Knowledge Resource Center for Ecological Environment in Arid Area
| DOI | 10.1007/s12665-020-09337-0 |
| Modeling the monthly pan evaporation rates using artificial intelligence methods: a case study in Iraq | |
| Al-Mukhtar, Mustafa | |
| 通讯作者 | Al-Mukhtar, M (corresponding author), Univ Technol Baghdad, Dept Civil Engn, Baghdad, Iraq. |
| 来源期刊 | ENVIRONMENTAL EARTH SCIENCES
 |
| ISSN | 1866-6280 |
| EISSN | 1866-6299 |
| 出版年 | 2021 |
| 卷号 | 80期号:1 |
| 英文摘要 | In arid areas, the estimation of evaporation rates plays a considerable role on both water resources management and agricultural activities. Hence, it is of utmost importance to determine the best model to predict these rates. This study investigates the applicability of using quantile regression forest in predicting the pan evaporation. The model was configured using data from three different meteorological stations located in arid to semi-arid climates in Iraq. These stations were in the cities of Baghdad, Basrah, and Mosul, which are located in the middle, south, and north of the country, respectively. The performance of quantile regression forests was compared with three kinds of artificial intelligence methods i.e. random forests, support vector machine, and artificial neural network in addition to the conventional multiple linear regression models. The maximum temperature (degrees C), minimum temperature (degrees C), relative humidity (%), and wind speed (m/sec) were used as input parameters to the predictive models. The collected data (from 1990 to 2013) was randomly partitioned into two periods; 75% for calibration and 25% for validation. The fivefold cross validation was used during the calibration stage for better model predictability. The results were evaluated using three performance criteria: determination coefficient (R-2), root mean square error (RMSE), and Nash and Sutcliff coefficient efficiency (NSE). Results showed that the quantile regression forests model attained the optimum performance among the evaluated methods. The value of R-2, RMSE, and NSE during validation was 0.99, 17.96 mm, and 0.99 at Baghdad; 0.98, 23.36 mm, and 0.98 at Basrah; and 0.99, 14.44 mm, and 0.99 at Mosul, respectively. Therefore, this method is the most appropriate one to use for predicting evaporation rates in arid to semi-arid climates. |
| 英文关键词 | Quantile regression forests Support vector machine Neural network Modeling Evaporation |
| 类型 | Article |
| 语种 | 英语 |
| 收录类别 | SCI-E |
| WOS记录号 | WOS:000610422200008 |
| WOS类目 | Environmental Sciences ; Geosciences, Multidisciplinary ; Water Resources |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Water Resources |
| 资源类型 | 期刊论文 |
| 条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/347850 |
| 作者单位 | [Al-Mukhtar, Mustafa] Univ Technol Baghdad, Dept Civil Engn, Baghdad, Iraq |
| 推荐引用方式 GB/T 7714 | Al-Mukhtar, Mustafa. Modeling the monthly pan evaporation rates using artificial intelligence methods: a case study in Iraq[J],2021,80(1). |
| APA | Al-Mukhtar, Mustafa.(2021).Modeling the monthly pan evaporation rates using artificial intelligence methods: a case study in Iraq.ENVIRONMENTAL EARTH SCIENCES,80(1). |
| MLA | Al-Mukhtar, Mustafa."Modeling the monthly pan evaporation rates using artificial intelligence methods: a case study in Iraq".ENVIRONMENTAL EARTH SCIENCES 80.1(2021). |
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