Expression of Concern: A hybrid computational intelligence method for predicting dew point temperature (Environmental Earth Sciences, (2016), 75, 5, (415), 10.1007/s12665-015-5135-7);A hybrid computational intelligence method for predicting dew point temperature

Environmental Earth Sciences

Volumn 75, Issue 5, 2016, Pages 1-12

  Amirmojahedi, Mohsen ^a   Mohammadi, Kasra ^b   Shamshirband, Shahaboddin ^a   Seyed Danesh, Amir ^c   Mostafaeipour, Ali ^d   Kamsin, Amirrudin ^a  

^a UNIVERSITY OF MALAYA   (Malaysia)

^b University of Massachusetts   (United States)

^c PAYAME NOOR UNIVERSITY   (Iran)

^d YAZD UNIVERSITY   (Iran)

Author keywords

Daily dew point temperature; Extreme learning machine (ELM); Hybrid approach; Prediction; Statistical analysis; Wavelet transform (WT) algorithm

Indexed keywords

COUPLINGS; ERRORS; FORECASTING; KNOWLEDGE ACQUISITION; LEARNING ALGORITHMS; MEAN SQUARE ERROR; NEURAL NETWORKS; STATISTICAL METHODS; SUPPORT VECTOR MACHINES; WAVELET TRANSFORMS;

COEFFICIENT OF DETERMINATION; DEWPOINT TEMPERATURE; EXTREME LEARNING MACHINE; HYBRID APPROACH; HYBRID COMPUTATIONAL INTELLIGENCE; MEAN ABSOLUTE PERCENTAGE ERROR; ROOT MEAN SQUARE ERRORS; STATISTICAL INDICATORS;

MACHINE LEARNING;

ARTIFICIAL NEURAL NETWORK; COMPUTER SIMULATION; DEW POINT; PREDICTION; RELATIVE HUMIDITY; RESEARCH METHOD; STATISTICAL ANALYSIS; TEMPERATURE; TRANSFORM; WAVELET;

EID: 84959104448     PISSN: 18666280     EISSN: 18666299     Source Type: Journal    
DOI: 10.1007/s12665-020-09032-0     Document Type: Erratum

References (61)

