Short-term forecasting of soil temperature using artificial neural network

Meteorological Applications

Volumn 22, Issue 3, 2015, Pages 576-585

  Tabari, Hossein ^a   Hosseinzadeh Talaee, P ^b   Willems, Patrick ^a,c  

^a UNIVERSITY OF LEUVEN   (Belgium)

^b ISLAMIC AZAD UNIVERSITY   (Iran)

^c VRIJE UNIVERSITEIT BRUSSEL   (Belgium)

Author keywords

Air temperature; Daily soil temperature; Multilayer perceptron; Soil depth; Time series forecasting

Indexed keywords

EFFICIENCY; FORECASTING; MEAN SQUARE ERROR; MULTILAYER NEURAL NETWORKS; SOILS;

AIR TEMPERATURE; ARTIFICIAL NEURAL NETWORK MODELS; METEOROLOGICAL PARAMETERS; ROOT MEAN SQUARE ERRORS; SHORT-TERM FORECASTING; SOIL DEPTH; SOIL TEMPERATURE; TIME SERIES FORECASTING;

ATMOSPHERIC TEMPERATURE;

AIR TEMPERATURE; ARTIFICIAL NEURAL NETWORK; FORECASTING METHOD; LAND SURFACE; NUMERICAL MODEL; SOIL DEPTH; SOIL TEMPERATURE; TIME SERIES ANALYSIS; WEATHER STATION;

EID: 84936933519     PISSN: 13504827     EISSN: 14698080     Source Type: Journal    
DOI: 10.1002/met.1489     Document Type: Article

References (44)

