Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

Springer Nature Link
Log in

Virtual weather stations for meteorological data estimations

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

Abstract

In this paper, the concept of Virtual Weather Stations (VWS) is introduced. A VWS is an integration of algorithms to download meteorological data, process and use them with the main objective of estimate data in nearby locations with no meteorological stations. To develop the VWS, the performances of different interpolation methods were evaluated to test the accuracy. Daily data from an automatic weather station network, such as precipitation (Precip), air temperature (Temp), air relative humidity, mean wind speed, total solar irradiation, and reference evapotranspiration were interpolated using artificial neural networks (ANNs) with the hardlim, sigmoid, hyperbolic tangent (tanh), softsign, and rectified linear unit (relu) activations functions were employed. To contrast the ANNs interpolations, alternatives methods such as inverse distance weighting, inverse-squared distance weighting, multilinear regression, and random forest regression were used. To validate the models, a randomly selected weather station was removed from the daily datasets, and the interpolated values were compared with the actual station records. Additionally, interpolations in the summer and winter months were performed to check the capability of the models during periods with more extreme phenomena. The results showed that the interpolation methods have an R2 up to 0.98 for variables such as temperatures for the period of 1 year. Meanwhile, during the summer and winter, the models presented lower accuracy. From a practical perspective, the methods here described could be useful to produce meteorological data with the VWS to record temperatures and dose the irrigation in crops.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Muzammal M, Talat R, Sodhro AH, Pirbhulal S (2020) A multi-sensor data fusion enables ensemble approach for medical data from body sensor networks. Inf Fusion 53:155–164. https://doi.org/10.1016/j.inffus.2019.06.021

    Article  Google Scholar 

  2. Sodhro AH, Pirbhulal S, Luo Z, de Albuquerque VHC (2019) Towards an optimal resource management for IoT based green and sustainable smart cities. J Clean Prod 220:1167–1179. https://doi.org/10.1016/j.jclepro.2019.01.188

    Article  Google Scholar 

  3. Talat R, Obaidat MS, Muzammal M, Sodhro AH, Luo Z, Pirbhulal S (2020) A decentralised approach to privacy preserving trajectory mining. Future Gener Comput Syst 102:382–392. https://doi.org/10.1016/j.future.2019.07.068

    Article  Google Scholar 

  4. Mason d’Croz D, Deryng D, Elliott J, Tabeau A, Von Lampe M, Schmitz C, van der Mensbrugghe D, Heyhoe E, Kyle P, Schmid E, van Meijl H, Robertson R, Sands RD, Popp A, Müller C, Lotze-Campen H, Nelson GC, Ahammad H, Valin H, Havlík P, Robinson S, Hasegawa T, Willenbockel D, Fujimori S (2013) Climate change effects on agriculture: economic responses to biophysical shocks. Proc Natl Acad Sci 111:3274–3279. https://doi.org/10.1073/pnas.1222465110

    Article  Google Scholar 

  5. Springmann M, Mason-D’Croz D, Robinson S, Garnett T, Godfray HCJ, Gollin D, Rayner M, Ballon P, Scarborough P (2016) Global and regional health effects of future food production under climate change: a modelling study. Lancet 387:1937–1946. https://doi.org/10.1016/S0140-6736(15)01156-3

    Article  Google Scholar 

  6. Naylor RL, Battisti DS, Tewksbury JJ, Tigchelaar M, Deutsch CA, Merrill SC, Huey RB (2018) Increase in crop losses to insect pests in a warming climate. Science 361:916–919. https://doi.org/10.1126/science.aat3466

    Article  Google Scholar 

  7. Teshome Y, Biazin B, Wolka K, Burka A (2018) Evaluating performance of traditional surface irrigation techniques in Cheleleka watershed in Central Rift Valley, Ethiopia. Appl Water Sci 8:1–14. https://doi.org/10.1007/s13201-018-0862-z

    Article  Google Scholar 

  8. Conforti P (2011) Looking ahead in world food and agriculture: perspectives to 2050. Food and Agriculture Organization of the United Nations (FAO), Rome

    Google Scholar 

  9. Allen RG, Pereira LS, Raes D, Smith M, et al. (1998) Crop evapotranspiration-guidelines for computing crop water requirements-FAO irrigation and drainage paper 56. Fao, Rome 300, D05109

