Skip to main content

Advertisement

SpringerLink
Log in

Snow water equivalent prediction in a mountainous area using hybrid bagging machine learning approaches

  • Research Article - Hydrology
  • Published:
Acta Geophysica Aims and scope Submit manuscript

Abstract

Snow Water Equivalent (SWE) is one of the most critical variables in mountainous watersheds and needs to be considered in water resources management plans. As direct measurement of SWE is difficult and empirical equations are highly uncertain, the present study aimed to obtain accurate predictions of SWE using machine learning methods. Five standalone algorithms of tree-based [M5P and random tree (RT)], rule-based [M5Rules (M5R)] and lazy-based learner (IBK and Kstar) and five novel hybrid bagging-based algorithms (BA) with standalone models (i.e., BA-M5P, BA-RT, BA-IBK, BA-Kstar and BA-M5R) were developed. A total of 2550 snow measurements were collected from 62 snow and rain-gauge stations located in 13 mountainous provinces in Iran. Data including ice beneath the snow (IBS), fresh snow depth (FSD), length of snow sample (LSS), snow density (SDN), snow depth (SD) and time of falling (TS) were measured. Based on the Pearson correlation between inputs (IBS, FSD, LSS, SDN, SD and TS) and output (SWE), six different input combinations were constructed. The dataset was separated into two groups (70% and 30% of the data) by a cross-validation technique for model construction (training dataset) and model evaluation (testing dataset), respectively. Different visual and quantitative metrics (e.g., Nash–Sutcliffe efficiency (NSE)) were used for evaluating model accuracy. It was found that SD had the highest correlation with SWE in Iran (r = 0.73). In general, the bootstrap aggregation (i.e., bagging) hybrid machine learning methods (BA-M5P, BA-RT, BA-IBK, BA-Kstar and BA-M5R) increased prediction accuracy when compared to each standalone method. While BA-M5R had the highest prediction accuracy (NSE = 0.83) (considering all six input variables), BA-IBK could predict SWE with high accuracy (NSE = 0.71) using only two input variables (SD and LSS). Our findings demonstrate that SWE can be accurately predicted through a variety of machine learning methods using easily measurable variables and may be useful for applications in other mountainous regions across the globe.

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

Similar content being viewed by others

References

  • Abraham S, Huynh C, Vu H (2020) Classification of soils into hydrologic groups using machine learning. Data 5(1):2

    Article  Google Scholar 

  • Adnan RM, Malik A, Kumar A, Parmar KS, Kisi O (2019) Pan evaporation modeling by three different neuro-fuzzy intelligent systems using climatic inputs. Arab J Geosci 12(20):606

    Article  Google Scholar 

  • Akgündoğdu A, Öz I, Uzunoğlu C (2019) Signal quality based power output prediction of a real distribution transformer station using M5P model tree. Electric Power Systems Research 177:106003

    Article  Google Scholar 

  • Aldous D (1991) The continuum random tree.I. Annal Prob. https://doi.org/10.1214/aop/1176990534

    Article  Google Scholar 

  • Asim Y, Shahid AR, Malik AK, Raza B (2018) Significance of machine learning algorithms in professional blogger’s classification. Comput Electr Eng 65:461–473

    Article  Google Scholar 

  • Bair EH, Abreu Calfa A, Rittger K, Dozier J (2018) Using machine learning for real-time estimates of snow water equivalent in the watersheds of Afghanistan. Cryosphere 12(5):1579–1594

    Article  Google Scholar 

  • Barzegar R, Ghasri M, Qi Z, Quilty J, Adamowski J (2019) Using bootstrap ELM and LSSVM models to estimate river ice thickness in the mackenzie river basin in the Northwest Territories. Canada J Hydrol 577:123903

    Article  Google Scholar 

  • Braga, P.L., Oliveira, A.L., Ribeiro, G.H., Meira, S.R., 2007. Bagging predictors for estimation of software project effort, 2007 International Joint Conference on Neural Networks. IEEE, pp. 1595–1600.

  • Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140

    Article  Google Scholar 

  • Breiman L (2001) Random Forests. Mach Learn 45(1):5–32

    Article  Google Scholar 

  • Breiman L, Friedman J, Stone CJ, Olshen RA (1984) Classification and regression trees. CRC Press

    Google Scholar 

  • Broxton PD, van Leeuwen WJ, Biederman JA (2019) Improving snow water equivalent maps with machine learning of snow survey and lidar measurements. Water Resour Res 55(5):3739–3757

    Article  Google Scholar 

  • Buckingham D, Skalka C, Bongard J (2015) Inductive machine learning for improved estimation of catchment-scale snow water equivalent. J Hydrol 524:311–325

    Article  Google Scholar 

  • Bui X-N, Jaroonpattanapong P, Nguyen H, Tran Q-H, Long NQ (2019a) A novel Hybrid Model for predicting Blast-induced ground vibration based on k-nearest neighbors and particle Swarm optimization. Sci Rep 9(1):1–14

    Article  Google Scholar 

  • Bui X-N, Nguyen H, Le H-A, Bui H-B, Do N-H (2019b) Prediction of blast-induced air over-pressure in Open-Pit Mine: assessment of different artificial intelligence techniques. Nat Resour Res. https://doi.org/10.1007/s11053-019-09461-0

    Article  Google Scholar 

  • Bui DT et al (2020) Enhancing nitrate and strontium concentration prediction in groundwater by using new data mining algorithm. Sci Total Environ 715:136836

    Article  Google Scholar 

  • Chatterji, S., Dhar, A., Barik, B., Sarkar, S., Basu, A., 2011. Anaphora resolution for bengali, hindi, and tamil using random tree algorithm in weka. In Proceedings of the ICON-2011.

  • Choubin B, Darabi H, Rahmati O, Sajedi-Hosseini F, Kløve B (2018) River suspended sediment modelling using the CART model: a comparative study of machine learning techniques. Sci Total Environ 615:272–281

    Article  Google Scholar 

  • Erdal HI, Karakurt O (2013) Advancing monthly streamflow prediction accuracy of CART models using ensemble learning paradigms. J Hydrol 477:119–128

    Article  Google Scholar 

  • Fang Q, Nguyen H, Bui X-N, Nguyen-Thoi T (2019) Prediction of blast-induced ground vibration in open-pit mines using a new technique based on imperialist competitive algorithm and M5Rules. Nat Resour Res. https://doi.org/10.1007/s11053-019-09577-3

    Article  Google Scholar 

  • Frank, E., Witten, I.H., 1998. Generating accurate rule sets without global optimization.

  • Ghaemi A, Rezaie-Balf M, Adamowski J, Kisi O, Quilty J (2019) On the applicability of maximum overlap discrete wavelet transform integrated with MARS and M5 model tree for monthly pan evaporation prediction. Agric Meteorol 278:107647

    Article  Google Scholar 

  • De Gregorio L et al (2019) Improving SWE estimation by fusion of snow models with topographic and remotely sensed data. Remote Sens 11(17):2033

    Article  Google Scholar 

  • Gupta A, Jain P (2017) A Map Reduce Hadoop implementation of Random Tree algorithm based on correlation feature selection. Int J Comput Appl 160(5):41–44

    Google Scholar 

  • Hernández-Lobato, D., Martínez-Muñoz, G., Suárez, A., 2006. Pruning in ordered regression bagging ensembles, The 2006 IEEE international joint conference on neural network proceedings. IEEE, pp. 1266–1273.

  • Hong H et al (2018) Landslide susceptibility mapping using J48 decision tree with adaboost, bagging and rotation forest ensembles in the guangchang area (China). CATENA 163:399–413

    Article  Google Scholar 

  • Hosseini SM, Mahjouri N (2016) Integrating support vector regression and a geomorphologic artificial neural network for daily rainfall-runoff modeling. Appl Soft Comput 38:329–345

    Article  Google Scholar 

  • Huang H-C, Cressie N (1996) Spatio-temporal prediction of snow water equivalent using the Kalman filter. Comput Stat Data Anal 22(2):159–175

    Article  Google Scholar 

  • Hulett, C., Hall, A., Qu, G., 2012. Dynamic selection of k nearest neighbors in instance-based learning, 2012 IEEE 13th international conference on information reuse & integration (IRI). IEEE, pp. 85–92.

