利用机器学习技术的集合利用有限的气象数据预测参考蒸发蒸腾量(ET0)

IF 3.1 Q2 WATER RESOURCES
Hamza Salahudin, M. Shoaib, R. Albano, Muhammad Azhar Inam Baig, Muhammad Hammad, Ali Raza, Alamgir Akhtar, Muhammad Usman Ali
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引用次数: 0

摘要

为了最大限度地提高作物产量,参考蒸散量(ET0)测量对于管理水资源和规划作物用水需求至关重要。FAO-PM56方法因其广泛的理论基础而被全球推荐用于估计ET0和评估替代方法。估算ET0所需的许多气象参数在发展中国家很难获得。因此,使用较少的气候数据来估计ET0的替代方法至关重要。为了使用替代方法估计ET0,研究中使用了1996年至2015年20年期间的温度、相对湿度(最大和最小)、日照时数和风速等不同气候参数。这些数据是由位于巴基斯坦不同气候区的11个气象观测站记录的。使用敏感性分析评估了所使用的气候参数的重要性。采用单决策树(SDT)、树提升(TB)和决策树森林(DTF)的机器学习技术进行敏感性分析。结果表明,与研究中使用的其他ML技术相比,基于DTF的模型以更高的精度和更少的气候变量估计ET0。以模型15为输入的DTF技术在大部分性能指标上优于其他技术(即NSE=0.93、R2=0.96和RMSE=0.48 mm/月)。结果表明,平均相对湿度、风速和最低温度等气候变量较少的DTF可以准确估计ET0,并且优于其他ML技术。此外,ML技术的非线性集成(NLE)被进一步用于使用最佳输入组合(即,模型15)来估计ET0。可以看出,与ML技术的单独应用相比,应用非线性集成(NLE)方法提高了建模精度(R2 Multan=0.97,R2 Skardu=0.99,R2 ISB=0.98,R2 Bahawalpur=0.98等)。研究结果证实了集成模型用于ET0估计,并建议将其应用于世界其他地区以验证模型性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using Ensembles of Machine Learning Techniques to Predict Reference Evapotranspiration (ET0) Using Limited Meteorological Data
To maximize crop production, reference evapotranspiration (ET0) measurement is crucial for managing water resources and planning crop water needs. The FAO-PM56 method is recommended globally for estimating ET0 and evaluating alternative methods due to its extensive theoretical foundation. Numerous meteorological parameters, needed for ET0 estimation, are difficult to obtain in developing countries. Therefore, alternative ways to estimate ET0 using fewer climatic data are of critical importance. To estimate ET0 with alternative methods, difference climatic parameters of temperatures, relative humidity (maximum and minimum), sunshine hours, and wind speed for a period of 20 years from 1996 to 2015 were used in the study. The data were recorded by 11 meteorological observatories situated in various climatic regions of Pakistan. The significance of the climatic parameters used was evaluated using sensitivity analysis. The machine learning techniques of single decision tree (SDT), tree boost (TB) and decision tree forest (DTF) were used to perform sensitivity analysis. The outcomes indicated that DTF-based models estimated ET0 with higher accuracy and fewer climatic variables as compared to other ML techniques used in the study. The DTF technique, with Model 15 as input, outperformed other techniques for the most part of the performance metrics (i.e., NSE = 0.93, R2 = 0.96 and RMSE = 0.48 mm/month). The results indicated that the DTF with fewer climatic variables of mean relative humidity, wind speed and minimum temperature could estimate ET0 accurately and outperformed other ML techniques. Additionally, a non-linear ensemble (NLE) of ML techniques was further used to estimate ET0 using the best input combination (i.e., Model 15). It was seen that the applied non-linear ensemble (NLE) approach enhanced modelling accuracy as compared to a stand-alone application of ML techniques (R2 Multan = 0.97, R2 Skardu = 0.99, R2 ISB = 0.98, R2 Bahawalpur = 0.98 etc.). The study results affirmed the use of an ensemble model for ET0 estimation and suggest applying it in other parts of the world to validate model performance.
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来源期刊
Hydrology
Hydrology Earth and Planetary Sciences-Earth-Surface Processes
CiteScore
4.90
自引率
21.90%
发文量
192
审稿时长
6 weeks
期刊介绍: Journal of Hydrology publishes original research papers and comprehensive reviews in all the subfields of the hydrological sciences, including water based management and policy issues that impact on economics and society. These comprise, but are not limited to the physical, chemical, biogeochemical, stochastic and systems aspects of surface and groundwater hydrology, hydrometeorology, hydrogeology and hydrogeophysics. Relevant topics incorporating the insights and methodologies of disciplines such as climatology, water resource systems, ecohydrology, geomorphology, soil science, instrumentation and remote sensing, data and information sciences, civil and environmental engineering are within scope. Social science perspectives on hydrological problems such as resource and ecological economics, sociology, psychology and behavioural science, management and policy analysis are also invited. Multi-and interdisciplinary analyses of hydrological problems are within scope. The science published in the Journal of Hydrology is relevant to catchment scales rather than exclusively to a local scale or site. Studies focused on urban hydrological issues are included.
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