  10. Estévez J, Gavilán P, Giráldez JV (2011) Guidelines on validation procedures for meteorological data from automatic weather stations. J Hydrol 402:144–154. https://doi.org/10.1016/j.jhydrol.2011.02.031

    Article  Google Scholar 

  11. Akram M, El C (2016) Sequence to sequence weather forecasting with long short-term memory recurrent neural networks. Int J Comput Appl 143:7–11. https://doi.org/10.5120/ijca2016910497

    Article  Google Scholar 

  12. Hung NQ, Babel MS, Weesakul S, Tripathi NK (2009) Hydrology and earth system sciences an artificial neural network model for rainfall forecasting in Bangkok, Thailand. Hydrol Earth Syst Sci 13:1413–1416

    Article  Google Scholar 

  13. Partal T, Cigizoglu HK, Kahya E (2015) Daily precipitation predictions using three different wavelet neural network algorithms by meteorological data. Stoch Environ Res Risk Assess 29:1317–1329. https://doi.org/10.1007/s00477-015-1061-1

    Article  Google Scholar 

  14. Cao Q, Ewing BT, Thompson MA (2012) Forecasting wind speed with recurrent neural networks. Eur J Oper Res 221:148–154. https://doi.org/10.1016/j.ejor.2012.02.042

    Article  MathSciNet  MATH  Google Scholar 

  15. de Oliviera MMF, Ebecken FF, de Oliviera JLF, de Azevedo Santos I (2009) Neural network model to predict a storm surge. J Appl Meteorol Climatol 48:143–155. https://doi.org/10.1175/2008JAMC1907.1

    Article  Google Scholar 

  16. Filippo A, Rebelo Torres A, Kjerfve B, Monat A (2012) Application of artificial neural network (ANN) to improve forecasting of sea level. Ocean Coast Manag 55:101–110. https://doi.org/10.1016/j.ocecoaman.2011.09.007

    Article  Google Scholar 

  17. Feng X, Li Q, Zhu Y, Hou J, Jin L, Wang J (2015) Artificial neural networks forecasting of PM2.5 pollution using air mass trajectory based geographic model and wavelet transformation. Atmos Environ 107:118–128. https://doi.org/10.1016/j.atmosenv.2015.02.030

    Article  Google Scholar 

  18. Hrust L, Klaić ZB, Križan J, Antonić O, Hercog P (2009) Neural network forecasting of air pollutants hourly concentrations using optimised temporal averages of meteorological variables and pollutant concentrations. Atmos Environ 43:5588–5596. https://doi.org/10.1016/j.atmosenv.2009.07.048

    Article  Google Scholar 

  19. Amrouche B, Le Pivert X (2014) Artificial neural network based daily local forecasting for global solar radiation. Appl Energy 130:333–341. https://doi.org/10.1016/j.apenergy.2014.05.055

    Article  Google Scholar 

  20. Hasni A, Sehli A, Draoui B, Bassou A, Amieur B (2012) Estimating global solar radiation using artificial neural network and climate data in the south-western region of Algeria. Energy Procedia 18:531–537. https://doi.org/10.1016/j.egypro.2012.05.064

    Article  Google Scholar 

  21. Şenkal O, Kuleli T (2009) Estimation of solar radiation over Turkey using artificial neural network and satellite data. Appl Energy 86:1222–1228. https://doi.org/10.1016/j.apenergy.2008.06.003

    Article  Google Scholar 

  22. Fan S, Liao JR, Yokoyama R, Chen L, Lee WJ (2009) Forecasting the wind generation using a two-stage network based on meteorological information. IEEE Trans Energy Convers 24:474–482. https://doi.org/10.1109/TEC.2008.2001457

    Article  Google Scholar 

  23. Olaofe ZO (2014) A 5-day wind speed & power forecasts using a layer recurrent neural network (LRNN). Sustain Energy Technol Assess 6:1–24. https://doi.org/10.1016/j.seta.2013.12.001

    Article  Google Scholar 

  24. Sideratos G, Hatziargyriou ND (2007) An advanced statistical method for wind power forecasting. IEEE Trans Power Syst 22:258–265. https://doi.org/10.1109/TPWRS.2006.889078

