Application of gene expression programming to predict daily dew point temperature

Applied Thermal Engineering

Volumn 112, Issue , 2017, Pages 1097-1107

  Mehdizadeh, Saeid ^a   Behmanesh, Javad ^a   Khalili, Keivan ^a  

^a URMIA UNIVERSITY   (Iran)

Author keywords

Daily dew point temperature; Estimation; Gene expression programming (GEP); Meteorological data

Indexed keywords

ATMOSPHERIC HUMIDITY; ATMOSPHERIC PRESSURE; ESTIMATION; GENE EXPRESSION; GENES; MEAN SQUARE ERROR; METEOROLOGY;

COEFFICIENT OF DETERMINATION; DEWPOINT TEMPERATURE; EFFECTIVE PARAMETERS; GENE EXPRESSION PROGRAMMING; MEAN AIR TEMPERATURES; METEOROLOGICAL DATA; METEOROLOGICAL PARAMETERS; ROOT MEAN SQUARE ERRORS;

ATMOSPHERIC TEMPERATURE;

EID: 84994577159     PISSN: 13594311     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.applthermaleng.2016.10.181     Document Type: Article

References (42)

1. [1] Emmel, C., Knippertz, P., Schulz, O., Climatology of convective density currents in the southern foothills of the Atlas Mountains. J. Geophys. Res., 115(D11), 2010, 10.1029/2009JD012863.
2. [2] Millan, H., Ghanbarian-Alavijeh, B., Garcia-Fornaris, I., Nonlinear dynamics of mean daily temperature and dew point time series at Babolsar, Iran, 1961–2005. Atmos. Res. 98 (2010), 89–101.
3. [3] Katul, G.G., Oren, R., Manzoni, S., Higgins, C., Parlange, M.B., Evapotranspiration: a process driving mass transport and energy exchange in the soil-plant-atmosphere-climate system. Rev. Geophys., 50, 2012, 10.1029/2011RG000366.
4. [4] Feld, S.I., Cristea, N.C., Lundquist, J.D., Representing atmospheric moisture content along mountain slopes: examination using distributed sensors in the Sierra Nevada, California. Water Resour. Res. 49 (2013), 4424–4441.
5. [5] Mohammadi, K., Shamshirband, S., Motamedi, S., Petkovic, D., Hashim, R., Gocic, M., Extreme learning machine based prediction of daily dew point temperature. Comput. Electron. Agric. 117 (2015), 214–225.
6. [6] Mahmood, R., Hubbard, K.G., Assessing bias in evapotranspiration and soil moisture estimates due to the use of modeled solar radiation and dew point temperature data. Agric. For. Meteorol. 130 (2005), 71–84.
7. [7] Agam, N., Berliner, P.R., Dew formation and water vapor absorption in semi-arid environments – a review. J. Arid Environ. 65 (2006), 572–590.
8. [8] Robinson, P.J., Temporal trends in United States dew point temperatures. Int. J. Climatol. 20 (2000), 985–1002.
9. [9] Kim, S., Singh, V.P., Lee, C.J., Seo, Y., Modeling the physical dynamics of daily dew point temperature using soft computing techniques. KSCE J. Civ. Eng. 19 (2015), 1930–1940.
10. [10] Kimball, J.S., Running, S.W., Nemani, R., An improved method for estimating surface humidity from daily minimum temperature. Agric. For. Meteorol. 85 (1997), 87–98.
11. [11] Shank, D.B., Hoogenboom, G., McClendon, R.W., Dew point temperature prediction using artificial neural networks. J. Appl. Meteorol. Climatol. 47 (2008), 1757–1769.
12. [12] Baghban, A., Bahadori, M., Rozyn, J., Lee, M., Abbas, A., Bahadori, A., Rahimali, A., Estimation of air dew point temperature using computational intelligence schemes. Appl. Therm. Eng. 93 (2016), 1043–1052.
13. [13] Hubbard, K.G., Mahmood, R., Carlson, C., Estimating daily dew point temperature for the northern Great Plains using maximum and minimum temperature. Agron. J. 95 (2003), 323–328.
14. [14] Shank, D.B., Dew Point Temperature Prediction Using Artificial Neural Networks. (MS Thesis), 2006, Harding University.
15. [15] Zadeh, L.A., Fuzzy Logic, Neural Networks and Soft Computing. One-Page Course Announcement of CS 294-4. 1992, University of California at Berkley.
16. [16] Chaturvedi, D.K., Soft Computing: Techniques and Its Applications in Electrical Engineering. 2008, Springer.
17. [17] Huang, Y., Lan, Y., Thomson, S.J., Fang, A., Hoffmann, W.C., Lacey, R.E., Development of soft computing and applications in agricultural and biological engineering. Comput. Electron. Agric. 71 (2010), 107–127.
18. [18] Gocic, M., Motamedi, S., Shamshirband, S., Petkovic, D., Hashim, S.Ch.R., Arif, M., Soft computing approaches for forecasting reference evapotranspiration. Comput. Electron. Agric. 113 (2015), 164–173.
19. [19] Aytek, A., Asce, M., Alp, M., An application of artificial intelligence for rainfall runoff modeling. J. Earth Syst. Sci. 117 (2008), 145–155.
20. [20] Guven, A., Linear genetic programming for time-series modeling of daily flow rate. J. Earth Syst. Sci. 118 (2009), 137–146.
