基于遥感数据和蜜獾优化模型的乌尔米亚湖水位日预测

IF 2.3 4区 地球科学
Mohsen Saroughi, Okan Mert Katipoğlu, Gaye Aktürk, Enes Gul, Oguz Simsek, Hatice Citakoglu
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引用次数: 0

摘要

将人工神经网络(ann)、支持向量回归(SVR)和CatBoost回归(CBR)机器学习方法与蜜獾优化算法(HBA)和元启发式优化算法相结合,准确、可靠地预测湖泊水位,对水资源的管理和规划具有重要意义。本研究采用温度(T)、降水(P)、日期(D)、表层土壤湿度(SSW)、根区湿度(RZW)和水位(WL)等气象水文参数作为乌尔米亚湖LWL的输入数据。输入的数据被用来开发六种不同的预测情景。本研究不仅考察了气象和水文参数对LWL预测的影响,还比较了单个模型和混合模型的性能。采用赤池信息准则(Akaike information criterion, AIC)指数确定最优机器学习模型,并对6种预测情景进行评价。研究结果表明,根据AIC指数,水位(WL)数据在预测模型中具有显著性。然而,需要注意的是,在某些情况下,不使用WL数据也可以获得令人满意的结果。在场景4(输入数据:D, T, P, SSW, RZW)中,不包含WL变量,HBA-CBR混合模型是AIC值最低的最佳模型(Train: -63,735, Test:-4693)。在包含WL数据的预测场景6(输入数据:D, T, P, SSW, RZW, WL)中,HBA-SVR混合模型表现出较好的性能,AIC值最低(Train: -102,358, Test:-27,233)。因此,建议使用滞后的WL值作为WL预测的输入,因为模型的预测精度明显提高。此外,由于混合模型的结果更加一致,因此发现混合模型的性能优于单个模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Daily prediction of Urmia Lake water level using remote sensing data and honey badger optimization-based data-driven models

Artificial neural networks (ANNs), support vector regression (SVR) and CatBoost regression (CBR) machine learning methods have been combined with the honey badger optimization algorithm (HBA) and metaheuristic optimization algorithm to accurately and reliably predict lake water level (LWL), which is of great importance for the management and planning of water resources. In this study, meteorological and hydrological parameters, including temperature (T), precipitation (P), date (D), surface soil moisture (SSW), root zone moisture (RZW) and water level (WL), were employed as input data for predicting the LWL of Urmia Lake. The input data were employed to develop six different prediction scenarios. This study not only examined the impact of meteorological and hydrological parameters on LWL prediction but also compared the performance of individual models and hybrid models. The Akaike information criterion (AIC) index was used to ascertain the optimal machine learning model and to evaluate the six prediction scenarios. The results of the study indicate that, according to the AIC index, the data regarding the water level (WL) were significant in the prediction models. However, it should be noted that satisfactory results could also be obtained without using the WL data in certain scenarios. In scenario 4 (input data: D, T, P, SSW, RZW), where the WL variable was not included, the HBA-CBR hybrid model was the best model with the lowest AIC value (Train: -63,735, Test:-4693). In prediction scenario 6 (input data: D, T, P, SSW, RZW, WL), which included the WL data, the HBA-SVR hybrid model demonstrated high performance with the lowest AIC value (Train: -102,358, Test:-27,233). Accordingly, it was recommended to use lagged WL values as input in WL prediction because the prediction accuracy of the models significantly improved. Furthermore, hybrid models were found to perform better than individual models due to their more consistent results.

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来源期刊
Acta Geophysica
Acta Geophysica GEOCHEMISTRY & GEOPHYSICS-
CiteScore
3.80
自引率
13.00%
发文量
251
期刊介绍: Acta Geophysica is open to all kinds of manuscripts including research and review articles, short communications, comments to published papers, letters to the Editor as well as book reviews. Some of the issues are fully devoted to particular topics; we do encourage proposals for such topical issues. We accept submissions from scientists world-wide, offering high scientific and editorial standard and comprehensive treatment of the discussed topics.
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