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A new wavelet conjunction approach for estimation of relative humidity: wavelet principal component analysis combined with ANN

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Abstract

Relative humidity (RH) has an important effect on precipitation, especially in arid and semiarid regions. Prediction of RH has been the focus of attention of climate change researchers as well. In this investigation, the accuracy of six intelligent models, including an artificial neural network (ANN), a co-active neuro-fuzzy inference system (CANFIS), principal component analysis (PCA) combined with ANN (PCA–ANN) and three hybrid wavelet-artificial intelligence models, including WANN, WCANFIS and WPCA–ANN, was evaluated in daily RH prediction. Thirty weather stations located in different climates in Iran for the period 2000–2010 were selected for the evaluation and comparison of these models. The performance of each model was evaluated using correlation coefficient (r) and normal root mean square error (NRMSE). Based on the statistical evaluation criteria, the accuracy ranks of the six models were: WPCA–ANN, WCANFIS, WANN, PCA–ANN, ANN and CANFIS. The results indicated that the WPCA–ANN model was the optimal model for estimation of RH, and the range of NRMSE and r values were from 0.009 to 0.080 and from 0.996 to 0.999, respectively. Overall, WPCA–ANN is a new approach that can be successfully applied to predict RH with a high accuracy.

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References

  1. Akbary M (2015) Combinatory Mediterranean-Sudanese systems role in the occurrence of heavy rainfalls (case study: south west of Iran). Meteorol Atmos Phys 127(6):675–683

    Google Scholar 

  2. Alizadeh MJ, Kavianpour MR, Kisi K, Nourani V (2017) A new approach for simulating and forecasting the rainfall-runoff process within the next two months. J Hydrol 548:588–597

    Google Scholar 

  3. Areerachakul S (2012) Comparison of ANFIS and ANN for estimation of biochemical oxygen demand parameter in surface water. Changes 257:13365

    Google Scholar 

  4. Aytek A (2008) Co-active neurofuzzy inference system for evapotranspiration modeling. A fusion of foundations. Methodologies and applications. Soft Comput 13(7):691–700

    Google Scholar 

  5. Bayazit M, Aksoy H (2001) Using wavelets for data generation. J Appl Stat 28:157–166

    MathSciNet  MATH  Google Scholar 

  6. Cannas B, Fanni A, See L, Sias G (2006) Data preprocessing for river flow forecasting using neural networks: wavelet transforms and data partitioning. Phys Chem Earth 31(18):1164–1171

    Google Scholar 

  7. Chaudhuri S, Chattopadhyay S (2005) Neuro-computing based short range prediction of some meteorological parameters during the pre-monsoon season. A fusion of foundations methodologies and applications. Soft Comput 9(5):349–354

    Google Scholar 

  8. Chen J, Chen C (2017) Uncertainty analysis in humidity measurements by the psychrometer method. Sensors 17(368):2–19

    Google Scholar 

  9. Citakoglu H (2016) Comparison of artificial intelligence techniques for prediction of soil temperatures in Turkey. Theor Appl Climatol 1:1–12

    Google Scholar 

  10. Critchfield H (1974) General climatology. Prentice-Hall, Englewood Cliffs

    Google Scholar 

  11. Elliott W, Angell J (1997) Variations of cloudiness, precipitable water, and relative humidity over the United States: 1973–1993. Geophys Res Lett 24(1):41–44

    Google Scholar 

  12. Gnana Sheela K, Deepa SN (2013) Review on methods to fix number of hidden neurons in neural networks. Math Probl Eng 2013:11

    Google Scholar 

  13. Haykin S (1999) Neural networks: a comprehensive foundation, 2nd edn. Prentice Hall, New Jersey

    MATH  Google Scholar 

  14. John VO, Soden BJ (2007) Temperature and humidity biases in global climate models and their impact on climate feedbacks. Geophys Res Lett 34:L18704. https://doi.org/10.1029/2007GL030429

    Article  Google Scholar 

  15. Junior L, Souza R, Menezes M, Cassiano K (2015) Artificial neural network and wavelet decomposition in the forecast of global horizontal solar radiation. Pesqui Operacional 35(1):73–90