1. Berberoglu S, Lloyd CD, Atkinson PM, Curran PJ. 2000. The integration of spectral and textural information using neural networks for land cover mapping in the Mediterranean. Comput. Geosci. 26: 385-396.
2. Bilgili M. 2010. Prediction of soil temperature using regression and artificial neural network models. Meteorol. Atmos. Phys. 110: 59-70.
3. Bilgili M, Sahin B, Sangun L. 2013. Estimating soil temperature using neighboring station data via multi-nonlinear regression and artificial neural network models. Environ. Monit. Assess. 185: 347-358.
4. Bond-Lamberty B, Wang C, Gower ST. 2005. Spatiotemporal measurement and modeling of stand-level boreal forest soil temperatures. Agric. For. Meteorol. 131: 27-40.
5. Braley WA, Zarling JP. 1991. MUT1D: user-friendly one-dimensional thermal model. In Cold Regions Engineering, US Army Cold Regions Research and Engineering Laboratory, Hanover, NH, Sodhi DC (ed), American Society of Civil Engineers: New York, NY; 1-10.
6. Dawson CW, Abrahart RJ, Shamseldin AY, Wilby RL. 2006. Flood estimation at ungauged sites using artificial neural networks. J. Hydrol. 319: 391-409.
7. George RK. 2001. Prediction of soil temperature by using artificial neural networks algorithms. Nonlinear Anal. 47: 1737-1748.
8. Hecht-Nielsen R. 1990. Neurocomputing. Addison-Wesley: Reading, MA.
9. Hosseinzadeh Talaee P. 2014. Daily soil temperature modeling using neuro-fuzzy approach. Theor. Appl. Climatol. 118: 481-489, DOI: 10.1007/s00704-013-1084-9.
10. Hsu KL, Gupta HV, Sorooshian S. 1995. Artificial neural network modeling of the rainfall runoff process. Water Resour. Res. 31(10): 2517-2530.
11. Hu QS, Feng S. 2002. A Daily Soil Temperature Dataset and Soil Temperature Climatology of the Contiguous United States. Climate and Bio-Atmospheric Sciences Group, School of Natural Resource Sciences, University of Nebraska-Lincoln: Lincoln, NE.
12. Jackson T, Mansfield K, Saafi M, Colman T, Romine P. 2008. Measuring soil temperature and moisture using wireless MEMS sensors. Measurement 41: 381-390.
13. Kim S, Shiri J, Kisi O, Singh VP. 2013. Estimating daily pan evaporation using different data-driven methods and lag-time patterns. Water Resour. Manage. 27: 2267-2286.
14. Kim S, Singh VP. 2014. Modeling daily soil temperature using data-driven models and spatial distribution. Theor. Appl. Climatol. 118: 465-479, DOI: 10.1007/s00704-013-1065-z.
15. Kisi O. 2007. Streamflow forecasting using different artificial neural network algorithms. J. Hydrol. Eng. 12(5): 532-539.
16. Kisi O. 2008. Stream flow forecasting using neuro-wavelet technique. Hydrol. Processes 22: 4142-4152.
17. Kisi O, Kim S, Shiri J. 2013a. Estimation of dew point temperature using neuro-fuzzy and neural network techniques. Theor. Appl. Climatol. 114: 365-373.
18. Kisi O, Shiri J, Nikoofar B. 2012. Forecasting daily lake levels using artificial intelligence approaches. Comput. Geosci. 41: 169-180.
19. Kisi O, Shiri J, Tombul M. 2013b. Modeling rainfall-runoff process using soft computing techniques. Comput. Geosci. 51: 108-117.
20. Kisi O, Tombul M, Kermani MZ. 2014. Modeling soil temperatures at different depths by using three different neural computing techniques. Theor. Appl. Climatol., DOI: 10.1007/s00704-014-1232-x.
21. Lei S, Daniels JL, Bian Z, Wainaina N. 2011. Improved soil temperature modeling. Environ. Earth Sci. 62(6): 1123-1130.
22. Maier HR, Dandy GC. 2000. Neural networks for the prediction and forecasting of water resources variables: a review of modeling issues and applications. Environ. Model. Software 15: 101-124.
23. Masters T. 1993. Practical Neural Network Recipes in C++. Academic Press: San Diego, CA.
24. Mihalakakou G. 2002. On estimating soil surface temperature profiles. Energy Build. 34: 251-259.
25. Nash JE, Sutcliffe JV. 1970. River flow forecasting through conceptual models: 1. A discussion of principles. J. Hydrol. 10: 282-290.
26. NeuroSolutions. 2003. The Neural Network Simulation Environment. NeuroDimension Inc: Gainesville, FL.
27. Ozturk M, Salman O, Koc M. 2011. Artificial neural network model for estimating the soil temperature. Can. J. Soil Sci. 91: 551-562.
28. Plauborg F. 2002. Simple model for 10cm soil temperature in different soils with short grass. Eur. J. Agron. 17: 173-179.
29. Rezaeianzadeh M, Stein A, Tabari H, Abghari H, Jalalkamali N, Zia Hosseinipour E, et al. 2013. Comparative assessment of a conceptual hydrological model and artificial neural networks for daily outflows forecasting. Int. J. Environ. Sci. Technol. 10(16): 1181-1192.
30. Rezaeianzadeh M, Tabari H, Arabi Yazdi A, Isik S, Kalin L. 2014. Flood flow forecasting using ANN, ANFIS and regression models. Neural Comput. Appl. 25: 25-37.
31. Schaetzl RJ, Tomczak DM. 2001. Wintertime temperature in the fine-textured soils of the Saginaw valley, Michigan. Great Lakes Geogr. 8: 87-98.
32. Shahin MA, Jaksa MB, Maier HR. 2008. State of the art of artificial neural networks in geotechnical engineering. Electr. J. Geotech. Eng. 8: 1-26.
33. Shamseldin AY. 1997. Application of a neural network technique to rainfall-runoff modelling. J. Hydrol. 199: 272-294.
34. Shiri J, Kim S, Kisi O. 2014a. Estimation of daily dew point temperature using genetic programming and neural networks approaches. Hydrol. Res. 45(2): 165-181.
35. Shiri J, Marti P, Nazemi AH, Sadraddini AA, Kisi O, Lenderas G, et al. 2013. Local vs. external training of neuro-fuzzy and neural networks models for estimating reference evapotranspiration assessed through k-fold testing. Hydrol. Res., DOI: 10.2166/nh.2013.112.
36. Shiri J, Nazemi AH, Sadraddini AA, Landeras G, Kisi K, Fakheri Fard A, et al. 2014b. Comparison of heuristic and empirical approaches for estimating reference evapotranspiration from limited inputs in Iran. Comput. Electron. Agric. 108: 230-241.
37. Smith M. 1993. Neural Networks for Statistical Modeling. John Wiley & Sons, Inc: New York, NY.
38. Tabari H, Hosseinzadeh Talaee P. 2013. Multilayer perceptron for reference evapotranspiration estimation in a semiarid region. Neural Comput. Appl. 23(2): 341-348.
39. Tabari H, Marofi S, Sabziparvar AA. 2010. Estimation of daily pan evaporation using artificial neural network and multivariate non-linear regression. Irrig. Sci. 28: 399-406.
40. Tabari H, Sabziparvar AA, Ahmadi M. 2011. Comparison of artificial neural network and multivariate linear regression methods for estimation of daily soil temperature in an arid region. Meteorol. Atmos. Phys. 110: 135-142.
41. Wang W, Van Gelder PHAJM, Vrijling JK. 2005. Some issues about the generalization of neural networks for time series prediction. ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II; 559-564.
42. Wu W, Tang X-P, Guo N-J, Yang C, Liu H-B, Shang Y-F. 2013. Spatiotemporal modeling of monthly soil temperature using artificial neural networks. Theor. Appl. Climatol. 113: 481-494.
43. Yadav D, Naresh R, Sharma V. 2011. Stream flow forecasting using Levenberg-Marquardt algorithm approach. Int. J. Water Resour. Environ. Eng. 3(1): 30-40.
44. Yonaba H, Anctil F, Fortin V. 2010. Comparing sigmoid transfer functions for neural network multistep ahead streamflow forecasting. J. Hydrol. Eng. 15(4): 275-283.