Skip to main content
SpringerLink
Log in

Evaluating different machine learning algorithms for snow water equivalent prediction

  • Research Article
  • Published:
Earth Science Informatics Aims and scope Submit manuscript

Abstract

The purpose of current study is to predict Snow Water Equivalent (SWE) in Sohrevard watershed, Iran, using different machine learning algorithms such as Bayesian Artificial Neural Network (BANN), Support Vector Machine (SVM), Cubist and Random Forest (RF) with Latin Hypercube Sampling (LHS). In this regard, nine geo-environmental variables—altitude, slope, eastness, profile curvature, plan curvature, solar radiation, Topographic Position Index (TPI), Topographic Wetness Index (TWI) and wind exposition index—were used as SWE influencing factors. Based on the results obtained from the error metrics, the RF algorithm (train and testing stages, r = 0.96 and 0.76; Root Mean Square Error (RMSE) = 2.54 and 5.46 cm; Mean Absolute Error (MAE) = 1.74 and 4.05 cm; Percent BIAS (PBIAS) = 0.4 and 2.3 respectively) was selected as the best model. Based on our findings, the highest amount of SWE was concentrated in the eastern part of the watershed. SWE modeling is a useful tool for optimal and integrated management of water resources.

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

Similar content being viewed by others

Data availability

The datasets generated during the current study are available from the corresponding author on reasonable request.

References

  • Allahbakhshian-Farsani P, Vafakhah M, Khosravi-Farsani H, Hertig E (2020) Regional flood frequency analysis through some machine learning models in semi-arid regions. Water Resour Manag 34(9):2887–2909

    Article  Google Scholar 

  • Allison P (1999) Multiple regression: A primer Pine Forge Press. Thousand Oaks, CA

    Google Scholar 

  • Appelhans T, Mwangomo E, Hardy DR, Hemp A, Nauss T (2015) Evaluating machine learning approaches for the interpolation of monthly air temperature at Mt. Kilimanjaro. Tanzania Spatial Statistics 14:91–113. https://doi.org/10.1016/j.spasta.2015.05.008

    Article  Google Scholar 

  • Avand M, Janizadeh S, Tien Bui D, Pham VH, Ngo PTT, Nhu VH (2020) A tree-based intelligence ensemble approach for spatial prediction of potential groundwater. Int J Digit Earth 13(12):1408–1429. https://doi.org/10.1080/17538947.2020.1718785

    Article  Google Scholar 

  • Bai Y, Fernald A, Tidwell V, Gunda T (2019) Reduced and earlier snowmelt runoff impacts traditional irrigation systems. J Contemp Water Res Education 168(1):10–28

    Article  Google Scholar 

  • Bair EH, Calfa AA, 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 

  • Berezowski T, Chybicki A (2018) High-resolution discharge forecasting for snowmelt and rainfall mixed events. Water (Switzerland) 10(1):56. https://doi.org/10.3390/w10010056

  • Botsis D, Latinopoulos P, Diamantaras K (2011) Rainfall–runoff modeling using support vector regression and artificial neural networks. In 12th International Conference on Environmental Science and Technology, Rhodes, Greece. http://aetos.it.teithe.gr/~kdiamant/docs/CEST2011.pdf

  • Breiman L (2001) Random forests Machine learning 45(1):5–32

    Article  Google Scholar 

  • Breiman L, Friedman J, Stone CJ, Olshen RA (1984) Classification and regression trees Chapman and Hall, New York

  • Bui DT, Ngo P-TT, Pham TD, Jaafari A, Minh NQ, Hoa PV, Samui P (2019) A novel hybrid approach based on a swarm intelligence optimized extreme learning machine for flash flood susceptibility mapping. Catena. 179:184–196

    Article  Google Scholar 

  • Cortes C, Vapnik V (1995) Support-vector networks. Machine learning 20(3):273–297

    Article  Google Scholar 

  • Costache R (2019) Flood susceptibility assessment by using bivariate statistics and machine learning models-a useful tool for flood risk management. Water Resour Manag 33(9):3239–3256

    Article  Google Scholar 

  • Darabi H, Choubin B, Rahmati O, Haghighi AT, Pradhan B, Kløve B (2019) Urban flood risk mapping using the GARP and QUEST models: a comparative study of machine learning techniques. J Hydrol 569:142–154

