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Volumn 121, Issue 1-2, 2015, Pages 377-387

Modeling soil temperatures at different depths by using three different neural computing techniques

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EID: 84931568449     PISSN: 0177798X     EISSN: 14344483     Source Type: Journal    
DOI: 10.1007/s00704-014-1232-x     Document Type: Article
Times cited : (90)

References (37)
  • 1
    • 77956838672 scopus 로고    scopus 로고
    • Comparison of multivariate regression and artificial neural networks for peak urban water-demand forecasting: evaluation of different ANN learning algorithms
    • Adamowski J, Karapataki C (2010) Comparison of multivariate regression and artificial neural networks for peak urban water-demand forecasting: evaluation of different ANN learning algorithms. J Hydrol Eng 15(10):729–743
    • (2010) J Hydrol Eng , vol.15 , Issue.10 , pp. 729-743
    • Adamowski, J.1    Karapataki, C.2
  • 2
    • 78650014065 scopus 로고    scopus 로고
    • Prediction of soil temperature using regression and artificial neural network models
    • Bilgili M (2010) Prediction of soil temperature using regression and artificial neural network models. Meteor Atmos Phys 110:59–70
    • (2010) Meteor Atmos Phys , vol.110 , pp. 59-70
    • Bilgili, M.1
  • 3
    • 84871989698 scopus 로고    scopus 로고
    • Estimating soil temperature using neighboring station data via multi-nonlinear regression and artificial neural network models
    • 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
    • (2013) Environ Monit Assess , vol.185 , pp. 347-358
    • Bilgili, M.1    Sahin, B.2    Sangun, L.3
  • 4
    • 27944503514 scopus 로고    scopus 로고
    • Generalized regression neural network in monthly flow forecasting
    • Cigizoglu HK (2005) Generalized regression neural network in monthly flow forecasting. Civ Eng Environ Syst 22(2):71–81
    • (2005) Civ Eng Environ Syst , vol.22 , Issue.2 , pp. 71-81
    • Cigizoglu, H.K.1
  • 5
    • 0032123339 scopus 로고    scopus 로고
    • Runoff forecasting using RBF networks with OLS algorithm
    • Fernando DAK, Jayawardena AW (1998) Runoff forecasting using RBF networks with OLS algorithm. J Hydrol Eng 3(3):203–209
    • (1998) J Hydrol Eng , vol.3 , Issue.3 , pp. 203-209
    • Fernando, D.A.K.1    Jayawardena, A.W.2
  • 6
    • 51749102673 scopus 로고    scopus 로고
    • An improved force-restore method for soil temperature prediction
    • Gao Z, Horton R, Wang L, Liu H, Wen J (2008) An improved force-restore method for soil temperature prediction. Eur J Soil Sci 59:972–981
    • (2008) Eur J Soil Sci , vol.59 , pp. 972-981
    • Gao, Z.1    Horton, R.2    Wang, L.3    Liu, H.4    Wen, J.5
  • 7
    • 33745624576 scopus 로고    scopus 로고
    • Soil temperatures at Armagh Observatory, Northern Ireland, from 1904 to 2002
    • García-Suárez AM, Butler CJ (2006) Soil temperatures at Armagh Observatory, Northern Ireland, from 1904 to 2002. Int J Climatol 26:1075–1089
    • (2006) Int J Climatol , vol.26 , pp. 1075-1089
    • García-Suárez, A.M.1    Butler, C.J.2
  • 8
    • 0035424839 scopus 로고    scopus 로고
    • Prediction of soil temperature by using artificial neural networks algorithms
    • George RK (2001) Prediction of soil temperature by using artificial neural networks algorithms. Nonlinear Anal 47:1737–1748
    • (2001) Nonlinear Anal , vol.47 , pp. 1737-1748
    • George, R.K.1
  • 9
    • 0141625690 scopus 로고    scopus 로고
    • A daily soil temperature dataset and soil temperature climatology of the contiguous United States
    • Hu Q, Feng S (2003) A daily soil temperature dataset and soil temperature climatology of the contiguous United States. J Appl Meteorol 42:1139–1156
