Skip to main content
SpringerLink
Log in

A data-driven approach for high accurate spatiotemporal precipitation estimation

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Precipitation is a fundamental factor affecting many fields, including freshwater reservation, flood warning and prevention, agriculture, and hydropower planning. Observation-based precipitation data usually come from two primary sources, namely gauges and satellite images. The former provides high reliability but sparse coverage, while the latter offers fine-grained data but is still inaccurate. There have been several efforts to estimate precipitation, including mathematical and probabilistic models and machine learning-based techniques. All existing solutions consider the target problem as a satellite data calibration with gauge data as complementary information. However, this approach fails to provide accurate predictive results due to the sparsity of gauges and significant errors in satellite data. This paper presents a novel precipitation estimating method that highlights the importance of gauge data, the most trustworthy data source. To be more precise, we formulate the precipitation estimation issue as a spatial prediction task with the goal of predicting rainfall data for non-monitoring locations using gauge data at the monitored sites. To this end, we propose a data-driven approach that exploits the encoder–decoder architecture, graph neural network, and the multimodal data fusion strategy. Specifically, we design an encoder that leverages a graph neural network for capturing the spatial relationship among the gauges. Meanwhile, the decoder exploits the convolutional networks to learn the temporal correlation within the historical data. Finally, we integrate satellite images and meteorological information using a multimodal data fusion based on a multilayer perceptron to enhance prediction accuracy. The experimental results show that our proposed model increases the estimation accuracy from 24.3 to 65.2% compared to the state-of-the-art.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Data availability statement

In this paper, we used the following datasets. [1.]GSMaP_MVK V6. This dataset is provided by Japan Science and Technology Center (JST) and Japan Space Research Center (JAXA). It can be downloaded from https://sharaku.eorc.jaxa.jp/GSMaP/index.htm.

[2.]PERSIANN and PERSIANN-CCS. These datasets are developed by the Center for Hydrometeorology and Remote Sensing (CHRS) at the University of California, Irvine (UCI). They can be downloaded from https://chrsdata.eng.uci.edu/.

[3.]ERA5. This dataset is the fifth generation ECMWF reanalysis for the global climate and weather, provided by Climate Data Store (CDS). It can be downloaded from https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels.

[4.]Vietnamese Gauge precipitation. This dataset is provided by Vietnam Meteorological and Hydrological Administration (VMHA). It can be downloaded from https://www.kaggle.com/datasets/khimphmminh/rain-gauge-stations-data-in-central-vietnam.

Notes

  1. The PERSIANN-CCS dataset has global coverage from \(50^\circ S\) to \(50^\circ N\) and a resolution of \(0.04^\circ \times 0.04^\circ\). The PERSIANN dataset has coverage from \(50^\circ S\) to \(50^\circ N\) with the spatial resolution of \(0.25^\circ \times 0.25^\circ\).

  2. for the Cau Lau monitoring station on the Thu Bon river basin in Quang Nam province.

References

  1. Hou A, Kakar RK, Neeck SP, Azarbarzin AA, Kummerow CD, Kojima M, Oki R, Nakamura K, Iguchi T (2014) The global precipitation measurement mission. Bull Am Meteor Soc 95:701–722

    Article  ADS  Google Scholar 

  2. Jonkman SN (2005) Global perspectives on loss of human life caused by floods. Nat Hazards 34:151–175. https://doi.org/10.1007/s11069-004-8891-3

    Article  Google Scholar 

  3. Xue M, Hang R, Liu Q, Yuan X-T, Lu X (2021) CNN-based near-real-time precipitation estimation from Fengyun-2 satellite over Xinjiang, china. Atmos Res 250:105337. https://doi.org/10.1016/j.atmosres.2020.105337

    Article  Google Scholar 

  4. Wu H, Yang Q, Liu J, Wang G (2020) A spatiotemporal deep fusion model for merging satellite and gauge precipitation in china. J Hydrol 584:124664. https://doi.org/10.1016/j.jhydrol.2020.124664