    Article  Google Scholar 

  • DeWalle DR, Rango A (2008) Principles of snow hydrology. Cambridge University Press

    Book  Google Scholar 

  • Essenfelder AH, Giupponi C (2020) A coupled hydrologic-machine learning modelling framework to support hydrologic modelling in river basins under Interbasin water transfer regimes. Environ Model Softw 131:104779. https://doi.org/10.1016/j.envsoft.2020.104779

  • Fathololoumi S, Vaezi AR, Alavipanah SK, Ghorbani A, Saurette D, Biswas A (2021) Effect of multi-temporal satellite images on soil moisture prediction using a digital soil mapping approach. Geoderma 385:114901

    Article  Google Scholar 

  • Fernández-Delgado M, Sirsat MS, Cernadas E, Alawadi S, Barro S, Febrero-Bande M (2019) An extensive experimental survey of regression methods. Neural Networks 111:11–34. https://doi.org/10.1016/j.neunet.2018.12.010

  • Ganjkhanlo H, Vafakhah M, Zeinivand H, Fathzadeh A (2020a) The effect of different sampling schemes on estimation precision of snow water equivalent (SWE) using geostatistics techniques in a semi-arid region of Iran. Geocarto Int 35(16):1769–1782. https://doi.org/10.1080/10106049.2019.1581267

  • Ganjkhanlo H, Vafakhah M, Zeinivand H, Fathzadeh A (2020b) Prediction of snow water equivalent using artificial neural network and adaptive neuro-fuzzy inference system with two sampling schemes in semi-arid region of Iran. J Mt Sci 17(7):1712–1723. https://doi.org/10.1007/s11629-018-4875-8

  • Ghanbarpour MR, Saghafian B, Saravi MM, Abbaspour KC (2007) Evaluation of spatial and temporal variability of snow cover in a large mountainous basin in Iran. Hydrol Res 38(1):45–58

    Article  Google Scholar 

  • Gholami H, Dolat Kordestani M, Li J, Telfer MW, Fathabadi A (2019) Diverse sources of aeolian sediment revealed in an arid landscape in southeastern Iran using a modified Bayesian un-mixing model. Aeolian Res 41(December 2018):100547. https://doi.org/10.1016/j.aeolia.2019.100547

  • Goodfellow I, Bengio Y, Courville A, Bengio Y (2016) Deep learning.  MIT press, Cambridge

    Google Scholar 

  • Granata F, Gargano R, de Marinis G (2016) Support vector regression for rainfall-runoffmodeling in urban drainage: a comparison with the EPA’s storm water management model. Water (Switzerland) 8(3):69. https://doi.org/10.3390/w8030069

  • Harvey AC (1977) Some comments on multicollinearity in regression. J R Stat Soc: Ser C: Appl Stat 26(2):188–191

    Google Scholar 

  • Hatta S, Nishimura T, Saga H, Fujita M (1995) Study on snowmelt runoff prediction using weekly weather forecast. Environ Int 21(5):501–507

    Article  Google Scholar 

  • Hong H, Pradhan B, Xu C, Bui DT (2015) Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines. Catena. 133:266–281

    Article  Google Scholar 

  • Hsieh WW (2009) Machine learning methods in the environmental sciences: neural networks and kernels. [place unknown]: Cambridge university press

  • Jaafari A, Zenner EK, Pham BT (2018) Wildfire spatial pattern analysis in the Zagros Mountains, Iran: a comparative study of decision tree based classifiers. Ecological informatics 43:200–211

    Article  Google Scholar 

  • Janizadeh S, Avand M, Jaafari A, Van Phong T, Bayat M, Ahmadisharaf E, Prakash I, Pham BT, Lee S (2019) Prediction success of machine learning methods for flash flood susceptibility mapping in the Tafresh watershed. Iran Sustainability (Switzerland) 11(19)

  • Koopialipoor M, Armaghani DJ, Hedayat A, Marto A, Gordan B (2019a) Applying various hybrid intelligent systems to evaluate and predict slope stability under static and dynamic conditions. Soft Comput 23(14):5913–5929

    Article  Google Scholar 

  • Koopialipoor M, Fallah A, Armaghani DJ, Azizi A, Mohamad ET (2019b) Three hybrid intelligent models in estimating flyrock distance resulting from blasting. Eng Comput 35(1):243–256