21. [21] Shiri, J., Kisi, O., Yoon, H., Lee, K.K., Nazemi, A.H., Predicting groundwater level fluctuations with meteorological effect implications – a comparative study among soft computing techniques. Comput. Geosci. 56 (2013), 32–44.
22. [22] Traore, S., Guven, A., Regional-specific numerical models of evapotranspiration using gene-expression programming interface in Sahel. Water Resour. Manage 26 (2012), 4367–4380.
23. [23] Shiri, J., Sadraddini, A.A., Nazemi, A.H., Kisi, O., Landeras, G., Fakheri Fard, A., Marti, P., Generalizability of gene expression programming-based approaches for estimating daily reference evapotranspiration in coastal stations of Iran. J. Hydrol. 508 (2014), 1–11.
24. [24] Yassin, M.A., Alazba, A.A., Mattar, M.A., Artificial neural networks versus gene expression programming forestimating reference evapotranspiration in arid climate. Agric. Water Manage. 163 (2016), 110–124.
25. [25] Landeras, G., Lopez, J.J., Kisi, O., Shiri, J., Comparison of gene expression programming with neuro-fuzzy and neural network computing techniques in estimating daily incoming solar radiation in the Basque Country (Northern Spain). Energy Convers. Manage. 62 (2012), 1–13.
26. [26] Mehdizadeh, S., Behmanesh, J., Khalili, K., Comparison of artificial intelligence methods and empirical equations to estimate daily solar radiation. J. Atmos. Sol. – Terr. Phys. 146 (2016), 215–227.
27. [27] Ghani, A.A., Azamathulla, H.M., Gene expression programming for sediment transport in sewer pipe systems. J. Pipeline Syst. Eng. Pract. 2 (2011), 102–106.
28. [28] Zounemat-Kermani, M., Hourly predictive Levenberg–Marquardt ANN and multi linear regression models for predicting of dew point temperature. Meteorol. Atmos. Phys. 117 (2012), 181–192.
29. [29] Nadig, K., Potter, W., Hoogenboom, G., McClendon, R., Comparison of individual and combined ANN models for prediction of air and dew point temperature. Appl. Intell. 39 (2013), 354–366.
30. [30] Shiri, J., Kim, S., Kisi, O., Estimation of daily dew point temperature using genetic programming and neural networks approaches. Hydrol. Res. 45 (2014), 165–181.
31. [31] Mohammadi, K., Shamshirband, S., Petkovic, D., Yee, P.L., Mansor, Z., Using ANFIS for selection of more relevant parameters to predict dew point temperature. Appl. Therm. Eng. 96 (2016), 311–319.
32. [32] Amirmojahedi, M., Mohammadi, K., Shamshirband, S., Seyed Danesh, A., Mostafaeipour, A., Kamsin, A., A hybrid computational intelligence method for predicting dew point temperature. Environ. Earth Sci., 75, 2016, 415.
33. [33] Al-Shammari, E., Mohammadi, K., Keivani, A., Ab Hamid, S., Akib, S., Shamshirband, S., Petkovic, D., Prediction of daily dew point temperature using a model combining the support vector machine with firefly algorithm. J. Irrig. Drain. Engin., 142, 2016.
34. [34] ZareNezhad, B., Aminian, A., A multi-layer feed forward neural network model for accurate prediction of flue gas sulfuric acid dew points in process industries. Appl. Therm. Eng. 30 (2010), 692–696.
35. [35] Wang, Y.C., Tang, G.H., Prediction of sulfuric acid dew point temperature on heat transfer fin surface. Appl. Therm. Eng. 98 (2016), 492–501.
36. [36] Xiang, B., Tang, B., Wu, Y., Yang, H., Zhang, M., Lu, J., Predicting acid dew point with a semi-empirical model. Appl. Therm. Eng. 106 (2016), 992–1001.
37. [37] UNEP, World Atlas of Desertification. 1993, The United Nations Environment Programme (UNEP), London.
38. [38] Allen, R.G., Pereira, L.S., Raes, D., Smith, M., Crop Evapotranspiration-Guidelines for Computing Crop Water Requirements. FAO Irrigation and Drainage Paper 56, 1998, United Nations Food and Agriculture Organization, Rome.
39. [39] Ferreira, C., Gene expression programming: a new adaptive algorithm for solving problems. Complex. Syst. 13 (2001), 87–129.
40. [40] Sajjadi, S., Shamshirband, S., Alizamir, M., Yee, P.L., Mansor, Z., Manaf, A.A., Altameem, T.A., Mostafaeipour, A., Extreme learning machine for prediction of heat load in district heating systems. Energy Build. 122 (2016), 222–227.
41. [41] Wang, S., Fu, Z.Y., Chen, H.S., Nie, Y.P., Wang, K.L., Modeling daily reference ET in the Karst area of northwest Guangxi (China) using gene expression programming (GEP) and artificial neural network (ANN). Theor. Appl. Climatol., 2016, 10.1007/s00704-015-1602-z.
42. [42] Yao, W., Li, Z., Wang, Y., Jiang, F., Hu, L., Evaluation of global solar radiation models for Shanghai, China. Energy Convers. Manage. 84 (2014), 597–612.