mingyuan Du & Rong Ge & Na Ze, 2020. "Estimating Ecosystem Respiration in the Grasslands of Northern China Using Machine Learning: Model Evaluation and Comparison," Sustainability, MDPI, vol. 12(5), pages 1-17, March.
    7. Lars Ole Hjelkrem & Petter Eilif de Lange, 2023. "Explaining Deep Learning Models for Credit Scoring with SHAP: A Case Study Using Open Banking Data," JRFM, MDPI, vol. 16(4), pages 1-19, April.
    8. Min Cheng & Binbin Huang & Lingqiao Kong & Zhiyun Ouyang, 2019. "Ecosystem Spatial Changes and Driving Forces in the Bohai Coastal Zone," IJERPH, MDPI, vol. 16(4), pages 1-17, February.
    9. Jiang, Wei & Wu, Tong & Fu, Bojie, 2021. "The value of ecosystem services in China: A systematic review for twenty years," Ecosystem Services, Elsevier, vol. 52(C).
    10. Sameh Mahjoub & Sami Labdai & Larbi Chrifi-Alaoui & Bruno Marhic & Laurent Delahoche, 2023. "Short-Term Occupancy Forecasting for a Smart Home Using Optimized Weight Updates Based on GA and PSO Algorithms for an LSTM Network," Energies, MDPI, vol. 16(4), pages 1-18, February.
    11. Jianfang Liu & Qian Zhang & Qianyi Wang & Yaoping Lv & Yingqi Tang, 2023. "Gross Ecosystem Product Accounting of a Globally Important Agricultural Heritage System: The Longxian Rice–Fish Symbiotic System," Sustainability, MDPI, vol. 15(13), pages 1-16, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. van der Hoff, Richard & Nascimento, Nathália & Fabrício-Neto, Ailton & Jaramillo-Giraldo, Carolina & Ambrosio, Geanderson & Arieira, Julia & Afonso Nobre, Carlos & Rajão, Raoni, 2022. "Policy-oriented ecosystem services research on tropical forests in South America: A systematic literature review," Ecosystem Services, Elsevier, vol. 56(C).
    2. Hui Yang & Jiansheng Cao & Xianglong Hou, 2023. "Study on the Evaluation and Assessment of Ecosystem Service Spatial Differentiation at Different Scales in Mountainous Areas around the Beijing–Tianjin–Hebei Region, China," IJERPH, MDPI, vol. 20(2), pages 1-21, January.
    3. Aryal, Kishor & Maraseni, Tek & Apan, Armando, 2023. "Examining policy−institution−program (PIP) responses against the drivers of ecosystem dynamics. A chronological review (1960–2020) from Nepal," Land Use Policy, Elsevier, vol. 132(C).
    4. Liu, Duan & Tang, Runcheng & Xie, Jun & Tian, Jingjing & Shi, Rui & Zhang, Kai, 2020. "Valuation of ecosystem services of rice–fish coculture systems in Ruyuan County, China," Ecosystem Services, Elsevier, vol. 41(C).
    5. Yajing Shao & Xuefeng Yuan & Chaoqun Ma & Ruifang Ma & Zhaoxia Ren, 2020. "Quantifying the Spatial Association between Land Use Change and Ecosystem Services Value: A Case Study in Xi’an, China," Sustainability, MDPI, vol. 12(11), pages 1-20, May.
    6. Pietrzyk-Kaszyńska, Agata & Olszańska, Agnieszka & Rechciński, Marcin & Tusznio, Joanna & Grodzińska-Jurczak, Małgorzata, 2022. "Divergent or convergent? Prioritization and spatial representation of ecosystem services as perceived by conservation professionals and local leaders," Land Use Policy, Elsevier, vol. 119(C).
    7. Robbie Maris & Mark Holmes, 2023. "Economic Growth Theory and Natural Resource Constraints: A Stocktake and Critical Assessment," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 56(2), pages 255-268, June.