    Article  Google Scholar 

  • Kotsiantis SB, Pintelas PE (2004) Combining bagging and boosting. Comput Intell 1(4):324–333. https://doi.org/10.1103/PhysRevD.77.085025

  • Kroll CN, Song P (2013) Impact of multicollinearity on small sample hydrologic regression models. Water Resour Res 49(6):3756–3769

    Article  Google Scholar 

  • Kuhn M, Johnson K, et al. (2013) Applied predictive modeling. [place unknown]: Springer

  • LaMalfa EM, Ryle R (2008) Differential snowpack accumulation and water dynamics in aspen and conifer communities: implications for water yield and ecosystem function. Ecosystems. 11(4):569–581

    Article  Google Scholar 

  • Lianjun C (2016) Research on snow extracting methods on the basis of random forests algorithm. International Journal of Simulation: Systems, Science and Technology 17(19):3.1–3.6

    Google Scholar 

  • Ließ M, Glaser B, Huwe B (2012) Uncertainty in the spatial prediction of soil texture: comparison of regression tree and random Forest models. Geoderma. 170:70–79

    Article  Google Scholar 

  • Ma Y, Huang Y, Chen X, Li Y, Bao A. 2013. Modelling snowmelt runoff under climate change scenarios in an ungauged mountainous watershed, Northwest China. Mathematical Problems in Engineering. 2013

  • MacKay DJC (1992) Bayesian interpolation. Neural Comput 4(3):415–447

    Article  Google Scholar 

  • Marwala T (2007) Bayesian training of neural networks using genetic programming. Pattern Recogn Lett 28(12):1452–1458

    Article  Google Scholar 

  • Maurer EP, Rhoads JD, Dubayah RO, Lettenmaier DP (2003) Evaluation of the snow-covered area data product from MODIS. Hydrol Process 17(1):59–71

    Article  Google Scholar 

  • McBratney AB, Santos MM, Minasny B (2003) On digital soil mapping. Geoderma 117(1–2):3–52

    Article  Google Scholar 

  • Mekanik F, Imteaz MA, Gato-Trinidad S, Elmahdi A (2013) Multiple regression and artificial neural network for long-term rainfall forecasting using large scale climate modes. J Hydrol 503:11–21

    Article  Google Scholar 

  • Mosavi A, Ozturk P, Chau K (2018) Flood prediction using machine learning models: literature review. Water. 10(11):1536

    Article  Google Scholar 

  • Mote PW, Hamlet AF, Clark MP, Lettenmaier DP (2005) Declining mountain snowpack in western North America. Bull Am Meteorol Soc 86(1):39–50

    Article  Google Scholar 

  • Nandhini M, Sivanandam SN (2015) An improved predictive association rule based classifier using gain ratio and T-test for health care data diagnosis. Sadhana. 40(6):1683–1699

    Article  Google Scholar 

  • Nguyen PT, Ha DH, Avand M, Jaafari A, Nguyen HD, Al-Ansari N, Van PT, Sharma R, Kumar R, Van LH et al (2020) Soft computing ensemble models based on logistic regression for groundwater potential mapping. Appl Sci 10(7):2469

    Article  Google Scholar 

  • Nhu VH, Janizadeh S, Avand M, Chen W, Farzin M, Omidvar E, Shirzadi A, Shahabi H, Clague JJ, Jaafari A et al (2020) GIS-based gully erosion susceptibility mapping: a comparison of computational ensemble data mining models. Applied Sciences (Switzerland). 10(6)

  • Nikoo M, Ramezani F, Hadzima-Nyarko M, Nyarko EK, Mohammad N (2016) Flood-routing modeling with neural network optimized by social-based algorithm. Nat Hazards 82(1):1–24

    Article  Google Scholar 

  • Noi PT, Degener J, Kappas M (2017) Comparison of multiple linear regression, cubist regression, and random forest algorithms to estimate daily air surface temperature from dynamic combinations of MODIS LST data. Remote Sens 9(5)

  • O’brien RM (2007) A caution regarding rules of thumb for variance inflation factors. Qual Quant 41(5):673–690

    Article  Google Scholar 

  • Peters J, Verhoest NEC, Samson R, Boeckx P, De Baets B (2008) Wetland vegetation distribution modelling for the identification of constraining environmental variables. Landsc Ecol 23(9):1049–1065