    • (2003) J Appl Meteorol , vol.42 , pp. 1139-1156
    • Hu, Q.1    Feng, S.2
  • 10
    • 40949103966 scopus 로고    scopus 로고
    • Measuring soil temperature and moisture using wireless MEMS sensors
    • Jackson TS, Mansfield K, Saafi M, Colman T, Romine P (2008) Measuring soil temperature and moisture using wireless MEMS sensors. Measurement 41:381–390
    • (2008) Measurement , vol.41 , pp. 381-390
    • Jackson, T.S.1    Mansfield, K.2    Saafi, M.3    Colman, T.4    Romine, P.5
  • 11
    • 84873417698 scopus 로고    scopus 로고
    • Prediction of annual and seasonal temperature variation using artificial neural network
    • Jebamalar AS, Raja SAT, Bai SJS (2012) Prediction of annual and seasonal temperature variation using artificial neural network. Indian J Radio Space Phys 41:48–57
    • (2012) Indian J Radio Space Phys , vol.41 , pp. 48-57
    • Jebamalar, A.S.1    Raja, S.A.T.2    Bai, S.J.S.3
  • 12
    • 0034308212 scopus 로고    scopus 로고
    • Predicting spatial and temporal patterns of soil temperature based on topography, surface cover and air temperature
    • Kang S, Kim S, Oh S, Lee D (2000) Predicting spatial and temporal patterns of soil temperature based on topography, surface cover and air temperature. For Ecol Manag 136:173–184
    • (2000) For Ecol Manag , vol.136 , pp. 173-184
    • Kang, S.1    Kim, S.2    Oh, S.3    Lee, D.4
  • 13
    • 84891500343 scopus 로고    scopus 로고
    • Modeling daily soil temperature using data-driven models and spatial distribution
    • Kim S, Singh VJ (2014) Modeling daily soil temperature using data-driven models and spatial distribution. Theor Appl Climatol. doi:10.1007/s00704-013-1065-z
    • (2014) Theor Appl Climatol
    • Kim, S.1    Singh, V.J.2
  • 14
    • 33845321370 scopus 로고    scopus 로고
    • Generalized regression neural networks for evapotranspiration modelling
    • Kisi O (2006) Generalized regression neural networks for evapotranspiration modelling. Hydrol Sci J 51(6):1092–1105
    • (2006) Hydrol Sci J , vol.51 , Issue.6 , pp. 1092-1105
    • Kisi, O.1
  • 15
    • 34548146808 scopus 로고    scopus 로고
    • Streamflow forecasting using different artificial neural network algorithms
    • Kisi O (2007) Streamflow forecasting using different artificial neural network algorithms. J Hydrol Eng 12(5):532–539
    • (2007) J Hydrol Eng , vol.12 , Issue.5 , pp. 532-539
    • Kisi, O.1
  • 16
    • 61749102355 scopus 로고    scopus 로고
    • Daily pan evaporation modelling using multi-layer perceptrons and radial basis neural networks
    • Kisi O (2009) Daily pan evaporation modelling using multi-layer perceptrons and radial basis neural networks. Hydrol Process 23:213–223
    • (2009) Hydrol Process , vol.23 , pp. 213-223
    • Kisi, O.1
  • 17
    • 84871475008 scopus 로고    scopus 로고
    • Modeling monthly pan evaporations using fuzzy genetic approach
    • Kisi O, Tombul M (2013) Modeling monthly pan evaporations using fuzzy genetic approach. J Hydrol 477:203–212
    • (2013) J Hydrol , vol.477 , pp. 203-212
    • Kisi, O.1    Tombul, M.2
  • 18
    • 84887318010 scopus 로고    scopus 로고
    • Estimation of dew point temperature using neuro-fuzzy and neural network techniques
    • Kisi O, Kim S, Shiri J (2013) Estimation of dew point temperature using neuro-fuzzy and neural network techniques. Theor Appl Climatol 114:365–373
    • (2013) Theor Appl Climatol , vol.114 , pp. 365-373
    • Kisi, O.1    Kim, S.2    Shiri, J.3
  • 19
    • 84931591150 scopus 로고    scopus 로고
    • Soil temperature prediction using time-delay neural networks
    • Advances in meteorology, climatology and atmospheric physics, Springer Atmospheric Sciences