    Article  Google Scholar 

  5. Li N, Tang G, Zhao P, Hong Y, Gou Y, Yang K (2017) Statistical assessment and hydrological utility of the latest multi-satellite precipitation analysis imerg in Ganjiang river basin. Atmos Res 183:212–223. https://doi.org/10.1016/j.atmosres.2016.07.020

    Article  Google Scholar 

  6. Yamamoto MK, Shige S (2015) Implementation of an orographic/nonorographic rainfall classification scheme in the GSMaP algorithm for microwave radiometers. Atmos Res 163:36–47

    Article  Google Scholar 

  7. Li X, Chen Y, Wang H, Zhang Y (2020) Assessment of GPM imerg and radar quantitative precipitation estimation (QPE) products using dense rain gauge observations in the Guangdong-Hong Kong-Macao greater bay area, china. Atmos Res 236:104834. https://doi.org/10.1016/j.atmosres.2019.104834

    Article  CAS  Google Scholar 

  8. Nguyen-Xuan T, Ngo-Duc T, Kamimera H, Trinh-Tuan L, Matsumoto J, Inoue T, Phan-Van T (2016) The Vietnam gridded precipitation (vngp) dataset: construction and validation. SOLA 12:291–296. https://doi.org/10.2151/sola.2016-057

    Article  ADS  Google Scholar 

  9. Joyce RJ, Janowiak JE, Arkin PA, Xie P (2004) Cmorph: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J Hydrometeorol 5(3):487–503. https://doi.org/10.1175/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2

    Article  ADS  Google Scholar 

  10. Huffman GJ, Bolvin DT, Nelkin EJ, Wolff DB, Adler RF, Gu G, Hong Y, Bowman KP, Stocker EF (2007) The TRMM multisatellite precipitation analysis (TMPa): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J Hydrometeorol 8(1):38–55. https://doi.org/10.1175/JHM560.1

    Article  ADS  Google Scholar 

  11. JAXA: Global satellite mapping of precipitation (GSMaP). https://sharaku.eorc.jaxa.jp/GSMaP/guide.html. [Accessed = 10 Apr 2022]

  12. Ngo-Duc T, Matsumoto J, Kamimera H, Bui H-H (2013) Monthly adjustment of global satellite mapping of precipitation (GSMaP)) data over the Vugia and Thubon river basin in central Vietnam using an artificial neural network. Hydrol Res Lett 7(4):85–90. https://doi.org/10.3178/hrl.7.85

    Article  ADS  Google Scholar 

  13. Shige S, Kida S, Ashiwake H, Kubota T, Aonashi K (2013) Improvement of tmi rain retrievals in mountainous areas. J Appl Meteorol Climatol 52(1):242–254. https://doi.org/10.1175/JAMC-D-12-074.1

    Article  ADS  Google Scholar 

  14. Areerachakul N, Prongnuch S, Longsomboon P, Kandasamy J (2022) Quantitative precipitation estimation (QPE) rainfall from meteorology radar over chi basin. Hydrology. https://doi.org/10.3390/hydrology9100178

    Article  Google Scholar 

  15. Li M, Shao Q (2010) An improved statistical approach to merge satellite rainfall estimates and Raingauge data. J Hydrol 385(1):51–64. https://doi.org/10.1016/j.jhydrol.2010.01.023

    Article  Google Scholar 

  16. Chao L, Zhang K, Li Z, Zhu Y, Wang J, Yu Z (2018) Geographically weighted regression based methods for merging satellite and gauge precipitation. J Hydrol 558:275–289. https://doi.org/10.1016/j.jhydrol.2018.01.042

    Article  Google Scholar 

  17. Dinku T, Hailemariam K, Maidment R, Tarnavsky E, Connor S (2014) Combined use of satellite estimates and rain gauge observations to generate high-quality historical rainfall time series over Ethiopia. Int J Climatol 34(7):2489–2504. https://doi.org/10.1002/joc.3855