    Article  Google Scholar 

  • Pham L, Luo L, Finley A (2020) Evaluation of random Forest for short-term daily streamflow forecast in rainfall and snowmelt driven watersheds. Hydrol Earth Syst Sci Discuss(June):1–33

  • Rahman MM, Karunasinghe J, Clifford S, Knibbs LD, Morawska L (2020) New insights into the spatial distribution of particle number concentrations by applying non-parametric land use regression modelling. Sci Total Environ 702:134708

    Article  Google Scholar 

  • Rahmati O, Pourghasemi HR, Zeinivand H (2016) Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Province. Iran Geocarto International 31(1):42–70

    Article  Google Scholar 

  • Rango A, Steele CM, Elias E, Mejia J, Fernald A. 2013. Potential impacts of climate warming on runoff from snowmelt: A case study of two mountainous basins in the Upper Rio Grande. In: AGU Fall Meeting Abstracts. Vol. 2013. [place unknown]; p. H23A--1217

  • Revuelto J, López-Moreno JI, Azorin-Molina C, Vicente-Serrano SM (2014) Topographic control of snowpack distribution in a small catchment in the central Spanish Pyrenees: intra-and inter-annual persistence. Cryosphere 8(5):1989–2006

    Article  Google Scholar 

  • Robinson C, Schumacker RE (2009) Interaction effects: centering, variance inflation factor, and interpretation issues. Multiple linear regression viewpoints 35(1):6–11

    Google Scholar 

  • Rozos E (2019) Machine learning, urban water resources management and operating policy. Resources. 8(4):173

    Article  Google Scholar 

  • Sahoo GB, Ray C, De Carlo EH (2006) Calibration and validation of a physically distributed hydrological model, MIKE SHE, to predict streamflow at high frequency in a flashy mountainous Hawaii stream. J Hydrol 327(1–2):94–109

    Article  Google Scholar 

  • Shabani S, Yousefi P, Adamowski J, Naser G. 2016. Intelligent soft computing models in water demand forecasting. Water Stress in Plants:99–117

  • Sharifi Garmdareh E, Vafakhah M, Eslamian SS (2018) Regional flood frequency analysis using support vector regression in arid and semi-arid regions of Iran. Hydrol Sci J 63(3):426–440. https://doi.org/10.1080/02626667.2018.1432056

  • Sharifi Garmdareh E, Vafakhah M, Eslamian SS, Khosrobeigi Bozchaloei S, Allahbakhshian-Farsani P, Vafakhah M, Khosravi-Farsani H, Hertig E, Appelhans T, Mwangomo E et al (2020) A coupled hydrologic-machine learning modelling framework to support hydrologic modelling in river basins under interbasin water transfer regimes. Water (Switzerland) 34(5):283–294. https://doi.org/10.1016/j.asoc.2016.12.052

  • Starzyk J (2010) Water resource planning and management using motivated machine learning. IAHS-AISH Publication 338(July):214–220

    Google Scholar 

  • Sun M, Chen T, Yu Y, Wang Z, Chi D, others. 2014. Extreme learning machine application in flood forecasting. Journal of Shenyang Agricultural University 45(2):245–248

  • Tahmasebi P, Kamrava S, Bai T, Sahimi M (2020) Machine learning in geo- and environmental sciences: from small to large scale. Adv Water Resour 142:103619. https://doi.org/10.1016/j.advwatres.2020.103619

  • Talei A, Chua LHC, Quek C, Jansson P-E (2013) Runoff forecasting using a Takagi--Sugeno neuro-fuzzy model with online learning. J Hydrol 488:17–32

    Article  Google Scholar 

  • Thapa S, Zhao Z, Li B, Lu L, Fu D, Shi X, Tang B, Qi H (2020) Snowmelt-driven streamflow prediction using machine learning techniques (LSTM, NARX, GPR, and SVR). Water (Switzerland). 12(6)

  • Tien Bui D, Shirzadi A, Shahabi H, Chapi K, Omidavr E, Pham BT, Talebpour Asl D, Khaledian H, Pradhan B, Panahi M et al (2019) A novel ensemble artificial intelligence approach for gully erosion mapping in a semi-arid watershed (Iran). Sensors. 19(11):2444