    Article  Google Scholar 

  25. Abrishami N, Sepaskhah AR, Shahrokhnia MH (2018) Estimating wheat and maize daily evapotranspiration using artificial neural network. Theor Appl Climatol. https://doi.org/10.1007/s00704-018-2418-4

    Article  Google Scholar 

  26. Ballesteros R, Ortega JF, Moreno MÁ (2016) FORETo: new software for reference evapotranspiration forecasting. J Arid Environ 124:128–141. https://doi.org/10.1016/j.jaridenv.2015.08.006

    Article  Google Scholar 

  27. Chowdhury A, Gupta D, Paswan-Das D, Bhowmick A (2017) Estimation of reference evapotranspiration using artificial neural network for Mohanpur, Nadia District, West Bengal: a case study. Int J Res Eng Technol 6:125–130. https://doi.org/10.15623/ijret.2017.0607021

    Article  Google Scholar 

  28. Kisi O (2007) Evapotranspiration modelling from climatic data using a neural computing technique. Hydrol Process 21:1925–1934. https://doi.org/10.1002/hyp.6403

    Article  Google Scholar 

  29. Hernández L, Baladrón C, Aguiar JM, Carro B, Sanchez-Esguevillas AJ, Lloret J, Massana J (2014) A survey on electric power demand forecasting: future trends in smart grids, microgrids and smart buildings. IEEE Commun Surv Tutor 16:1460–1495. https://doi.org/10.1109/SURV.2014.032014.00094

    Article  Google Scholar 

  30. Li J, Heap AD (2011) A review of comparative studies of spatial interpolation methods in environmental sciences: performance and impact factors. Ecol Inform 6:228–241. https://doi.org/10.1016/j.ecoinf.2010.12.003

    Article  Google Scholar 

  31. Lu GY, Wong DW (2008) An adaptive inverse-distance weighting spatial interpolation technique. Comput Geosci 34:1044–1055. https://doi.org/10.1016/j.cageo.2007.07.010

    Article  Google Scholar 

  32. Jin Q, Zhang J, Shi M, Huang J (2016) Estimating loess plateau average annual precipitation with multiple linear regression kriging and geographically weighted regression kriging. Water (Switzerland) 8:266. https://doi.org/10.3390/W8060266

    Article  Google Scholar 

  33. Nalder IA, Wein RW (1998) Spatial interpolation of climatic normals: test of a new method in the Canadian boreal forest. Agric For Meteorol 92:211–225. https://doi.org/10.1016/S0168-1923(98)00102-6

    Article  Google Scholar 

  34. Li J, Heap AD (2014) Spatial interpolation methods applied in the environmental sciences: a review. Environ Model Softw 53:173–189. https://doi.org/10.1016/j.envsoft.2013.12.008

    Article  Google Scholar 

  35. Li J, Heap AD, Potter A, Daniell JJ (2011) Application of machine learning methods to spatial interpolation of environmental variables. Environ Model Softw 26:1647–1659. https://doi.org/10.1016/j.envsoft.2011.07.004

    Article  Google Scholar 

  36. Berndt C, Haberlandt U (2018) Spatial interpolation of climate variables in Northern Germany—influence of temporal resolution and network density. J Hydrol Reg Stud 15:184–202. https://doi.org/10.1016/j.ejrh.2018.02.002

    Article  Google Scholar 

  37. Jeffrey SJ, Carter JO, Moodie KB, Beswick AR (2001) Using spatial interpolation to construct a comprehensive archive of Australian climate data. Environ Model Softw 16:309–330. https://doi.org/10.1016/S1364-8152(01)00008-1

    Article  Google Scholar 

  38. Mendez M, Calvo-Valverde L (2016) Assessing the performance of several rainfall interpolation methods as evaluated by a conceptual hydrological model. Procedia Eng 154:1050–1057. https://doi.org/10.1016/j.proeng.2016.07.595

    Article  Google Scholar 

  39. Wagner PD, Fiener P, Wilken F, Kumar S, Schneider K (2012) Comparison and evaluation of spatial interpolation schemes for daily rainfall in data scarce regions. J Hydrol 464–465:388–400. https://doi.org/10.1016/j.jhydrol.2012.07.026

    Article  Google Scholar 

  40. Wu T, Li Y (2013) Spatial interpolation of temperature in the United States using residual kriging. Appl Geogr 44:112–120. https://doi.org/10.1016/j.apgeog.2013.07.012

