پیش‌بینی دمای نقطه شبنم با استفاده از مدل‌های مبتنی بر درخت و هسته

Alizamir M, Kim S, Zounemat-Kermani M, Heddam S, Kim NW and Singh VP, 2020. Kernel extreme learning machine: An efficient model for estimating daily dew point temperature using weather data. Water 12(9): 2600.

Mojahedi MA, Mohammadi K, Shamshirband S, Seyed Danesh A, Mostafaeipour A and Kamsin A, 2016. A hybrid computational intelligence method for predicting dew point temperature. Environmental Earth Sciences 75: 415.

Brieman L, 2001. Random Forests. Machine Learning, 45: 5-23.

Deka P, Patil AP, Yeswanth Kumar P and Naganna SR, 2017. Estimation of dew point temperature using SVM and ELM for humid and semi-arid regions of India. ISH Journal of Hydraulic Engineering 24(2): 190-197.

Dong J, Wu L, Liu X, Li Z, Gao Y, Zhang Y and Yang Q, 2020. Estimation of daily dew point temperature by using bat algorithm optimization based extreme learning machine. Applied Thermal Engineering 165: 114569.

Dong J, Zeng W, Lei G, Wu Lifeng, Chen H, Wu J, Huang J, Gaiser T, and Srivastava AK, 2022. Simulation of dew point temperature in different time scales based on grasshopper algorithm optimized extreme gradient boosting. Journal of Hydrology 606: 127452.

Fathollahzadeh N, Khalili K, Behmanesh J and Khanmohammadi N, 2018. On the reliability of soft computing methods in the estimation of dew point temperature: The case of arid regions of Iran. Computers and Electronics in Agriculture 153: 334-346.

Fischer MM, 2015. Spatial Analysis in Geography. International Encyclopedia of the Social & Behavioral Sciences. Elsevier 12: 14752-14758.

Holmes G, Hall M and Prank B, 2007. Generating rule sets from model trees. Advanced topics in artificial intelligence. Lecture Notes in Computer Science: 1774.

Kalmeg S, 2015. Analysis of weka data mining algorithm reptree, simple cart and random tree for classification of indian news. Engineering Technician 2(2): 444602.

Kermani MZ, 2011. Hourly predictive Levenberg–Marquardt ANN and multi linear regression models for predicting of dew point temperature. Meteorology and Atmospheric Physics 117: 181-192.

Kisi O, Kim S and Shiri J, 2013. Estimation of dew point temperature using neuro-fuzzy and neural network techniques. Theoretical and Applied Climatology 114: 365-373.

Lawrence MG, 2005. The relationship between relative humidity and the dewpoint temperature in moist air: a simple conversion and applications. Bulletin of the American Meteorological Society 86: 225-234.

Mahdizadeh S, Behmanesh J and Khalili K, 2017. Using MARS, SVM, GEP and empirical equations for estimation of monthly mean reference evapotranspiration. Computers and Electronics in Agriculture 139: 103-114.

Mohammadi K, Shamshirband S, Motamedi S, Petković D, Hashim R and Gocic M, 2015. Extreme learning machine based prediction of daily dew point temperature. Computers and Electronics in Agriculture 117: 214-225.

Mohammadi K, Shamshirband S, Petković D, Yee PL and Mansor Z, 2016. Using ANFIS for selection of more relevant parameters to predict dew point temperature. Applied Thermal Engineering 96: 311–319.

Naganna S, Deka P, Ghorbani M, Biazar S, Al-Ansari N and Yaseen Z, 2019. Dew point temperature estimation: application of artificial intelligence model integrated with natureinspired optimization algorithms. Water 11(4): 742.

Osouli M, Mikaeili F and Samadianfard S, 2022. Evaluating capabilities of gradient boosted tree and optimized random forest models in estimating daily dew point temperature. Environment and Water Engineering 8(3): 654–668 (in Persian with English abstract)

Qasem SN, Samadianfard S, Sadri Nahand H, Mosavi A, Shamshirband S and Chau KW, 2019. Estimating daily dew point temperature using machine learning algorithms. Water 11(3): 582.

Shank DB, McClendon RW, Paz J and Hoogenboom G, 2008. Ensemble artificial neural networks for prediction of dew point temperature. Applied Artificial Intelligence 22(6): 523-542.

Shiri J, 2019. Prediction vs. estimation of dew point temperature: assessing GEP, MARS and RF models. Hydrology: Current Research 50(2): 633–643. 

Shiri J, Kim S and Kisi O, 2014. Estimation of daily dew point temperature using genetic programming and neural networks approaches. Hydrology: Current Research 45(2): 165- 181.

Zhan C, Gan A and Hadi M, 2011. Prediction of lane clearance time of freeway incidents using the M5P tree algorithm. IEEE Transactions on Intelligent Transportation Systems 12(4): 1549–1557.