    Article  Google Scholar 

  18. Dong S, Wang P, Abbas K (2021) A survey on deep learning and its applications. Comput Sci Rev 40:100379. https://doi.org/10.1016/j.cosrev.2021.100379

    Article  MathSciNet  Google Scholar 

  19. Alam M, Samad MD, Vidyaratne L, Glandon A, Iftekharuddin KM (2020) Survey on deep neural networks in speech and vision systems. Neurocomputing 417:302–321. https://doi.org/10.1016/j.neucom.2020.07.053

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Ouallouche F, Lazri M, Ameur S (2018) Improvement of rainfall estimation from msg data using random forests classification and regression. Atmos Res 211:62–72. https://doi.org/10.1016/j.atmosres.2018.05.001

    Article  Google Scholar 

  21. Folino G, Guarascio M, Chiaravalloti F, Gabriele S (2019) A deep learning based architecture for rainfall estimation integrating heterogeneous data sources. In: 2019 International joint conference on neural networks (IJCNN), pp. 1–8. https://doi.org/10.1109/IJCNN.2019.8852229

  22. Shen Y, Zhao P, Pan Y, Yu J (2014) A high spatiotemporal gauge-satellite merged precipitation analysis over china. J Geophys Res Atmos 119(6):3063–3075. https://doi.org/10.1002/2013JD020686

    Article  ADS  Google Scholar 

  23. Wu Z, Zhang Y, Sun Z, Lin Q, He H (2018) Improvement of a combination of TMPA (or imerg) and ground-based precipitation and application to a typical region of the east china plain. Sci Total Environ 640–641:1165–1175. https://doi.org/10.1016/j.scitotenv.2018.05.272

    Article  ADS  CAS  PubMed  Google Scholar 

  24. Xie P, Xiong A.-Y (2011) A conceptual model for constructing high-resolution gauge-satellite merged precipitation analyses. J Geophys Res Atmos 116(D21). https://doi.org/10.1029/2011JD016118

  25. Zhang G, Tian G, Cai D, Bai R, Tong J (2021) Merging radar and rain gauge data by using spatial-temporal local weighted linear regression kriging for quantitative precipitation estimation. J Hydrol 601:126612. https://doi.org/10.1016/j.jhydrol.2021.126612

    Article  Google Scholar 

  26. Xu L, Chen N, Moradkhani H, Zhang X, Hu C (2020) Improving global monthly and daily precipitation estimation by fusing gauge observations, remote sensing, and reanalysis data sets. Water Resour Res 56(3):2019–026444. https://doi.org/10.1029/2019WR026444

    Article  Google Scholar 

  27. Kühnlein M, Appelhans T, Thies B, Nauss T (2014) Improving the accuracy of rainfall rates from optical satellite sensors with machine learning - a random forests-based approach applied to msg Seviri. Remote Sens Environ 141:129–143. https://doi.org/10.1016/j.rse.2013.10.026

    Article  ADS  Google Scholar 

  28. Min M, Bai C, Guo J, Sun F, Liu C, Wang F, Xu H, Tang S, Li B, Di D, Dong L, Li J (2019) Estimating summertime precipitation from Himawari-8 and global forecast system based on machine learning. IEEE Trans Geosci Remote Sens 57(5):2557–2570. https://doi.org/10.1109/TGRS.2018.2874950

    Article  ADS  Google Scholar 

  29. Sehad M, Lazri M, Ameur S (2017) Novel SVM-based technique to improve rainfall estimation over the mediterranean region (north of algeria) using the multispectral msg Seviri imagery. Adv Space Res 59(5):1381–1394. https://doi.org/10.1016/j.asr.2016.11.042

    Article  ADS  Google Scholar 

  30. Hsu K-L, Gao X, Sorooshian S, Gupta HV (1997) Precipitation estimation from remotely sensed information using artificial neural networks. J Appl Meteorol 36(9):1176–1190. https://doi.org/10.1175/1520-0450(1997)036<1176:PEFRSI>2.0.CO;2