    Article  Google Scholar 

  • Vafakhah M, Khosrobeigi BS (2020) Regional analysis of flow duration curves through support vector regression. Water Resour Manag 34(1):283–294

    Article  Google Scholar 

  • Vafakhah M, Mohseni SM, Mahdavi M, Alavipanah SK (2011) Snowmelt runoff prediction by using artificial neural network and adaptive neuro-fuzzy inference system in Taleghan watershed. Iranian J Watershed Manag Sci Eng 5(14):23–35

  • Vafakhah M, Nouri A, Alavipanah SK (2015) Snowmelt-runoff estimation using radiation SRM model in Taleghan watershed. Environ Earth Sci 73(3):993–1003

    Article  Google Scholar 

  • Viswesvaran C (1998) Multiple regression in behavioral research: explanation and prediction. Pers Psychol 51(1):223

    Google Scholar 

  • Vorpahl P, Elsenbeer H, Märker M, Schröder B (2012) How can statistical models help to determine driving factors of landslides? Ecol Model 239:27–39

    Article  Google Scholar 

  • Wester P, Mishra A, Mukherji A, Shrestha AB. 2019. The Hindu Kush Himalaya assessment: Mountains, climate change, sustainability and people. [place unknown]: Springer Nature

  • Wheeler D, Tiefelsdorf M (2005) Multicollinearity and correlation among local regression coefficients in geographically weighted regression. J Geogr Syst 7(2):161–187

    Article  Google Scholar 

  • Winstral A, Elder K, Davis RE (2002 Oct) Spatial snow modeling of wind-redistributed snow using terrain-based parameters. J Hydrometeorol 3(5):524–538

    Article  Google Scholar 

  • Xie Z, Lou I, Ung WK, Mok KM (2012) Freshwater algal bloom prediction by support vector machine in Macau storage reservoirs. Math Probl Eng 2012

  • Yariyan P, Janizadeh S, Van Phong T, Nguyen HD, Costache R, Van Le H, Pham BT, Pradhan B, Tiefenbacher JP (2020) Improvement of best first decision trees using bagging and Dagging ensembles for flood probability mapping. Water Resour Manag 34(9):3037–3053

    Article  Google Scholar 

  • Yoo C, Cho E (2019) Effect of multicollinearity on the bivariate frequency analysis of annual maximum rainfall events. Water (Switzerland) 11(5):905. https://doi.org/10.3390/w11050905

  • Young CC, Liu WC, Wu MC (2017) A physically based and machine learning hybrid approach for accurate rainfall-runoff modeling during extreme typhoon events. App Soft Comput 53:205–216. https://doi.org/10.1016/j.asoc.2016.12.052

  • Zheng X, Wang Q, Zhou L, Sun Q, Li Q (2018) Predictive contributions of snowmelt and rainfall to streamflow variations in the Western United States. Adv Meteorol 2018:3765098. https://doi.org/10.1155/2018/3765098

  • Zhou J, Li E, Wei H, Li C, Qiao Q, Armaghani DJ (2019) Random forests and cubist algorithms for predicting shear strengths of rockfill materials. Applied Sciences (Switzerland) 9(8):1–16

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Watershed Management Engineering, Faculty of Natural Resources and Marine Sciences, Tarbiat Modares University, Noor, 46414-356, Iran

    Mehdi Vafakhah, Ali Nasiri Khiavi, Saeid Janizadeh & Hojatolah Ganjkhanlo

Authors
  1. Mehdi Vafakhah
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ali Nasiri Khiavi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Saeid Janizadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hojatolah Ganjkhanlo
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Mehdi Vafakhah: assisted in running the program and data collection, revised manuscript; Ali Nasiri Khiavi: drafted the manuscript; Saeid Janizadeh: conducted data analysis; Hojatolah Ganjkhanlo: collected data and provided maps. The authors read and approved the final manuscript.

Corresponding author

Correspondence to Mehdi Vafakhah.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Human and animal rights

This article does not contain any studies with animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Communicated by: H. Babaie

Publisher’s note

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

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

Vafakhah, M., Nasiri Khiavi, A., Janizadeh, S. et al. Evaluating different machine learning algorithms for snow water equivalent prediction. Earth Sci Inform 15, 2431–2445 (2022). https://doi.org/10.1007/s12145-022-00846-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12145-022-00846-z

Keywords

Search

Navigation