Application of Wavelet Neural Network in Estimation of Average Air-temperature

Document Type : Research Paper

Authors

1 PhD. Water Engineering, Faculty of Agriculture, Lorestan University, Iran

2 B.Sc. student, Civil engineering, Lorestan University, Iran

Abstract

Standard weather station evaluates air-temperature and it is major descriptor for earth environment condition. Thus, estimation and estimation of average daily temperature is one of the main perquisites for agriculture programming and also water source management which is possible by empirical, quasi-empirical l and intelligent method. This study evaluates the application wavelet neural network (WNN) to estimation of average daily air-temperature in Sari weather station and also compares its efficiency with artificial neural network (ANN). It was used thermograph data of Sari weather station for modeling. Relative humidity, maximum temperature, minimum temperature, wind velocity and daily evaporation were considered as network input and air-temperature was considered as network output during 2010 to 2020 years. Criteria including correlation coefficient, root mean square error (RMSE), Nash-Sutcliffe (NS) coefficient were used to evaluate and comparison of models efficiency. Results showed that WNN model had better performance rather than ANN for modeling, so that WNN model showed the most coefficient of determination (0.999), RMSE (0.001) and NS (0.998) which was initiated in accuracy stage. In conclusion, results showed higher precision of WNN model in estimation air-temperature.

