IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v107y2021i1d10.1007_s11069-021-04585-0.html
   My bibliography  Save this article

Prediction model based on the Laplacian eigenmap method combined with a random forest algorithm for rainstorm satellite images during the first annual rainy season in South China

Author

Listed:
  • Xiao-yan Huang

    (Guangxi Research Institute of Meteorological Sciences)

  • Li He

    (Guangxi Research Institute of Meteorological Sciences)

  • Hua-sheng Zhao

    (Guangxi Research Institute of Meteorological Sciences)

  • Ying Huang

    (Guangxi Research Institute of Meteorological Sciences)

  • Yu-shuang Wu

    (Guangxi Research Institute of Meteorological Sciences)

Abstract

The recent emergence of satellite detection and imaging technologies has increased the demand for the application of satellite cloud images to current weather forecasting. However, approaches based on nonlinear prediction technology to forecast satellite images are lacking, and forecasting timelines are relatively short, e.g., 1–3 h in advance. In the present study, a nonlinear dimensionality reduction approach based on Laplacian eigenmaps (LEs) was combined with a random forest (RF) algorithm to construct an intelligent computing prediction model for rainstorm satellite images obtained from the first annual rainy season (April–June) in South China from 2010 to 2018. Results showed that the proposed forecasting model based on nonlinear intelligent calculation can accurately predict the key features and trends of the development and movement of strong precipitation clouds. The predicted satellite images described by the model were also consistent with the major features of the observed satellite images. This study then used a multiple linear regression (MLR) method based on the same prediction factors to establish a model for predicting satellite images for the same modeling and forecasting samples. Comparative results of the two prediction schemes showed that the LE + RF algorithm satellite image prediction scheme yields more samples exhibiting a high correlation with observed satellite images than the MLR method. Compared with that of the proposed scheme, the amount of samples of the MLR scheme in the low-correlation area was significantly larger. In general, the nonlinear intelligent computing scheme developed in this study is superior to the MLR method for predicting satellite cloud images. Thus, the LE + RF algorithm satellite image prediction scheme provides an objective and practical method for observed satellite cloud image predictions.