    Article  Google Scholar 

  41. Voyant C, Notton G, Kalogirou S, Nivet ML, Paoli C, Motte F, Fouilloy A (2017) Machine learning methods for solar radiation forecasting: a review. Renew Energy 105:569–582. https://doi.org/10.1016/j.renene.2016.12.095

    Article  Google Scholar 

  42. Tripathy AK, Adinarayana J, Sudharsan D, Merchant SN, Desai UB, Vijayalakshmi K, Raji Reddy D, Sreenivas G, Ninomiya S, Hirafuji M, Kiura T, Tanaka K (2011) Data mining and wireless sensor network for agriculture pest/disease predictions. In: Proceedings of the 2011 World congress on information and communication technologies. WICT 2011 1229–1234. https://doi.org/10.1109/WICT.2011.6141424

  43. Valipour M (2016) How much meteorological information is necessary to achieve reliable accuracy for rainfall estimations? Agriculture 6:53. https://doi.org/10.3390/agriculture6040053

    Article  Google Scholar 

  44. Kumar R, Aggarwal RK, Sharma JD (2015) Comparison of regression and artificial neural network models for estimation of global solar radiations. Renew Sustain Energy Rev 52:1294–1299. https://doi.org/10.1016/j.rser.2015.08.021

    Article  Google Scholar 

  45. Fan S, Chen L, Lee W-J (2009) Short-term load forecasting using comprehensive combination based on multimeteorological information. IEEE Trans Ind Appl 45:1460–1466. https://doi.org/10.1109/tia.2009.2023571

    Article  Google Scholar 

  46. López G, Batlles FJ, Tovar-Pescador J (2005) Selection of input parameters to model direct solar irradiance by using artificial neural networks. Energy 30:1675–1684. https://doi.org/10.1016/j.energy.2004.04.035

    Article  Google Scholar 

  47. Gnana Sheela K, Deepa SN (2013) Neural network based hybrid computing model for wind speed prediction. Neurocomputing 122:425–429. https://doi.org/10.1016/j.neucom.2013.06.008

    Article  Google Scholar 

  48. del Río S, Penas Á, Fraile R (2005) Analysis of recent climatic variations in Castile and Leon (Spain). Atmos Res 73:69–85. https://doi.org/10.1016/j.atmosres.2004.06.005

    Article  Google Scholar 

  49. Nafría DA, Garrido N, Álvarez MV, Cubero D, Fernández M, Villarino I, Gutiérrez A, Abia I (2013) Atlas Agroclimático de Castilla y León, Junta de Castilla y León. Instituto Tecnológico Agrario de Castilla y León. Ministerio de Agricultura, Alimentación y Medio Ambiente, Madrid

    Google Scholar 

  50. Nayak DR, Mahapatra A, Mishra P (2013) A survey on rainfall prediction using artificial neural network. Int J Comput Appl 72:32–40

    Google Scholar 

  51. Falamarzi Y, Palizdan N, Huang YF, Lee TS (2014) Estimating evapotranspiration from temperature and wind speed data using artificial and wavelet neural networks (WNNs). Agric Water Manag 140:26–36. https://doi.org/10.1016/j.agwat.2014.03.014

    Article  Google Scholar 

  52. Diamantopoulou MJ, Georgiou PE, Papamichail DM (2011) Performance evaluation of artificial neural networks in estimating reference evapotranspiration with minimal meteorological data. Glob NST J 13:18–27

    Google Scholar 

  53. Bilgili M, Sahin B (2010) Comparative analysis of regression and artificial neural network models for wind speed prediction. Meteorol Atmos Phys 109:61–72. https://doi.org/10.1007/s00703-010-0093-9

    Article  Google Scholar 

  54. 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. https://doi.org/10.1016/j.jhydrol.2013.08.035

    Article  Google Scholar 

  55. Laaboudi A, Mouhouche B, Draoui B (2012) Neural network approach to reference evapotranspiration modeling from limited climatic data in arid regions. Int J Biometeorol 56:831–841. https://doi.org/10.1007/s00484-011-0485-7