  • Jonas T, Marty C, Magnusson J (2009) Estimating the snow water equivalent from snow depth measurements in the Swiss Alps. J Hydrol 378(1–2):161–167

    Article  Google Scholar 

  • Khosravi K, Mao L, Kisi O, Yaseen ZM, Shahid S (2018) Quantifying hourly suspended sediment load using data mining models: case study of a glacierized andean catchment in Chile. J Hydrol 567:165–179

    Article  Google Scholar 

  • Khosravi K et al (2019) Stochastic modeling of groundwater fluoride contamination: introducing lazy learners. Groundwater 58(5):723–734

    Google Scholar 

  • Khosravi K et al (2019a) Meteorological data mining and hybrid data-intelligence models for reference evaporation simulation: a case study in Iraq. Comput Electron Agric 167:105041

    Article  Google Scholar 

  • Khosravi K et al (2019b) A comparative assessment of flood susceptibility modeling using Multi-criteria decision-making analysis and Machine Learning Methods. J Hydrol 573:311–323

    Article  Google Scholar 

  • Khosravi K, Cooper JR, Daggupati P, Pham BT, Bui DT (2020) Bedload transport rate prediction: application of novel hybrid data mining techniques. J Hydrol 585:124774

    Article  Google Scholar 

  • Kirkham JD et al (2019) Near real-time measurement of snow water equivalent in the Nepal Himalayas. Front Earth Sci 7:177

    Article  Google Scholar 

  • Kişi Ö (2008) River flow forecasting and estimation using different artificial neural network techniques. Hydrol Res 39(1):27–40

    Article  Google Scholar 

  • Leisenring M, Moradkhani H (2011) Snow water equivalent prediction using bayesian data assimilation methods. Stoch Env Res Risk Assess 25(2):253–270

    Article  Google Scholar 

  • Lin L, Wang Q, Sadek AW (2016) A combined M5P tree and hazard-based duration model for predicting urban freeway traffic accident durations. Accid Anal Prev 91:114–126

    Article  Google Scholar 

  • López-Moreno JI et al (2013) Small scale spatial variability of snow density and depth over complex alpine terrain: Implications for estimating snow water equivalent. Adv Water Resour 55:40–52

    Article  Google Scholar 

  • Ma L, Zhang T, Frauenfeld OW, Ye B, Yang D, Qin D (2009) Evaluation of precipitation from the ERA-40, NCEP-1, and NCEP-2 reanalyses and CMAP-1, CMAP-2, and GPCP-2 with ground-based measurements in China. J Geophys Res. https://doi.org/10.1029/2008JD011178

    Article  Google Scholar 

  • Madhusudana C, Kumar H, Narendranath S (2016) Condition monitoring of face milling tool using K-star algorithm and histogram features of vibration signal. Eng Sci Technol Int J 19(3):1543–1551

    Google Scholar 

  • Magnusson J, Winstral A, Stordal AS, Essery R, Jonas T (2017) Improving physically based snow simulations by assimilating snow depths using the particle filter. Water Resour Res 53(2):1125–1143

    Article  Google Scholar 

  • Margulis SA, Girotto M, Cortés G, Durand M (2015) A particle batch smoother approach to snow water equivalent estimation. J Hydrometeorol 16(4):1752–1772

    Article  Google Scholar 

  • Marofi S, Tabari H, Abyaneh HZ (2011) Predicting spatial distribution of snow water equivalent using multivariate non-linear regression and computational intelligence methods. Water Resour Manage 25(5):1417–1435

    Article  Google Scholar 

  • Ménégoz M, Gallée H, Jacobi H (2013) Precipitation and snow cover in the Himalaya: from reanalysis to regional climate simulations. Hydrol Earth Syst Sci 17(10):392–393

    Article  Google Scholar 

  • Moriasi DN, Arnold JG, Van Liew MW, Binger RL, Harmel RD, Veith TL (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE 50:885–900. https://doi.org/10.13031/2013.23153

    Article  Google Scholar 

  • Nguyen H, Bui X-N (2018) Predicting blast-induced air overpressure: a robust artificial intelligence system based on artificial neural networks and random forest. Nat Resour Res. https://doi.org/10.1007/s11053-018-9424-1

    Article  Google Scholar 

  • Nguyen H, Bui X-N, Nguyen-Thoi T, Ragam P, Moayedi H (2019a) Toward a state-of-the-art of fly-rock prediction technology in open-pit mines using EANNs model. Appl Sci 9(21):4554

    Article  Google Scholar 

  • Nguyen H, Bui X-N, Tran Q-H, Mai N-L (2019b) A new soft computing model for estimating and controlling blast-produced ground vibration based on hierarchical K-means clustering and cubist algorithms. Appl Soft Comput 77:376–386. https://doi.org/10.1016/j.asoc.2019.01.042