    8. Joel C. Creed & Laura Sol Aranda & Júlia Gomes de Sousa & Caio Barros Brito do Bem & Beatriz Sant’Anna Vasconcelos Marafiga Dutra & Marianna Lanari & Virgínia Eduarda de Sousa & Karine M. Magalhães & , 2023. "A Synthesis of Provision and Impact in Seagrass Ecosystem Services in the Brazilian Southwest Atlantic," Sustainability, MDPI, vol. 15(20), pages 1-19, October.
    9. Wanxu Chen & Guangqing Chi & Jiangfeng Li, 2020. "Ecosystem Services and Their Driving Forces in the Middle Reaches of the Yangtze River Urban Agglomerations, China," IJERPH, MDPI, vol. 17(10), pages 1-19, May.
    10. O'Sullivan, Jane N., 2020. "The social and environmental influences of population growth rate and demographic pressure deserve greater attention in ecological economics," Ecological Economics, Elsevier, vol. 172(C).
    11. Nicolás Ruiz, Néstor & Suárez Alonso, María Luisa & Vidal-Abarca, María Rosario, 2021. "Contributions of dry rivers to human well-being: A global review for future research," Ecosystem Services, Elsevier, vol. 50(C).
    12. Moreno-Llorca, R. & Vaz, A.S. & Herrero, J. & Millares, A. & Bonet-García, F.J. & Alcaraz-Segura, D., 2020. "Multi-scale evolution of ecosystem services’ supply in Sierra Nevada (Spain): An assessment over the last half-century," Ecosystem Services, Elsevier, vol. 46(C).
    13. Xiaoyu Li & Shudan Gong & Qingdong Shi & Yuan Fang, 2023. "A Review of Ecosystem Services Based on Bibliometric Analysis: Progress, Challenges, and Future Directions," Sustainability, MDPI, vol. 15(23), pages 1-18, November.
    14. Daniels, Silvie & Bellmore, J. Ryan & Benjamin, Joseph R. & Witters, Nele & Vangronsveld, Jaco & Van Passel, Steven, 2018. "Quantification of the Indirect Use Value of Functional Group Diversity Based on the Ecological Role of Species in the Ecosystem," Ecological Economics, Elsevier, vol. 153(C), pages 181-194.
    15. Lau, Jacqueline D. & Hicks, Christina C. & Gurney, Georgina G. & Cinner, Joshua E., 2018. "Disaggregating ecosystem service values and priorities by wealth, age, and education," Ecosystem Services, Elsevier, vol. 29(PA), pages 91-98.
    16. Shujun Liu & Xinzhuan Yao & Degang Zhao & Litang Lu, 2021. "Evaluation of the ecological benefits of tea gardens in Meitan County, China, using the InVEST model," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(5), pages 7140-7155, May.
    17. Dai, Xuhuan & Li, Bo & Zheng, Hua & Yang, Yanzheng & Yang, Zihan & Peng, Chenchen, 2023. "Can sedentarization decrease the dependence of pastoral livelihoods on ecosystem services?," Ecological Economics, Elsevier, vol. 203(C).
    18. Egor Selivanov & Petra Hlaváčková, 2021. "Methods for monetary valuation of ecosystem services: A scoping review," Journal of Forest Science, Czech Academy of Agricultural Sciences, vol. 67(11), pages 499-511.
    19. Wei Jiang & Rainer Marggraf, 2021. "Making Intangibles Tangible: Identifying Manifestations of Cultural Ecosystem Services in a Cultural Landscape," Land, MDPI, vol. 11(1), pages 1-14, December.
    20. Xinxin Fu & Xiaofeng Wang & Jitao Zhou & Jiahao Ma, 2021. "Optimizing the Production-Living-Ecological Space for Reducing the Ecosystem Services Deficit," Land, MDPI, vol. 10(10), pages 1-17, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2024:i:16:p:7203-:d:1461320. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.