Knowledge Resource Center for Ecological Environment in Arid Area
| DOI | 10.1007/s12517-021-09290-7 |
| Prediction of missing temperature data using different machine learning methods | |
| Katipoglu, Okan Mert | |
| 通讯作者 | Katipoglu, OM (corresponding author), Erzincan Binali Yildirim Univ, Fac Engn & Architecture, Dept Civil Engn, Erzincan, Turkey. |
| 来源期刊 | ARABIAN JOURNAL OF GEOSCIENCES
 |
| ISSN | 1866-7511 |
| EISSN | 1866-7538 |
| 出版年 | 2022 |
| 卷号 | 15期号:1 |
| 英文摘要 | Temperature data is one of the basic inputs of meteorological, hydrological and climatic studies. The completeness of this data is of great importance for reliability in research. This study aimed to compare the performances of various machine learning methods such as support vector machines (SVM), adaptive neuro-fuzzy inference system (ANFIS) and decision tree (DT) to infill missing air temperature data. Monthly average temperature data from 1968 to 2017 (50 years) was used to develop the models. In the established model, the data is divided as 80/20% (1968-2007 training/2008-2017 testing). Neighbouring stations, like Sarikamis, Tortum and Agri, which have a high correlation with Horasan, were used as inputs to estimate the temperature data of the Horasan station. The most suitable machine learning method was chosen according to the mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE) and determination coefficients (R-2) of the training and test results. The ANFIS model with four sub-sets, triangular membership function, hybrid learning algorithm and 300 iterations was selected as the most suitable model. It was recommended using ANFIS to estimate monthly air temperatures in the northeastern part of Turkey and perhaps in other semi-arid climatic regions around the world. |
| 英文关键词 | Temperature Missing data Support vector machines (SVM) Decision tree (DT) Adaptive neuro-fuzzy inference system (ANFIS) |
| 类型 | Article |
| 语种 | 英语 |
| 收录类别 | SCI-E |
| WOS记录号 | WOS:000733929200012 |
| WOS关键词 | ARTIFICIAL NEURAL-NETWORK ; AIR-TEMPERATURE ; SOLAR-RADIATION ; ANFIS ; REGRESSION ; MODELS ; SVM ; ANN |
| WOS类目 | Geosciences, Multidisciplinary |
| WOS研究方向 | Geology |
| 资源类型 | 期刊论文 |
| 条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/374375 |
| 作者单位 | [Katipoglu, Okan Mert] Erzincan Binali Yildirim Univ, Fac Engn & Architecture, Dept Civil Engn, Erzincan, Turkey |
| 推荐引用方式 GB/T 7714 | Katipoglu, Okan Mert. Prediction of missing temperature data using different machine learning methods[J],2022,15(1). |
| APA | Katipoglu, Okan Mert.(2022).Prediction of missing temperature data using different machine learning methods.ARABIAN JOURNAL OF GEOSCIENCES,15(1). |
| MLA | Katipoglu, Okan Mert."Prediction of missing temperature data using different machine learning methods".ARABIAN JOURNAL OF GEOSCIENCES 15.1(2022). |
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