1. Adamowski J, Chan HF (2011) A wavelet neural network conjunction model for groundwater level forecasting. J Hydrol 407(1):28–40
2. Agam N, Berliner PR (2006) Dew formation and water vapor absorption in semi-arid environments—a review. J Arid Environ 65:572–590
3. Asefa T, Kemblowski M, McKee M, Khalil A (2006) Multi-time scale stream flow predictions: the support vector machines approach. J Hydrol 318:7–16
4. Atzema AJ, Jacobs AFG, Wartena L (1990) Moisture distribution within a maize crop due to dew. Neth J Agric Sci 38:117–129
5. Aziz A, Wong K (1992) Neural-network approach to the determination of aquifer parameters. Ground Water GRWAAP 30:164–166
6. Balkhair K (2002) Aquifer parameters determination for large diameter wells using neural network approach. J Hydrol 265:118–128
7. Burrus CS, Gopinath RA, Guo H (1997) Introduction to wavelets and wavelet transforms: a primer. Prentice Hall, Englewood Cliffs
8. Chau K (2007) Reliability and performance-based design by artificial neural network. Adv Eng Softw 38:145–149
9. Duzen H, Aydin H (2012) Sunshine-based estimation of global solar radiation on horizontal surface at Lake Van region (Turkey). Energy Convers Manag 58:35–46
10. Ghouti L, Sheltami TR, Alutaibi KS (2013) Mobility prediction in mobile ad hoc networks using extreme learning machines. Proc Comput Sci 19:305–312
11. Huang GB, Zhu QY, Siew CK (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. Int Jt Conf Neural Netw 2:985–990
12. Huang GB, Zhu QY, Siew CK (2006a) Extreme learning machine: theory and applications. Neurocomputing 70:489–501
13. Huang GB, Chen L, Siew CK (2006b) Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans Neural Netw 17:879–892
14. Huang G, Huang GB, Song S, You K (2015) Trends in extreme learning machines: a review. Neural Netw 61:32–48
15. Hubbard KG, Mahmood R, Carlson C (2003) Estimating daily dew point temperature for the northern Great Plains using maximum and minimum temperature. Agron J 95(2):323–328
16. Jawerth B, Sweldens W (1994) An overview of wavelet based multiresolution analyses. SIAM Rev 36(3):377–412
17. Ji Y, Sun S (2013) Multitask multiclass support vector machines: model and experiments. Pattern Recogn 46(3):914–924
18. Kalteh AM (2013) Monthly river flow forecasting using artificial neural network and support vector regression models coupled with wavelet transform. Comput Geosci 54:1–8
19. Kavousi-Fard A, Samet H, Marzbani F (2014) A new hybrid modified firefly algorithm and support vector regression model for accurate short term load forecasting. Expert Syst Appl 41:6047–6056
20. Kim S, Singh VP, Lee CJ, Seo Y (2014) Modeling the physical dynamics of daily dew point temperature using soft computing techniques. KSCE J Civ Eng. doi:10.1007/s12205-014-1197-4
21. Kimball JS, Running SW, Nemani R (1997) An improved method for estimating surface humidity from daily minimum temperature. Agric For Meteorol 85:87–98
22. Kisi O, Cimen M (2012) Precipitation forecasting by using wavelet-support vector machine conjunction model. Eng Appl Artif Intell 25:783–792
23. Kottek M, Grieser J, Beck C, Rudolf B, Rubel F (2006) World map of the Koppen–Geiger climate classification updated. Meteorol Z 15(3):259–263
24. Lee S-W, Verri A (2003) Support vector machines for computer vision and pattern recognition. World Scientific, Singapore
25. Liang NY, Huang GB, Rong HJ, Saratchandran P, Sundararajan N (2006) A fast and accurate on-line sequential learning algorithm for feedforward networks. IEEE Trans Neural Netw 17:1411–1423
26. Liang R-P, Huang S-Y, Shi S-P, Sun X-Y, Suo S-B, Qiu J-D (2012) A novel algorithm combining support vector machine with the discrete wavelet transform for the prediction of protein sub cellular localization. Comput Biol Med 42:180–187
27. Lu WZ, Wang WJ (2005) Potential assessment of the “support vector machine” method in forecasting ambient air pollutant trends. Chemosphere 59:693–701
28. Mallat SGA (1989) Theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11(7):674–693
29. Mallat SGA (2009) A wavelet tour of signal processing: the sparse way, 3rd edn. Academic Press, Burlington
30. Mohammadi K, Shamshirband S, Seyed Danesh A, Zamani M, Sudheer C (2015) Horizontal global solar radiation estimation using hybrid SVM-firefly and SVM-wavelet algorithms: a case study. Nat Hazards. doi:10.1007/s11069-015-2047-5
31. Mohammadi K, Shamshirband S, Petkovic D, Yee PL, Mansor Z (2016) Using ANFIS for selection of more relevant parameters to predict dew point temperature. Appl Therm Eng 96:311–319
