Yazar "Sattari, Mohammad Taghi" seçeneğine göre listele

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  • Küçük Resim
    Öğe
    Estimation of monthly precipitation based on machine learning methods by using meteorological variables
    (Hatay Mustafa Kemal Üniversitesi, 2019) Sureh, Fatemeh Shaker; Sattari, Mohammad Taghi; İrvem, Ahmet
    Aims: The aim of this study is to estimate monthly precipitation by support vector regression and the nearest neighbourhood methods using meteorological variables data of Chabahar station. Methods and Results: Monthly precipitation was modelled by using two support vector regression and the nearest neighbourhood methods based on the two proposed input combinations. Conclusions: The results showed that the support vector regression method using normalized polynomial kernel function has higher accuracy and it has lower estimation error than the nearest neighbour method. Significance and Impact of the Study: Precipitation is one of the most important parts of the water cycle and plays an important role in assessing the climatic characteristics of each region. Modelling of monthly precipitation values for a variety of purposes, such as flood and sediment control, runoff, sediment, irrigation planning, and river basin management, is very important. The modelling of precipitation in each region requires the existence of accurately measured historical data such as humidity, temperature, wind speed, etc. Limitations such as insufficient knowledge of precipitation on spatial and temporal scales as well as the complexity of the relationship between precipitation-related climatic parameters make it impossible to estimate precipitation using conventional inaccurate and unreliable methods.
  • Küçük Resim
    Öğe
    Potential of kernel and tree-based machine-learning models for estimating missing data of rainfall
    (Hong Kong Polytechnic Univ, Dept Civil & Structural Eng, 2020) Sattari, Mohammad Taghi; Falsafian, Kambiz; Irvem, Ahmet; Shahab, S.; Qasem, Sultan Noman
    In this study, two kernel-based models were used which include Support Vector Regression (SVR) and Gaussian Process Regression (GPR) and were compared with two tree-based models that are M5 and Random Forest (RF) for estimating missing monthly precipitation data in Antakya, Dortyol, Iskenderun and Samandag stations, which are the important precipitation stations in the Eastern Mediterranean region, Turkey. For this purpose, firstly 10% random precipitation data were assumed as missing data for the period 1980-2019. Secondly, the missing data in each station was estimated with the data of other stations within the framework of four data combinations scenarios. In Kernel-based SVR and GPR methods, the RBF kernel gave suitable results for the selected study area. While SVR and RF methods gave very close estimation results, the SVR method gave relatively better results than the other methods especially in error minimizing aspects. Gaussian function based GPR model generally tries to estimate missing data closer to means. This is the main disadvantage of the GPR model and therefore it is unsuccessful in the estimation process. Finally, the results showed that the algorithms based on machine learning are successful in estimating the missing precipitation data.