For effective planning and management of water resources and implementation of the related strategies, it is important to ensure proper estimation of evaporation losses, especially in regions that are prone to drought. Changes in climatic factors, such as changes in temperature, wind speed, sunshine hours, humidity, and solar radiation can have a significant impact on the evaporation process. As such, evaporation is a highly non-linear, non-stationary process, and can be difficult to be modeled based on climatic factors, especially in different agro-climatic conditions. The aim of this study, therefore, is to investigate the feasibility of several machines learning (ML) models (conditional random forest regression, Multivariate Adaptive Regression Splines, Bagged Multivariate Adaptive Regression Splines, Model Tree M5, K- nearest neighbor, and the weighted K- nearest neighbor) for modeling the monthly pan evaporation estimation. This study proposes the development of newly explored ML models for modeling evaporation losses in three different locations over the Iraq region based on the available climatic data in such areas. The evaluation of the performance of the proposed model based on various evaluation criteria showed the capability of the proposed weighted K- nearest neighbor model in modeling the monthly evaporation losses in the studies areas with better accuracy when compared with the other existing models used as a benchmark in this study.
翻译:对水资源的有效规划和管理以及相关战略的执行而言,必须确保适当估计蒸发损失,特别是在易发生干旱的地区。气候因素的变化,如温度、风速、阳光时间、湿度和太阳辐射的变化,可对蒸发过程产生重大影响。因此,蒸发是一个高度非线性、非静止的过程,难以根据气候因素进行模型化,特别是在不同的农业气候条件下。因此,这项研究的目的是调查若干机器学习模型(有条件随机森林回归、多变适应性回退流流流流线、堵塞多变回流流流流、模型M5、K-最近的邻居和加权K-最近的邻居)的可行性,以模拟月度蒸发估计。这项研究的目的是根据这些地区现有的气候数据,调查若干机械学习模型(ML)的可行性(有条件随机森林回归、多变适应性回流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流变变变变变变变变变变变和回流流流流流流、多变变流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流流回回流流流流流流流流流流流流流流流流流流流流流流流流流流流流、潮流流流流流