    • Mazou E, Alvertos N, Tsiros IX (2012) Soil temperature prediction using time-delay neural networks. In: CG, Helmis and PT Nastos (eds) Advances in meteorology, climatology and atmospheric physics. Springer Atmospheric Sciences
    • (2012) CG
    • Mazou, E.1    Alvertos, N.2    Tsiros, I.X.3
  • 20
    • 0036497467 scopus 로고    scopus 로고
    • On estimating soil surface temperature profiles
    • Mihalakakou G (2002) On estimating soil surface temperature profiles. Energy Build 34:251–259
    • (2002) Energy Build , vol.34 , pp. 251-259
    • Mihalakakou, G.1
  • 21
    • 49849106661 scopus 로고    scopus 로고
    • An ANN application for water quality forecasting
    • Palani S, Liong S, Tkalich P (2008) An ANN application for water quality forecasting. Mar Pollut Bull 56(9):1586–1597
    • (2008) Mar Pollut Bull , vol.56 , Issue.9 , pp. 1586-1597
    • Palani, S.1    Liong, S.2    Tkalich, P.3
  • 23
    • 69049087742 scopus 로고    scopus 로고
    • Estimating daily pan evaporation using artificial neural network in a semi-arid environment
    • Rahimikhoob A (2009) Estimating daily pan evaporation using artificial neural network in a semi-arid environment. Theor Appl Climatol 98:101–105
    • (2009) Theor Appl Climatol , vol.98 , pp. 101-105
    • Rahimikhoob, A.1
  • 24
    • 77953810978 scopus 로고    scopus 로고
    • Estimation of evapotranspiration based on only air temperature data using artificial neural networks for a subtropical climate in Iran
    • Rahimikhoob A (2010) Estimation of evapotranspiration based on only air temperature data using artificial neural networks for a subtropical climate in Iran. Theor Appl Climatol 101:83–91
    • (2010) Theor Appl Climatol , vol.101 , pp. 83-91
    • Rahimikhoob, A.1
  • 25
    • 84864424467 scopus 로고    scopus 로고
    • Hourly air temperature driven using multi-layer perceptron and radial basis function networks in arid and semi-arid regions
    • Rezaeian-Zadeh M, Zand-Parsa S, Abghari H, Zolghadr M, Singh VP (2012) Hourly air temperature driven using multi-layer perceptron and radial basis function networks in arid and semi-arid regions. Theor Appl Climatol 109:519–528
    • (2012) Theor Appl Climatol , vol.109 , pp. 519-528
    • Rezaeian-Zadeh, M.1    Zand-Parsa, S.2    Abghari, H.3    Zolghadr, M.4    Singh, V.P.5
  • 26
    • 0026254768 scopus 로고
    • A general regression neural network
    • Specht D (1991) A general regression neural network. IEEE Trans Neural Netw 2(6):568–576
    • (1991) IEEE Trans Neural Netw , vol.2 , Issue.6 , pp. 568-576
    • Specht, D.1
  • 27
    • 0038546820 scopus 로고    scopus 로고
    • Estimating actual evapotranspiration from limited climatic data using neural computing technique
    • Sudheer KP, Gosain AK, Ramasastri KS (2003) Estimating actual evapotranspiration from limited climatic data using neural computing technique. J Irrig Drain Eng 129(3):214–218
    • (2003) J Irrig Drain Eng , vol.129 , Issue.3 , pp. 214-218
    • Sudheer, K.P.1    Gosain, A.K.2    Ramasastri, K.S.3
  • 28
    • 77953692036 scopus 로고    scopus 로고
    • Estimation of daily pan evaporation using artificial neural network and multivariate nonlinear regression
    • Tabari H, Marofi S, Sabziparvar AA (2010a) Estimation of daily pan evaporation using artificial neural network and multivariate nonlinear regression. Irrig Sci 28:399–406
    • (2010) Irrig Sci , vol.28 , pp. 399-406
    • Tabari, H.1    Marofi, S.2    Sabziparvar, A.A.3
  • 29
    • 77952883472 scopus 로고    scopus 로고
    • Comparison of artificial neural network and combined models in estimating spatial distribution of snow depth and snow water equivalent in Samsami basin of Iran