    Article  Google Scholar 

  31. Hong Y, Hsu K-L, Sorooshian S, Gao X (2004) Precipitation estimation from remotely sensed imagery using an artificial neural network cloud classification system. J Appl Meteorol 43(12):1834–1853. https://doi.org/10.1175/JAM2173.1

    Article  Google Scholar 

  32. Sadeghi M, Nguyen P, Hsu K, Sorooshian S (2020) Improving near real-time precipitation estimation using a u-net convolutional neural network and geographical information. Environ Model Softw 134:104856. https://doi.org/10.1016/j.envsoft.2020.104856

    Article  Google Scholar 

  33. Tao Y, Gao X, Hsu K, Sorooshian S, Ihler A (2016) A deep neural network modeling framework to reduce bias in satellite precipitation products. J Hydrometeorol 17(3):931–945. https://doi.org/10.1175/JHM-D-15-0075.1

    Article  ADS  Google Scholar 

  34. Wang C, Xu J, Tang G, Yang Y, Hong Y (2020) Infrared precipitation estimation using convolutional neural network. IEEE Trans Geosci Remote Sens 58(12):8612–8625. https://doi.org/10.1109/TGRS.2020.2989183

    Article  ADS  Google Scholar 

  35. Pan B, Hsu K, AghaKouchak A, Sorooshian S (2019) Improving precipitation estimation using convolutional neural network. Water Resour Res 55(3):2301–2321. https://doi.org/10.1029/2018WR024090

    Article  ADS  Google Scholar 

  36. Zhang C-J, Zeng J, Wang H-Y, Ma L-M, Chu H (2020) Correction model for rainfall forecasts using the LSTM with multiple meteorological factors. Meteorol Appl 27(1):1852. https://doi.org/10.1002/met.1852

    Article  Google Scholar 

  37. Luo C, Li X, Ye Y (2021) Pfst-LSTM: A spatiotemporal LSTM model with pseudoflow prediction for precipitation nowcasting. IEEE J Select Topics Appl Earth Observ Remote Sensing 14:843–857. https://doi.org/10.1109/JSTARS.2020.3040648

    Article  ADS  Google Scholar 

  38. Yen M-C, Chen T-C, Hu H-L, Tzeng R-Y, Dinh DT, Nguyen TTT, Wong CJ (2011) Interannual variation of the fall rainfall in central Vietnam. SOLA 89A:259–270. https://doi.org/10.2151/jmsj.2011-A1

    Article  Google Scholar 

  39. AXA Global Rainfall Watch. https://sharaku.eorc.jaxa.jp/GSMaP/. [Accessed = 10 Apr 2022]

  40. ERA5 hourly data on single levels from 1979 to present. https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=form. [Accessed = 10 Apr 2022]

  41. Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60(6):84–90. https://doi.org/10.1145/3065386

    Article  Google Scholar 

  42. Sun Y, Chen Y, Wang X, Tang X (2014) Deep learning face representation by joint identification-verification. In: Proceedings of the 27th international conference on neural information processing systems - Vol. 2. NIPS’14, pp. 1988–1996. MIT Press, Cambridge

  43. Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: Proceedings of the 5th international conference on learning representations. ICLR ’17. https://doi.org/10.48550/ARXIV.1609.02907

  44. Zhu Y, Xu Y, Yu F, Liu Q, Wu S, Wang L (2020) Deep graph contrastive representation learning. In: ICML Workshop on graph representation learning and beyond. https://doi.org/10.48550/arXiv.2006.04131. https://icml.cc/Conferences/2020/ScheduleMultitrack?event=5715

  45. Fung KF, Chew KS, Huang YF, Ahmed AN, Teo FY, Ng JL, Elshafie A (2022) Evaluation of spatial interpolation methods and spatiotemporal modeling of rainfall distribution in peninsular Malaysia. Ain Shams Eng J 13(2):101571. https://doi.org/10.1016/j.asej.2021.09.001

    Article  Google Scholar 

  46. Chen F-W, Liu C-W (2012) Estimation of the spatial rainfall distribution using inverse distance weighting (IDW) in the middle of Taiwan. Paddy Water Environ 10(3):209–222. https://doi.org/10.1007/s10333-012-0319-1