32. Nadig K, Potter W, Hoogenboom G, McClendon R (2013) Comparison of individual and combined ANN models for prediction of air and dew point temperature. Appl Intell 39:354–366. doi:10.1007/s10489-012-0417-1
33. Nian R, He B, Zheng B, Heeswijk MV, Yu Q, Miche Y et al (2014) Extreme learning machine towards dynamic model hypothesis in fish ethology research. Neurocomputing 128:273–284
34. Olatomiwa L, Mekhilef S, Shamshirband S, Mohammadi K, Petkovic D, Sudheer C (2015) A support vector machine-firefly algorithm-based model for global solar radiation prediction. Sol Energy 115:632–644
35. Partal T, Kisi O (2007) Wavelet and neuro-fuzzy conjunction model for precipitation forecasting. J Hydrol 342:199–212
36. Peng Z, Chu F (2004) Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography. Mech Syst Signal Process 18(2):199–221
37. Rajasekaran S, Gayathri S, Lee TL (2008) Support vector regression methodology for storm surge predictions. Ocean Eng 35(16):1578–1587
38. Schalkoff RJ (1997) Artificial neural networks. McGraw-Hill Higher Education, New York
39. Shamshirband S, Petković D, Pavlović NT, Sudheer C, Torki A, Altameem TA, Gani A (2015) Support vector machine firefly algorithm based optimization of lens system. Appl Opt 54:37–45
40. Shamshirband S, Mohammadi K, Khorasanizadeh H, Yee PL, Lee M, Petković D, Zalnezhad E (2016) Estimating the diffuse solar radiation using a coupled support vector machine–wavelet transform model. Renew Sustain Energy Rev 56:428–435
41. Shank DB (2006) Dew point temperature prediction using artificial neural networks, MS thesis. Harding University
42. Shank DB, Hoogenboom G, McClendon RW (2008) Dew point temperature prediction using artificial neural networks. Appl Meteorol Climatol 47:1757–1769
43. Shiri J, Kim S, Kisi O (2014) Estimation of daily dew point temperature using genetic programming and neural networks approaches. Hydrol Res 45(2):165–181
44. Shrivastava NA, Panigrahi BK (2014) A hybrid wavelet-ELM based short term price forecasting for electricity Markets. Electr Power Energy Syst 55:41–50
45. Slatyer RO (1967) Plant–water relationships. Academic Press, London
46. Snyder RL, Melo-Abreu JPd (2005) Frost protection: fundamentals, practice and economics, vol 1. Food and Agricultural Organization of the United Nations, Rome
47. Sudheer C, Sohani SK, Kumar D, Malik A, Chahar BR, Nema AK et al (2014) A support vector machine-firefly algorithm based forecasting model to determine malaria transmission. Neurocomputing 129:279–288
48. Sun S (2013) A survey of multi-view machine learning. Neural Comput Appl 23(7–8):2031–2038
49. Vapnik V (2000) The nature of statistical learning theory. Springer, New York
50. Vapnik VN, Vapnik V (1998) Statistical learning theory, vol 2. Wiley, New York
51. Wang W, Ding J (2003) Wavelet network model and its application to the prediction of hydrology. Nat Sci 1(1):67–71
52. Wang X, Han M (2014) Online sequential extreme learning machine with kernels for nonstationary time series prediction. Neurocomputing 145:90–97
53. Wang DD, Wang R, Yan H (2014) Fast prediction of protein–protein interaction sites based on Extreme Learning Machines. Neurocomputing 128:258–266
54. Went FW (1955) Fog, mist dew and other sources of water. Year book agriculture. US Department of Agriculture, Washington, DC, pp 103–109
55. Wong PK, Wong KI, Vong CM, Cheung CS (2015) Modeling and optimization of biodiesel engine performance using kernel-based extreme learning machine and cuckoo search. Renew Energy 74:640–647
56. Wu KP, Wang SD (2009) Choosing the kernel parameters for support vector machines by the inter-cluster distance in the feature space. Pattern Recogn 42(5):710–717
57. Xiong T, Bao Y, Hu Z (2014) Multiple-output support vector regression with a firefly algorithm for interval-valued stock price index forecasting. Knowl Based Syst 55:87–100
58. Yang H, Huang K, King I, Lyu MR (2009) Localized support vector regression for time series prediction. Neurocomputing 72(10):2659–2669
59. Yu Q, Miche Y, Séverin E, Lendasse A (2014) Bankruptcy prediction using extreme learning machine and financial expertise. Neurocomputing 128:296–302
60. Zhao Z, Li P, Xu X (2013) Forecasting model of coal mine water inrush based on extreme learning machine. Appl Math Inf Sci 7:1243–1250
61. Zounemat-Kermani M (2012) Hourly predictive Levenberg–Marquardt ANN and multi linear regression models for predicting of dew point temperature. Meteorol Atmos Phys 117:181–192