    Article  Google Scholar 

  • Nguyen H et al (2019c) Optimizing ANN models with PSO for predicting short building seismic response. Eng Comput. https://doi.org/10.1007/s00366-019-00733-0

    Article  Google Scholar 

  • Nourani V, Kisi Ö, Komasi M (2011) Two hybrid artificial intelligence approaches for modeling rainfall–runoff process. J Hydrol 402(1–2):41–59

    Article  Google Scholar 

  • Painuli S, Elangovan M, Sugumaran V (2014) Tool condition monitoring using K-star algorithm. Expert Syst Appl 41(6):2638–2643

    Article  Google Scholar 

  • Panthong R, Srivihok A (2015) Wrapper feature subset selection for dimension reduction based on ensemble learning algorithm. Proced Comput Sci 72:162–169

    Article  Google Scholar 

  • Pérez I et al (2009) Increased APN/CD13 and acid aminopeptidase activities in head and neck squamous cell carcinoma. Head Neck: J Sci Spec Head Neck 31(10):1335–1340

    Article  Google Scholar 

  • Quinlan, J.R., 1992. Learning with continuous classes, 5th Australian joint conference on artificial intelligence. Singapore, pp. 343–348.

  • Rohrer M, Salzmann N, Stoffel M, Kulkarni AV (2013) Missing (in-situ) snow cover data hampers climate change and runoff studies in the Greater Himalayas. Sci Total Environ 468:S60–S70

    Article  Google Scholar 

  • Salih SQ et al (2019) River suspended sediment load prediction based on river discharge information: application of newly developed data mining models. Hydrol Sci J 65(4):624–637 (Just-Accepted)

    Article  Google Scholar 

  • Samadi M, Jabbari E, Azamathulla HM (2014) Assessment of M5′ model tree and classification and regression trees for prediction of scour depth below free overfall spillways. Neural Comput Appl 24(2):357–366

    Article  Google Scholar 

  • Schmucki E, Marty C, Fierz C, Lehning M (2014) Evaluation of modelled snow depth and snow water equivalent at three contrasting sites in Switzerland using SNOWPACK simulations driven by different meteorological data input. Cold Reg Sci Technol 99:27–37

    Article  Google Scholar 

  • Shang Y, Nguyen H, Bui X-N, Tran Q-H, Moayedi H (2019) A novel artificial intelligence approach to predict blast-induced ground vibration in open-pit mines based on the firefly algorithm and artificial neural network. Nat Resour Res. https://doi.org/10.1007/s11053-019-09503-7

    Article  Google Scholar 

  • Sharafati A et al (2019) The potential of novel data mining models for global solar radiation prediction. Int J Environ Sci Technol 16(11):7147–7164

    Article  Google Scholar 

  • Sharma R, Kumar S, Maheshwari R (2015) Comparative analysis of classification techniques in data mining using different datasets. Int J Comput Sci Mob Comput 4(12):125–134

    Google Scholar 

  • Shi, J., 2004. Estimation of snow water equivalence with two Ku-band dual polarization radar. IGARSS 2004. 2004 IEEE international geoscience and remote sensing symposium. IEEE, pp. 1649–1652.

  • Snauffer AM, Hsieh WW, Cannon AJ, Schnorbus MA (2018) Improving gridded snow water equivalent products in British Columbia, Canada: multi-source data fusion by neural network models. Cryosphere 12(3):891–905

    Article  Google Scholar 

  • Tabari H, Marofi S, Abyaneh HZ, Sharifi M (2010) Comparison of artificial neural network and combined models in estimating spatial distribution of snow depth and snow water equivalent in Samsami basin of Iran. Neural Comput Appl 19(4):625–635

    Article  Google Scholar 

  • Takala M et al (2011) Estimating northern hemisphere snow water equivalent for climate research through assimilation of space-borne radiometer data and ground-based measurements. Remote Sens Environ 115(12):3517–3529

    Article  Google Scholar 

  • Tappeiner U, Tappeiner G, Aschenwald J, Tasser E, Ostendorf B (2001) GIS-based modelling of spatial pattern of snow cover duration in an alpine area. Ecol Model 138(1–3):265–275

    Article  Google Scholar 

  • Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res Atmos 106(D7):7183–7192

    Article  Google Scholar 

  • Tejera Hernández DC (2015) An experimental study of K* algorithm. Int J Inform Eng Electron Bus 7(2):14–19

    Google Scholar 

  • Tikhamarine Y, Souag-Gamane D, Ahmed AN, Kisi O, El-Shafie A (2020) Improving artificial intelligence models accuracy for monthly streamflow forecasting using grey Wolf optimization (GWO) algorithm. J Hydrol 582:124435

    Article  Google Scholar 

  • Vafakhah, M., Mohseni, S.M., Mahdavi, M., Alavipanah, S., 2011. Snowmelt runoff prediction by using artificial neural network and adaptive neuro-fuzzy inference system in Taleghan watershed.