    Google Scholar 

  16. Karthika BS, Paresh CD (2016) Modeling of air temperature using ANFIS by wavelet refined parameters. Int J Intell Syst Appl 8(1):25–34

    Google Scholar 

  17. Katiraie Boroujerdy PS, Arkian F, Rezai F (2011) Trend of humidity (specific and relative) in synoptic stations in Iran in period 1976–2005. J Mar Sci Technol 6:17–29

    Google Scholar 

  18. Kaur A, Sharma JK, Agrawal S (2011) Artificial neural networks in forecasting maximum and minimum relative humidity. Int J Comput Sci Netw Secur 11(5):197–199

    Google Scholar 

  19. Kumar P, Kumar D, Tiwari AK (2012) Evaporation estimation using artificial neural networks and adaptive Neuro-Fuzzy inference system techniques. Pak J Meteorol 8:81–88

    Google Scholar 

  20. Loele G, De Luca M, Dinç E, Oliverio F, Ragno G (2011) Artificial neural network combined with principal component analysis for resolution of complex pharmaceutical formulations. Chem Pharm Bull (Tokyo) 59(1):35–40

    Google Scholar 

  21. Mba L, Meukam P, Kemajou A (2016) Application of artificial neural network for predicting hourly indoor air temperature and relative humidity in modern building in humid region. Energy Build 121:32–42

    Google Scholar 

  22. Milewski R, Jankowska D, Cwalina U, Milewska A, Citko D, Więsak T, Morgan A, Wołczyński S (2016) Application of artificial neural networks and principal component analysis to predict results of infertility treatment using the IVF method, studies in logic. Gramm Rhetor 47(1):33–46

    Google Scholar 

  23. Moghaddamnia A, GhafariGousheh M, Piri J, Amin S, Han D (2009) Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques. Adv Water Resour 32(1):88–97

    Google Scholar 

  24. Moustris KP, Larissi LK, Nastos PT, Paliatsos AG (2011) Precipitation forecast using artificial neural networks in specific regions of greece. Water Resour Manag 25(8):1979–1993

    Google Scholar 

  25. Nourani N, HosseiniBaghanam A, Adamowski A, Kisi O (2014) Applications of hybrid wavelet–artificial intelligence models in hydrology: a review. J Hydrol 514:358–377

    Google Scholar 

  26. Okkan U, Serbes ZA (2013) The combined use of wavelet transform and black box models in reservoir inflow modeling. J Hydrol Hydromech 61(2):112–119

    Google Scholar 

  27. Papantoniou S, Kolokotsa D (2016) Prediction of outdoor air temperature using neural networks: application in 4 European cities. Energy Build 114:72–79

    Google Scholar 

  28. Patil A, Deka (2016) Performance evaluation of hybrid Wavelet-ANN and Wavelet-ANFIS models for estimating evapotranspiration in arid regions of India. Neural Comput Appl 28:275–285

    Google Scholar 

  29. Philippopoulos K, Deligiorgi D, Kouroupetroglou G (2015) Artificial neural network modeling of relative humidity and air temperature spatial and temporal distributions over complex terrains. Pattern Recognit Appl Methods 318:171–187

    Google Scholar 

  30. Premalatha N, Arasu A (2016) Prediction of solar radiation for solar systems by using ANN models with different back propagation algorithms. J Appl Res Technol 14(3):206–214

    Google Scholar 

  31. Ramana R, Krishna B, Kumar SR, Pandey NG (2013) Monthly rainfall prediction using wavelet neural network analysis. Water Resour Manag 27:3697–3711

    Google Scholar 

  32. Ranganayaki V, Deepa SN (2016) An intelligent ensemble neural network model for wind speed prediction in renewable energy systems. Sci World J 2016:1–14

    Google Scholar 

  33. Samer A, Tamer K (2012) Modeling of relative humidity using artificial neural network. Asian Econ Soc Soc 2(2):81–86

    Google Scholar 

  34. Shafaei M, Kisi O (2016) Predicting river daily flow using wavelet-artificial neural networks based on regression analyses in comparison with artificial neural networks and support vector machine models. Neural Comput Appl 28:1–14