Keywords

Main Subjects

References

Abhishek, K., Singh, M.P., Ghosh, S., and Anand, A. 2012 Weather Forecasting Model using Artificial Neural Network. Journal of Procedia Technology, 4(4), 311-318.
Alexiadis, M.C., Dokopoulos, P.S., Sahsamanoglou, H.S., and Manousarids, I.M. 1998 Shortterm forecasting of wind speed and related electrical power. Solar Energy. 63(1), 61–68.
Astsatryan, H., Grigoryan, H., Poghosyn, A., Abrahamyan, R., and Asmaryan, S. 2021. Air temperature forecasting using artificial neural network for Ararat valley. Earth Science Informatics.14, 711–722
Bellagarda, A., Cesari, S., and Aliberti, A. 2022. Effectiveness of neural networks and transfer learning for indoor air-temperature forecasting. Automation in Construction.140, 114-122
Deo,  R.C., and Şahin,  M. 2015. Application of the Artificial Neural Network model for prediction of monthly Standardized Precipitation and Evapotranspiration Index using hydrometeorological parameters and climate indices in eastern Australia. Atmospheric Research. 161-162(4), 65-81.
Ding, W., Zhang, J., and Leung, Y. 2016 Prediction of air pollutant concentration based on sparse response back-propagation training feedforward neural networks. Environmental Science and Pollutant Research. 23, 19481-19494.
Dutta, M. K., and Basu,  J. 2013. Application of artificial neural network for prediction of Pb(II) adsorption characteristics. Environmental Science and Pollution Research. 20(5), 3322–3330
Ghorbani, M.A., Khatibi, R., Fazelidard, M.H., and Makarynsky, O. 2015. Short-term wind speed predictions with machine learning techniques. Meteorology and Atmospheric Physics. 128(1), 57–72.
Hornik, K. 1998. Multilayer feed-forward networks are universal approximators. Neural Networks. 2(5), 359-366.
Hou, G., Wang, Y., Zhou, J., and Tian, Q. 2022. Prediction of hourly air temperature based on CNN–LSTM. Geomatics, Natural Hazards and Risk. 13(1), 14-22.
Imran, T., Shafiqur, R., and Bubshait, K.H. 2002. Application of neural networks for the prediction of hourly mean surface temperatures in Saudi Arabia. Renewable Energy. 25(2), 545-554.
Karthika, B.S., and Deka, P.C. 2016. Modeling of Air Temperature using ANFIS by Wavelet Refined Parameters. International Journal of Intelligent Systems and Applications. 8(1), 25-34.
Kémajou, A., Léopold, M., and Pierre, M .2012. Application of Artificial Neural Network for Predicting the Indoor Air Temperature in Modern Building in Humid Region. British Journal of Applied Science and Technology. 2(1), 23-34
Khatibi, R., Naghipour, L., Ghorbani, M.A., and Alami, M.T .2012. Predictability of Relative Humidity by Two Artificial Intelligence Techniques Using Noisy Data from Two Californian Gauging Stations. Neural Computing and Applications. 23(7), 2241–2252
Khatibi, R., Naghipour, L., Ghorbani, M.A., Smith, M.S., Karimi, V., Farhoudi, R., Delafrouz, H., and Arvanaghi, H. 2012. Developing a Predictive Tropospheric Ozone Model for Tabriz. Atmospheric Environment. 68(2), 286–294
Kisi, O., Genc, O., Dinc, S., and Zounemat-Kermani, M. 2016. Daily pan evaporation modeling using chi-squared automatic interaction detector, neural networks, classification and regression tree. Computers and Electronics in Agriculture. 122(c),112-117
Kisi, O., Karahan, M., and Sen, Z .2006. River suspended sediment modeling using fuzzy logic approach. Hydrological Process. 20(2), 4351-4362.
Nagy, H., Watanabe, K., and Hirano, M. 2002. Prediction of sediment load concentration in rivers using artificial neural network model. Journal of Hydraulic Engineering. 128(4), 558-559.
Nourani, V., Alami, M.T., and Aminfar, M.H. 2009. A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation. Engineering Applications of Artificial Intelligence. 22(2), 466-472.
Nourani, V., Kisi, Ö., and Komasi, M .2011. Two hybrid artificial intelligence approaches for modeling rainfall–runoff process. Journal of Hydrology. 402(2), 41-59.
Okkan, U. 2012. Wavelet neural network model for reservoir inflow prediction. Journal of Scientia Iranica. 4(2),1445-1455.
Pammar, L., and Deka, P. 2015. Forecasting daily pan evaporation using hybrid model of wavelet transform and support vector machines.International Journal of Hydrology Science and Technology. 5(3), 274-294.
Pires, J.C.M., Gonçalves, B., Azevedo, F.G., Carneiro, A.P., Rego, N., Assembleia, A.J.B, Lima, J.F.B., Silva, P.A., Alves, C., and Martins, F.G. 2012. Optimization of artificial neural network models through genetic algorithms for surface ozone concentration forecasting. Environmental Science and Pollution Research. 19, 3228-3234.
Safavi, A.A., and Romagnoli, J,A. 1997. Application of wavelet-based neural networks to modelling and optimisation of an experimental distillation column, (IFAC Journal of) Eng Appl Artificial Intelligence. 10(3),301-313.
Saha Roy, D. 2020. Forecasting the Air temperature at a weather station using deep neural networks. Procedia computer science. 178, 38-46
Sharifi ,S.S., Rezaverdinejad, V., and  Nourani, A. 2016. Estimation of Daily Global Solar Radiation using Wavelet Regression, ANN, GEP and Empirical Models: A Comparative Study of Selected Temperature-Based Approaches. Journal of Atmospheric and Solar-Terrestrial Physics.149(3),131-145.
Singh, V.P., and Xu, C.Y. 1997. Evaluation and Generalization of 13 Mass-Transfer Equations for Determining Free Water Evaporation. Hydrology Process. 11(3), 311–323.
Soleimani-Moheseni, M., Thomas, B., and Fahien, P. 2006.  Estimation of operative temperature in buildings using artificial neural networks. Energy and Buildings. 38(3), 635-640.
Tokar, A.S., and Johnson, P.A. 1999. Rainfall- Runoff modeling using artificial neural networks. Journal of Hydrological Engineerinh. 3(2):232-239.
Venkata Ramana, R., Krishna, B., Kumar, S.R., and Pandey, N.G. 2013. Monthly Rainfall Prediction Using Wavelet Neural Network Analysis. Water Resources Management. 27(10), 3697-3711.
Wang, D.,  Safavi, A.A., and Romagnoli, J.A. 2000. Wavelet-based adaptive robust M-estimator for non-linear system identification. AIChE Journal.46(8),1607-1615.
Wang, L., Lu, Y., and Yao, Y .2019. Comparison of three algorithms for the retrieval of land surface temperature from landsat 8 images. Sensors. 19(22), 5049
Wang, W., and Ding, J .2003. Wavelet Network Model and Its Application to the Prediction of Hydrology. Nature and Science. 1(1), 67-71.
Yakut, E., and Suzulmus, S. 2020. Modelling monthly mean air temperature using artificial neural network, adaptive neuro-fuzzy inference system and support vector regression methods: A case of study for Turkey. Network: Computation in Neural Systems.31,1-4
Yang, Q., Lee C.Y., and Tippett, M.K. 2020. A long short-term memory model for global rapid intensification prediction. Weather and Forecasting. 35, 1203-1220
Zhu, Y.M., Lu, X.X., and Zhou, Y. 2007. Suspended sediment flux modeling with artificial neural network: An example of the longchuanjiang river in the upper yangtze catchment. Geomorphology. 84(4), 111-125.
Environmental Resources Research
Volume 10, Issue 2
July 2022
Pages 291-300

Files

Share

How to cite

Statistics