Optimizing transient monitoring of river streamflow by a highly predictive model utilizing Ensemble learning models and Multi algorithms

IF 5.9 1区 地球科学 Q1 ENGINEERING, CIVIL
Mojtaba Poursaeid
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Abstract

Global warming and population growth have significantly intensified the challenges in securing drinking water supplies. This study investigates transient instabilities of streamflow using ensemble machine learning (EML) and machine learning (ML) methodologies on the South Platte river in the United States. The United States Geological Survey’s online database was utilized to obtain the primary dataset. Several technical approaches were employed for preprocessing the initial dataset: cleaning outlier data, clean missing data, and 10 fold cross-validation. Nonlinear programming, genetic algorithm, least square, linear programming, gradient descent, particle swarm optimization, Nelder Mead, and simulated annealing were employed algorithms to develop eight-weighted EML models. The results showed that the ensemble learning approach and the aggregation of weak learners by mentioned algorithms have been significantly successful. Particularly, the nonlinear programming-EML (NLP-EML) outperformed others, achieving the highest prediction accuracy with an R2 coefficient equal to 0.97. The probability density function showed that NLP-EML was the most reliable model. Overall, the findings highlight the superior performance and reliability of EML approaches in hydrological modeling, offering practical guidance to experts on the creation of robust ensemble models for improved prediction accuracy.
利用集合学习模型和多重算法的高度预测模型优化河流流量的瞬态监测
全球变暖和人口增长大大加剧了保障饮用水供应的挑战。本研究采用集合机器学习(EML)和机器学习(ML)方法,对美国南普拉特河的瞬态不稳定性进行了研究。研究利用美国地质调查局的在线数据库获取主要数据集。预处理初始数据集时采用了几种技术方法:清除离群数据、清除缺失数据和 10 倍交叉验证。非线性编程、遗传算法、最小平方、线性规划、梯度下降、粒子群优化、Nelder Mead 和模拟退火等算法被用于开发八权重 EML 模型。结果表明,上述算法中的集合学习法和弱学习者聚合法取得了显著的成功。其中,非线性编程-EML(NLP-EML)的效果优于其他算法,其预测准确率最高,R2系数为0.97。概率密度函数显示,NLP-EML 是最可靠的模型。总之,研究结果凸显了 EML 方法在水文建模中的卓越性能和可靠性,为专家创建稳健的集合模型以提高预测精度提供了实用指导。
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来源期刊
Journal of Hydrology
Journal of Hydrology 地学-地球科学综合
CiteScore
11.00
自引率
12.50%
发文量
1309
审稿时长
7.5 months
期刊介绍: The 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 and hydrogeology. Relevant topics incorporating the insights and methodologies of disciplines such as climatology, water resource systems, hydraulics, agrohydrology, geomorphology, soil science, instrumentation and remote sensing, civil and environmental engineering are included. Social science perspectives on hydrological problems such as resource and ecological economics, environmental 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.
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