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An Intelligent Multi-output Regression Model for Soil Moisture Prediction

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Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation (INFUS 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 308))

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Abstract

Soil moisture prediction plays a vital role in developing plants, soil properties, and sustenance of agricultural systems. Considering this motivation, in this study, an intelligent Multi-output regression method was implemented on daily values of meteorological and soil data obtained from Kemalpaşa-Örnekköy station in Izmir, Turkey, at three soil depths (15, 30, and 45 cm) between the years 2017 and 2019. In this study, nine different machine learning algorithms (Linear Regression (LR), Ridge Regression (RR), Least Absolute Shrinkage and Selection Operator (Lasso), Random Forest (RF), Extra Tree Regression (ETR), Adaptive Boosting (AdaBoost), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), and Histogram-Based Gradient Boosting (HGB)) were compared each other in terms of MAE, RMSE, and R2 metrics. The experiments indicate that the implemented Multi-output regression models show good soil moisture prediction performance. Also, the ETR algorithm provided the best prediction performance with an 0.81 R2 value among the other models.

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Acknowledgements

The authors are deeply grateful to Selim Alpaslan in the Izmir Metropolitan Municipality for providing the experimental dataset used in the study.

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Authors and Affiliations

  1. Graduate School of Natural and Applied Sciences, Dokuz Eylul University, 35390, Izmir, Buca, Turkey

    Cansel Kucuk

  2. Department of Computer Engineering, Dokuz Eylul University, 35390, Izmir, Buca, Turkey

    Derya Birant

  3. Department of Computer Engineering, Izmir Bakırçay University, 35665, Izmir, Menemen, Turkey

    Pelin Yildirim Taser

Authors
  1. Cansel Kucuk
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  2. Derya Birant
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  3. Pelin Yildirim Taser
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Corresponding authors

Correspondence to Cansel Kucuk , Derya Birant or Pelin Yildirim Taser .

Editor information

Editors and Affiliations

  1. Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey

    Cengiz Kahraman

  2. Industrial Engineering Department, Yildiz Technical University, Istanbul, Turkey

    Selcuk Cebi

  3. Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey

    Sezi Cevik Onar

  4. Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey

    Basar Oztaysi

  5. Industrial Engineering Department, Galatasaray University, Istanbul, Turkey

    A. Cagri Tolga

  6. Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey

    Irem Ucal Sari

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Kucuk, C., Birant, D., Yildirim Taser, P. (2022). An Intelligent Multi-output Regression Model for Soil Moisture Prediction. In: Kahraman, C., Cebi, S., Cevik Onar, S., Oztaysi, B., Tolga, A.C., Sari, I.U. (eds) Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation. INFUS 2021. Lecture Notes in Networks and Systems, vol 308. Springer, Cham. https://doi.org/10.1007/978-3-030-85577-2_56

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