    Article  Google Scholar 

  47. Paradilaga S.N, Sulistyoningsih M, Lestari R.K, Laksitaningtyas A.P (2021) Flood prediction using inverse distance weighted interpolation of k-nearest neighbor points. In: 2021 IEEE International geoscience and remote sensing symposium IGARSS, pp. 4616–4619. https://doi.org/10.1109/IGARSS47720.2021.9553774

  48. CHRS data portal. https://chrsdata.eng.uci.edu/. [Accessed = 10 Apr 2022]

  49. Robbins H, Monro S (1951) A stochastic approximation method. Annal Math Stat 22(3):400–407

    Article  MathSciNet  Google Scholar 

  50. Kingma D.P, Ba J (2015) Adam: A method for stochastic optimization. In: International Conference on Learning Representations, ICLR (Poster). https://doi.org/10.48550/arXiv.1412.6980. https://openreview.net/forum?id=8gmWwjFyLj

  51. Nusret D, Dug S (2012) Applying the inverse distance weighting and kriging methods of the spatial interpolation on the mapping the annual precipitation in Bosnia and Herzegovina

  52. Chen F-W, Liu C-W (2012) Estimation of the spatial rainfall distribution using inverse distance weighting (IDW) in the middle of Taiwan. Paddy Water Environ 10:209–222

    Article  Google Scholar 

  53. Yang Z, Hsu K, Sorooshian S, Xu X, Braithwaite D, Verbist KM (2016) Bias adjustment of satellite-based precipitation estimation using gauge observations: a case study in Chile. J Geophys Res Atmos 121(8):3790–3806

    Article  ADS  Google Scholar 

  54. Hasenauer H, Merganicova K, Petritsch R, Pietsch SA, Thornton PE (2003) Validating daily climate interpolations over complex terrain in Austria. Agric For Meteorol 119(1–2):87–107

    Article  ADS  Google Scholar 

Download references

Acknowledgements

This research was funded by Hanoi University of Science and Technology (HUST) under grant number T2022-PC-049. This research was also partially supported by NAVER Corporation within the framework of collaboration with the International Research Center for Artificial Intelligence (BKAI), School of Information and Communication Technology, HUST under projectNAVER.2022.DA07. Pham Minh Khiem and Vu Viet Hung were funded by Vingroup JSC and supported by the Master, PhD Scholarship Programme of Vingroup Innovation Foundation (VINIF), Institute of Big Data, under code VINIF.2021.Ths.BK.05 and VINIF.2022.Ths.BK.05, respectively.

Author information

Authors and Affiliations

  1. Hanoi University of Science and Technology, Hanoi, Vietnam

    Minh Khiem Pham, Phi Le Nguyen & Viet Hung Vu

  2. National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan

    Thao Nguyen Truong

  3. Northern Delta Regional Hydro-Meteorological Center, Hanoi, Vietnam

    Hoa Vo-Van

  4. University of Science and Technology of Hanoi (USTH), Vietnam Academy of Science and Technology (VAST), Hanoi, Vietnam

    Thanh Ngo-Duc

Authors
  1. Minh Khiem Pham
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Phi Le Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Viet Hung Vu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Thao Nguyen Truong
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hoa Vo-Van
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Thanh Ngo-Duc
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Phi Le Nguyen.

Ethics declarations

Conflicts of interest

The authors certify that all authors have participated in (a) conception and design, or analysis and interpretation of the data; (b) drafting the article or revising it critically for important intellectual content; and (c) approval of the final version; this manuscript has not been submitted to, nor is under review at, another journal or other publishing venue; the authors have no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file 1 (csv 80 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pham, M., Nguyen, P., Vu, V. et al. A data-driven approach for high accurate spatiotemporal precipitation estimation. Neural Comput & Applic 36, 6099–6118 (2024). https://doi.org/10.1007/s00521-023-09397-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-023-09397-w

Keywords

Search

Navigation