    Google Scholar 

  35. Soden BJ, Jackson DL, Ramaswamy V (2005) The radiative signature of upper tropospheric moistening. Science 310:841–844

    Google Scholar 

  36. Tabari H, HosseinzadehTalaee P, Willems P (2014) Short-term forecasting of soil temperature using artificial neural network. Meteorol Appl 22(3):576–585

    Google Scholar 

  37. Tabari H, Willems P (2016) Daily precipitation extremes in Iran: decadal anomalies and possible drivers. J Am Water Resour Assoc 52:541–599

    Google Scholar 

  38. Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst 15(1):116–132

    MATH  Google Scholar 

  39. Tokar AS, Markus M (2000) Precipitation runoff modeling using artificial neural network and conceptual models. J Hydrol Eng ASCE 5:156–161

    Google Scholar 

  40. Wang L, Kisi K, Zounemat-Kermani M, Li H (2017) Pan evaporation modeling using six different heuristic computing methods in different climates of China. J Hydrol 544:407–427

    Google Scholar 

  41. Xiao Yong, Xiaomin Gu, Yin Shiyang, Shao Jingli, Cui Yali, Zhang Qiulan, Niu Yong (2016) Geostatistical interpolation model selection based on ArcGIS and spatio–temporal variability analysis of groundwater level in piedmont plains, northwest China. Springer Plus 5(425):1–15

    Google Scholar 

  42. Yan Q, Ma C, Song Y, Zhou W (2016) Wavelet and ANFIS combination model for groundwater level forecasting. Revista Tecnica de la Facultad de Ingenieria Universidad del Zulia 39(2):317–328

    Google Scholar 

  43. Yousefi F, Mohammadiyan S, Karimi H (2016) Application of artificial neural network and PCA to predict the thermal conductivities of nanofluids. Heat and Mass Transf 52(10):2141–2154

    Google Scholar 

  44. Zareabyaneh H, Bayat-Varkeshi M, Golmohammadi G, Mohammadi K (2016) Soil temperature estimation using an artificial neural network and co-active neuro-fuzzy inference system in two different climates. Arab J Geosci 9(377):1–10

    Google Scholar 

  45. Zhang Q, Qi T, Li J, Singh V, Wang A (2015) Spatiotemporal variations of pan evaporation in China during 1960–2005: changing patterns and causes. Int J Climatol 35(6):903–912

    Google Scholar 

  46. Zhang Y, Li H, Hou A, Havel J (2006) Artificial neural networks based on principal component analysis input selection for quantification in overlapped capillary electrophoresis peaks. Chemom Intell Lab Syst 82(1):165–175

    Google Scholar 

  47. Zhou L, Ma W, Zhang H, Li L, Tang L (2015) Developing a PCA–ANN model for predicting chlorophyll a concentration from field hyperspectral measurements in Dianshan Lake, China. Water Qual Expos Health 7(4):591–602

    Google Scholar 

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Authors and Affiliations

  1. Agriculture Faculty, Malayer University, Malayer, Iran

    Maryam Bayatvarkeshi & Rojin Fasihi

  2. HLV2 K Engineering, Brampton, ON, Canada

    Kourosh Mohammadi

  3. Faculty of Natural Sciences and Engineering, Ilia State University, Tbilisi, Georgia

    Ozgur Kisi

Authors
  1. Maryam Bayatvarkeshi
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  2. Kourosh Mohammadi
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  3. Ozgur Kisi
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  4. Rojin Fasihi
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Correspondence to Ozgur Kisi.

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Bayatvarkeshi, M., Mohammadi, K., Kisi, O. et al. A new wavelet conjunction approach for estimation of relative humidity: wavelet principal component analysis combined with ANN. Neural Comput & Applic 32, 4989–5000 (2020). https://doi.org/10.1007/s00521-018-3916-0

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  • DOI: https://doi.org/10.1007/s00521-018-3916-0

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