Suggested Citation

  • Xiao-yan Huang & Li He & Hua-sheng Zhao & Ying Huang & Yu-shuang Wu, 2021. "Prediction model based on the Laplacian eigenmap method combined with a random forest algorithm for rainstorm satellite images during the first annual rainy season in South China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 107(1), pages 331-353, May.
    • Handle: RePEc:spr:nathaz:v:107:y:2021:i:1:d:10.1007_s11069-021-04585-0
    DOI: 10.1007/s11069-021-04585-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-021-04585-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11069-021-04585-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Nga Thi Thanh Pham & Quang Hong Nguyen & Anh Duc Ngo & Hang Thi Thu Le & Cong Tien Nguyen, 2018. "Investigating the impacts of typhoon-induced floods on the agriculture in the central region of Vietnam by using hydrological models and satellite data," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 92(1), pages 189-204, May.
    2. Anoop Kumar Mishra & Mohammad Suhail Meer & Vanganuru Nagaraju, 2019. "Satellite-based monitoring of recent heavy flooding over north-eastern states of India in July 2019," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 97(3), pages 1407-1412, July.
    3. Chongchong Qi & Andy Fourie & Xuhao Du & Xiaolin Tang, 2018. "Prediction of open stope hangingwall stability using random forests," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 92(2), pages 1179-1197, June.
    4. Guilherme Garcia Oliveira & Luis Fernando Chimelo Ruiz & Laurindo Antonio Guasselli & Claus Haetinger, 2019. "Random forest and artificial neural networks in landslide susceptibility modeling: a case study of the Fão River Basin, Southern Brazil," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 99(2), pages 1049-1073, November.
    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. Zongguo Zhang & Xianyang Qiu & Xiuzhi Shi & Zhi Yu, 2023. "Chamber roof deformation prediction and analysis of underground mining using experimental design methodologies," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 115(1), pages 757-777, January.
    2. Rui Yuan & Jing Chen, 2022. "A hybrid deep learning method for landslide susceptibility analysis with the application of InSAR data," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 114(2), pages 1393-1426, November.
    3. Mohamed Elgharib Gomah & Guichen Li & Naseer Muhammad Khan & Changlun Sun & Jiahui Xu & Ahmed A. Omar & B. G. Mousa & Marzouk Mohamed Aly Abdelhamid & M. M. Zaki, 2022. "Prediction of Strength Parameters of Thermally Treated Egyptian Granodiorite Using Multivariate Statistics and Machine Learning Techniques," Mathematics, MDPI, vol. 10(23), pages 1-21, November.
    4. Sukanta Malakar & Abhishek K. Rai & Arun K. Gupta, 2023. "Earthquake risk mapping in the Himalayas by integrated analytical hierarchy process, entropy with neural network," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(1), pages 951-975, March.
    5. Paulo Rodolpho Pereira Hader & Fábio Augusto Gomes Vieira Reis & Anna Silvia Palcheco Peixoto, 2022. "Landslide risk assessment considering socionatural factors: methodology and application to Cubatão municipality, São Paulo, Brazil," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 110(2), pages 1273-1304, January.
    6. Dandan Zhang & Juqin Shen & Pengfei Liu & Fuhua Sun, 2020. "Allocation of Flood Drainage Rights Based on the PSR Model and Pythagoras Fuzzy TOPSIS Method," IJERPH, MDPI, vol. 17(16), pages 1-19, August.
    7. Tingyu Zhang & Quan Fu & Chao Li & Fangfang Liu & Huanyuan Wang & Ling Han & Renata Pacheco Quevedo & Tianqing Chen & Na Lei, 2022. "Modeling landslide susceptibility using data mining techniques of kernel logistic regression, fuzzy unordered rule induction algorithm, SysFor and random forest," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 114(3), pages 3327-3358, December.
    8. Hamid Reza Pourghasemi & Soheila Pouyan & Mojgan Bordbar & Foroogh Golkar & John J. Clague, 2023. "Flood, landslides, forest fire, and earthquake susceptibility maps using machine learning techniques and their combination," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(3), pages 3797-3816, April.
    9. Jui-Sheng Chou & Dinh-Nhat Truong & Yonatan Che, 2020. "Optimized multi-output machine learning system for engineering informatics in assessing natural hazards," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 101(3), pages 727-754, April.
    10. Chimunhu, Prosper & Topal, Erkan & Ajak, Ajak Duany & Asad, Waqar, 2022. "A review of machine learning applications for underground mine planning and scheduling," Resources Policy, Elsevier, vol. 77(C).
    11. Xiang Zhang & Minghui Zhang & Xin Liu & Berhanu Keno Terfa & Won-Ho Nam & Xihui Gu & Xu Zhang & Chao Wang & Jian Yang & Peng Wang & Chenghong Hu & Wenkui Wu & Nengcheng Chen, 2024. "Review on the progress and future prospects of geological disasters prediction in the era of artificial intelligence," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(13), pages 11485-11525, October.
    12. Mária Barančoková & Matej Šošovička & Peter Barančok & Peter Barančok, 2021. "Predictive Modelling of Landslide Susceptibility in the Western Carpathian Flysch Zone," Land, MDPI, vol. 10(12), pages 1-28, December.
    13. Arun Rana & Qinhan Zhu & Annette Detken & Karina Whalley & Christelle Castet, 2022. "Strengthening climate-resilient development and transformation in Viet Nam," Climatic Change, Springer, vol. 170(1), pages 1-23, January.
    14. Qian Ke & Jiangshan Yin & Jeremy D. Bricker & Nicholas Savage & Erasmo Buonomo & Qinghua Ye & Paul Visser & Guangtao Dong & Shuai Wang & Zhan Tian & Laixiang Sun & Ralf Toumi & Sebastiaan N. Jonkman, 2021. "An integrated framework of coastal flood modelling under the failures of sea dikes: a case study in Shanghai," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 109(1), pages 671-703, October.
    15. Youssef El Miloudi & Younes El Kharim & Ali Bounab & Rachid El Hamdouni, 2024. "Effect of Rockfall Spatial Representation on the Accuracy and Reliability of Susceptibility Models (The Case of the Haouz Dorsale Calcaire, Morocco)," Land, MDPI, vol. 13(2), pages 1-16, February.
    16. Joško Trošelj & Han Soo Lee & Lena Hobohm, 2023. "Enhancing a Real-Time Flash Flood Predictive Accuracy Approach for the Development of Early Warning Systems: Hydrological Ensemble Hindcasts and Parameterizations," Sustainability, MDPI, vol. 15(18), pages 1-33, September.
    17. Weijun Liu & Zhixiang Liu & Zida Liu & Shuai Xiong & Shuangxia Zhang, 2023. "Random Forest and Whale Optimization Algorithm to Predict the Invalidation Risk of Backfilling Pipeline," Mathematics, MDPI, vol. 11(7), pages 1-19, March.
    18. Roland Cochard & Bien Thanh Vu & Dung Tri Ngo, 2021. "Acacia Plantation Development and the Configuration of Tree Farmers’ Agricultural Assets and Land Management—A Survey in Central Vietnam," Land, MDPI, vol. 10(12), pages 1-39, November.
    19. Linh N. Luu & Paolo Scussolini & Sarah Kew & Sjoukje Philip & Mugni Hadi Hariadi & Robert Vautard & Khiem Mai & Thang Vu & Kien Ba Truong & Friederike Otto & Gerard Schrier & Maarten K. Aalst & Geert , 2021. "Attribution of typhoon-induced torrential precipitation in Central Vietnam, October 2020," Climatic Change, Springer, vol. 169(3), pages 1-22, December.
    20. Xudong Hu & Hongbo Mei & Han Zhang & Yuanyuan Li & Mengdi Li, 2021. "Performance evaluation of ensemble learning techniques for landslide susceptibility mapping at the Jinping county, Southwest China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 105(2), pages 1663-1689, January.

    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:spr:nathaz:v:107:y:2021:i:1:d:10.1007_s11069-021-04585-0. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.