  • Vijayarani S, Muthulakshmi M (2013) Comparative analysis of bayes and lazy classification algorithms. Int J Adv Res Comput Communicat Eng 2(8):3118–3124

    Google Scholar 

  • Wang, Y., Witten, I.H., 1996. Induction of model trees for predicting continuous classes, Computer Science Working Papers. Department of Computer Science, University of Waikato, Hamilton, New Zealand, pp. 1–10.

  • Wang, Y., Witten, I., 1997. Induction of model trees for predicting continuous classes. proceedings of the poster papers of the european conference on machine learning. Prague: University of Economics, Faculty of Informatics and Statistics.

  • Wang J, Tedesco M (2007) Identification of atmospheric influences on the estimation of snow water equivalent from AMSR-E measurements. Remote Sens Environ 111(2–3):398–408

    Article  Google Scholar 

  • Williamson DF, Parker RA, Kendrick JS (1989) The box plot: a simple visual method to interpret data. Ann Intern Med 110(11):916–921

    Article  Google Scholar 

  • Yaseen ZM et al (2016) Stream-flow forecasting using extreme learning machines: a case study in a semi-arid region in Iraq. J Hydrol 542:603–614

    Article  Google Scholar 

  • Young C-C, Liu W-C, Wu M-C (2017) A physically based and machine learning hybrid approach for accurate rainfall-runoff modeling during extreme typhoon events. Appl Soft Comput 53:205–216

    Article  Google Scholar 

  • Zhan C, Gan A, Hadi M (2011) Prediction of lane clearance time of freeway incidents using the M5P tree algorithm. IEEE Trans Intell Transp Syst 12(4):1549–1557

    Article  Google Scholar 

  • Zhang X et al (2019) Novel soft computing model for predicting Blast-induced ground vibration in Open-pit mines based on particle swarm optimization and XGBoost. Nat Resour Res. https://doi.org/10.1007/s11053-019-09492-7

    Article  Google Scholar 

Download references

 Acknowledgements

The publication has been prepared with the support of the RUDN University Strategic Academic Leadership Program.

Author information

Authors and Affiliations

  1. Department of Earth and Environment, Florida International University, Miami, USA

    Khabat Khosravi

  2. Department of Watershed Management, Ferdowsi University of Mashhad, Mashhad, Iran

    Khabat Khosravi & Ali Golkarian

  3. Department of Range and Watershed Management, Faculty of Natural Resources and Earth Sciences, University of Kashan, Kashan, Iran

    Ebrahim Omidvar

  4. Department of Mechanics and Control Processes, Academy of Engineering, Peoples’ Friendship University of Russia (RUDN University), Miklukho-Maklaya Str. 6, Moscow, Russian Federation, 117198

    Javad Hatamiafkoueieh

  5. Department of GIS and RS, Yazd Branch, Islamic Azad University, Hesabi Blv, Safaeie, Yazd, Iran

    Masoud Shirali

Authors
  1. Khabat Khosravi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ali Golkarian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ebrahim Omidvar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Javad Hatamiafkoueieh
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Masoud Shirali
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Javad Hatamiafkoueieh.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interest associated with this research or manuscript.

Ethics approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Additional information

Edited by Dr. Senlin Zhu (ASSOCIATE EDITOR) / Dr. Michael Nones (CO-EDITOR-IN-CHIEF).

Appendix

Appendix

See Tables 7 and 8.

Table 7 List of all snow stations
Full size table
Table 8 Definition of each models parameter
Full size table

Rights and permissions

Springer Nature or its licensor 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

Khosravi, K., Golkarian, A., Omidvar, E. et al. Snow water equivalent prediction in a mountainous area using hybrid bagging machine learning approaches. Acta Geophys. 71, 1015–1031 (2023). https://doi.org/10.1007/s11600-022-00934-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11600-022-00934-0

Keywords

Search

Navigation