    • Tabari H, Marofi S, Zare Abyaneh H, Sharifi MR (2010b) 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 Applic 19:625–635
    • (2010) Neural Comput Applic , vol.19 , pp. 625-635
    • Tabari, H.1    Marofi, S.2    Zare Abyaneh, H.3    Sharifi, M.R.4
  • 30
    • 78650738248 scopus 로고    scopus 로고
    • Comparison of artificial neural network and multivariate linear regression methods for estimation of daily soil temperature in an arid region
    • 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. Meteor Atmos Phys 110:135–142
    • (2011) Meteor Atmos Phys , vol.110 , pp. 135-142
    • Tabari, H.1    Sabziparvar, A.A.2    Ahmadi, M.3
  • 31
    • 76649134880 scopus 로고    scopus 로고
    • Testing hourly reference evapotranspiration approaches using lysimeter measurements in a semiarid climate
    • Trajkovic (2010) Testing hourly reference evapotranspiration approaches using lysimeter measurements in a semiarid climate. Hydrol Res 41(1):38–49
    • (2010) Hydrol Res , vol.41 , Issue.1 , pp. 38-49
  • 32
    • 0034234155 scopus 로고    scopus 로고
    • Estimation of FAO Blaney-Criddle b factor by RBF network
    • Trajkovic S, Stankovic M, Todorovic B (2000) Estimation of FAO Blaney-Criddle b factor by RBF network. J Irrig Drain Eng 126(4):268–271
    • (2000) J Irrig Drain Eng , vol.126 , Issue.4 , pp. 268-271
    • Trajkovic, S.1    Stankovic, M.2    Todorovic, B.3
  • 33
    • 84880763583 scopus 로고    scopus 로고
    • Spatiotemporal modeling of monthly soil temperature using artificial neural networks
    • 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
    • (2013) Theor Appl Climatol , vol.113 , pp. 481-494
    • Wu, W.1    Tang, X.-P.2    Guo, N.-J.3    Yang, C.4    Liu, H.-B.5    Shang, Y.-F.6
  • 34
    • 33947362356 scopus 로고    scopus 로고
    • Estimating evapotranspiration using artificial neural network and minimum climatological data
    • Zanetti SS, Sousa EF, Oliveira VPS, Almeida FT, Bernardo S (2007) Estimating evapotranspiration using artificial neural network and minimum climatological data. J Irrig Drain Eng 133(2):83–89
    • (2007) J Irrig Drain Eng , vol.133 , Issue.2 , pp. 83-89
    • Zanetti, S.S.1    Sousa, E.F.2    Oliveira, V.P.S.3    Almeida, F.T.4    Bernardo, S.5
  • 35
    • 84865770172 scopus 로고    scopus 로고
    • Hourly predictive Levenberg–Marquardt ANN and multi linear regression models for predicting of dew point temperature
    • Zounemat-Kermani M (2012) Hourly predictive Levenberg–Marquardt ANN and multi linear regression models for predicting of dew point temperature. Meteorog Atmos Phys 117(3–4):181–192
    • (2012) Meteorog Atmos Phys , vol.117 , Issue.3-4 , pp. 181-192
    • Zounemat-Kermani, M.1
  • 36
    • 84879749443 scopus 로고    scopus 로고
    • Hydrometeorological parameters in prediction of soil temperature by means of artificial neural network: case study in Wyoming
    • Zounemat-Kermani M (2013) Hydrometeorological parameters in prediction of soil temperature by means of artificial neural network: case study in Wyoming. J Hydrol Eng 18(6):707–718
    • (2013) J Hydrol Eng , vol.18 , Issue.6 , pp. 707-718
    • Zounemat-Kermani, M.1
  • 37
    • 84893806156 scopus 로고    scopus 로고
    • Principal component analysis (PCA) for estimating chlorophyll concentration using forward and generalized regression neural networks
    • Zounemat-Kermani M (2014) Principal component analysis (PCA) for estimating chlorophyll concentration using forward and generalized regression neural networks. Appl Artif Intell 28(1):16–29
    • (2014) Appl Artif Intell , vol.28 , Issue.1 , pp. 16-29
    • Zounemat-Kermani, M.1


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