A multi-perspective input selection strategy for daily net ecosystem exchange predictions based on machine learning methods

Ömer Ekmekcioğlu*, Eyyup Ensar Başakın, Nilcan Altınbaş, Mehmet Özger, Serhan Yeşilköy, Levent Şaylan

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Özet

This research presents a prediction framework to simulate the net ecosystem exchange (NEE) based on a multi-perspective input selection strategy, which is an initial attempt in the pertinent literature. To accomplish the overarching aim of the current study, the data obtained by an eddy-covariance system established for monitoring three growing seasons of winter wheat were utilized. In this sense, four different input selection strategies, i.e., crop variables-based (leaf area index and total dry biomass), meteorological variables-based (solar radiation, average air temperature, soil temperature, and average relative humidity), the combination of crop and meteorological variables-based, and sensitivity analysis-based, were considered. In the last scenario, a total of 18 environmental determinants were taken into account and the most significant variables were determined based on a statistical manner via step-wise regression (SWR). The multivariate adaptive regression splines (MARS) algorithm was employed to perform the predictions, and the proposed framework was benchmarked with the artificial neural networks (ANN) as it is one of the widely used machine learning algorithms. The results revealed that the MARS model outperformed the ANN model in all scenarios but the meteorological-based. The best model performance was attained through the SWR-based MARS estimations (R2 = 0.90 and NSE = 0.90), followed by the combination scenario (R2 = 0.83 and NSE = 0.82), meteorological (R2ANN = 0.74 and NSEANN = 0.73), and crop variable-based (R2 = 0.72 and NSE = 0.71) scenario. Thus, this study revealed that using limited variables instead of those obtained through exhaustive measurements could yield promising performance in modelling the ecosystem fluxes, that of which can be a rewarding alternative for data-scarce regions.

Orijinal dilİngilizce
Sayfa (başlangıç-bitiş)81-98
Sayfa sayısı18
DergiTheoretical and Applied Climatology
Hacim151
Basın numarası1-2
DOI'lar
Yayın durumuYayınlandı - Oca 2023

Bibliyografik not

Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.

Finansman

We would like to thank the Istanbul Technical University and General Directorate of Agricultural Research and Policies, Atatürk Soil Water and Agricultural Meteorology Research Institute (ASWAM) for their valuable supports. Special thanks to Assoc. Prof. Barış Çaldağ, Dr. Fatih Bakanoğulları, Elif Semizoğlu, Yunus Özkoca, and Osman Çaylak for their contributions. Additionally, thanks to the technicians who helped us during the experimental studies at ASWAM for their efforts. The authors received financial support from the Scientific and Technological Research Council of Turkey (TUBITAK) (108O567 and 109R006).

FinansörlerFinansör numarası
Atatürk Soil Water and Agricultural Meteorology Research Institute
General Directorate of Agricultural Research and Policies
Türkiye Bilimsel ve Teknolojik Araştırma Kurumu108O567, 109R006
Istanbul Teknik Üniversitesi

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