Modelling monthly pan evaporation utilising Random Forest and deep learning algorithms.
Modelling monthly pan evaporation utilising Random Forest and deep learning algorithms.
复制标题
DOI：
10.1038/s41598-022-17263-3
复制
发表时间：
2022-07-30
影响因子：
4.6
通讯作者：
Huang, Yuk Feng
中科院分区：
文献类型：
--
作者：
Abed, Mustafa;Imteaz, Monzur Alam;Ahmed, Ali Najah;Huang, Yuk Feng
在水循环中，蒸发是造成水损失的主要方面；因此，必须精确测量水损失。蒸发是一个复杂的非线性过程，它是由几个气候因素引起的。本研究的目的是评估使用随机森林(RF)和两种深度学习技术，即卷积神经网络(CNN)和深度神经网络(DNN)来精确估计月蒸发量的可行性。2000年至2019年期间从马来西亚四个气象站收集的月度天气数据被用于训练和评估模型。研究了几个输入属性(预测变量)，以选择最适合机器学习模型的变量。每种方法都用几个模型进行了测试，每个模型都有不同的模型方面和输入参数组合。制定的ML方法以两种常用的经验方法为基准：Stephens&Stewart和Thornthwaite。使用标准统计方法对模型结果进行评估，以确定它们在预测蒸发方面的有效性。结果表明，本研究建立的三个最大似然模型均优于经验模型，即使在相同的输入集合下，也能显着提高月EP估计的精度。性能评估指标还表明，与本研究中探索的其他ML框架相比，所制定的CNN方法对于模拟由于蒸发而造成的月水分损失具有更高的准确度。此外，CNN框架的表现优于使用相同数据输入对相同领域进行评估的其他人工智能技术。与各种性能指标相关的研究结果表明，所提出的CNN模型能够捕捉到蒸发的高度非线性，可以被视为预测蒸发的有效工具。
Evaporation is the primary aspect causing water loss in the hydrological cycle; therefore, water loss must be precisely measured. Evaporation is an intricate nonlinear process occurring as a result of several climatic aspects. The purpose of this research is to assess the feasibility of using Random Forest (RF) and two deep learning techniques, namely convolutional neural network (CNN), and deep neural network (DNN) to accurately estimate monthly pan evaporation rates. Month-based weather data gathered from four Malaysian weather stations during the 2000–2019 timeframe was used to train and evaluate the models. Several input attributes (predictor variables) were investigated to select the most suitable variables for machine learning models. Every approach was tested with several models, each with a different set of model aspects and input parameter combinations. The formulated ML approaches were benchmarked against two commonly used empirical methods: Stephens & Stewart and Thornthwaite. Model outcomes were assessed using standard statistical measures to determine their effectiveness in predicting evaporation. The results indicated that the three ML models developed in the study performed better than empirical models and could significantly improve the precision of monthly Ep estimates even with the identical input sets. The performance assessment metrics also show that the formulated CNN approach was acceptable for modelling monthly water loss due to evaporation with a higher degree of accuracy than other ML frameworks explored in this study. In addition, the CNN framework outperformed other AI techniques evaluated for the same areas using identical data inputs. The investigation’s findings in relation to the various performance criteria show that the proposed CNN model is capable of capturing the highly non-linearity of evaporation and could be regarded as an effective tool to predict evaporation.
登录
查看更多内容
影响因子：
6.7
作者：
Falamarzi, Yashar;Palizdan, Narges;Lee, Teang Shui
通讯作者：
Lee, Teang Shui
1956-2015年气候变化对中国不同气候区参考作物蒸散量的影响
影响因子：
6.4
作者：
Fan, Junliang;Wu, Lifeng;Zheng, Jing
通讯作者：
Zheng, Jing
影响因子：
4.6
作者：
Abed M;Imteaz MA;Ahmed AN;Huang YF
通讯作者：
Huang YF
DOI：
10.1007/s00477-015-1153-y
发表时间：
2016-08-01
影响因子：
4.2
作者：
Deo, Ravinesh C.;Samui, Pijush;Kim, Dookie
通讯作者：
Kim, Dookie
基于有限气象数据的中国不同气候下的SVM、ELM和四种基于树的集合模型预测日参考蒸散量的评估
影响因子：
6.2
作者：
Fan, Junliang;Yue, Wenjun;Xiang, Youzhen
通讯作者：
Xiang, Youzhen