    Article  Google Scholar 

  56. Shao Y, Pan J, Zhang C, Jiang L, He Y (2014) Detection in situ of carotenoid in microalgae by transmission spectroscopy. Comput Electron Agric 112:121–127. https://doi.org/10.1016/j.compag.2014.10.008

    Article  Google Scholar 

  57. Nastos PT, Moustris KP, Larissi IK, Paliatsos AG (2013) Rain intensity forecast using artificial neural networks in Athens, Greece. Atmos Res 119:153–160. https://doi.org/10.1016/j.atmosres.2011.07.020

    Article  Google Scholar 

  58. Thorsen SM, Höglind M (2010) Assessing winter survival of forage grasses in Norway under future climate scenarios by simulating potential frost tolerance in combination with simple agroclimatic indices. Agric For Meteorol 150:1272–1282. https://doi.org/10.1016/j.agrformet.2010.05.010

    Article  Google Scholar 

  59. Bailey A, Chase TN, Cassano JJ, Noone D (2011) Changing temperature inversion characteristics in the U.S. southwest and relationships to large-scale atmospheric circulation. J Appl Meteorol Climatol 50:1307–1323. https://doi.org/10.1175/2011JAMC2584.1

    Article  Google Scholar 

  60. Meehl GA, Tebaldi C (2004) More intense, more frequent, and longer lasting heat waves in the 21st century. Science 305:994–997. https://doi.org/10.1126/science.1098704

    Article  Google Scholar 

  61. Luber G, McGeehin M (2008) Climate change and extreme heat events. Am J Prev Med 35:429–435. https://doi.org/10.1016/j.amepre.2008.08.021

    Article  Google Scholar 

  62. Kemmoku Y, Orita S, Nakagawa S, Sakakibara T (1999) Daily insolation forecasting using a multi-stage neural network. Sol Energy 66:193–199. https://doi.org/10.1016/S0038-092X(99)00017-1

    Article  Google Scholar 

  63. Linares-Rodriguez A, Ruiz-Arias JA, Pozo-Vazquez D, Tovar-Pescador J (2013) An artificial neural network ensemble model for estimating global solar radiation from meteosat satellite images. Energy 61:636–645. https://doi.org/10.1016/j.energy.2013.09.008

    Article  Google Scholar 

  64. Nagpal A, Gabrani G (2019) Python for data analytics, scientific and technical applications. In: 2019 Amity international conference on artificial intelligence (AICAI), pp 140–145. https://doi.org/10.1109/aicai.2019.8701341

  65. Lin Y, Jin X, Chen J, Sodhro AH, Pan Z (2019) An analytic computation-driven algorithm for decentralized multicore systems. Future Gener Comput Syst 96:101–110. https://doi.org/10.1016/j.future.2019.01.031

    Article  Google Scholar 

  66. Marowka A (2018) On parallel software engineering education using python. Educ Inf Technol 23:357–372. https://doi.org/10.1007/s10639-017-9607-0

    Article  Google Scholar 

  67. Sodhro AH, Malokani AS, Sodhro GH, Muzammal M, Zongwei L (2019) An adaptive QoS computation for medical data processing in intelligent healthcare. Neural Comput Appl. https://doi.org/10.1007/s00521-018-3931-1

    Article  Google Scholar 

  68. Sodhro AH, Pirbhulal S, De Albuquerque VHC (2019) Artificial intelligence-driven mechanism for edge computing-based industrial applications. IEEE Trans Ind Inform 15:4235–4243. https://doi.org/10.1109/TII.2019.2902878

    Article  Google Scholar 

Download references

Acknowledgements

This research was possible thanks to the funding from the Spanish Ministry of Education and Science via a doctoral Grant to Franco, BM [Grant Number FPU15/01707].

Author information

Authors and Affiliations

  1. Department of Agricultural and Forestry Engineering, University of Valladolid, 34005, Palencia, Spain

    B. M. Franco, L. Hernández-Callejo & L. M. Navas-Gracia

Authors
  1. B. M. Franco
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. L. Hernández-Callejo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. L. M. Navas-Gracia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to B. M. Franco.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Franco, B.M., Hernández-Callejo, L. & Navas-Gracia, L.M. Virtual weather stations for meteorological data estimations. Neural Comput & Applic 32, 12801–12812 (2020). https://doi.org/10.1007/s00521-020-04727-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-04727-8

Keywords

Search

Navigation