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Enhancing Subsurface Soil Moisture Forecasting: A Long Short-Term Memory Network Model Using Weather Data

Author

Listed:
  • Md. Samiul Basir

    (Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907, USA)

  • Samuel Noel

    (Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907, USA)

  • Dennis Buckmaster

    (Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907, USA)

  • Muhammad Ashik-E-Rabbani

    (Department of Farm Power and Machinery, Bangladesh Agricultural University, Mymensingh 2202, Bangladesh)

Abstract

Subsurface soil moisture is a primary determinant for root development and nutrient transportation in the soil and affects the tractability of agricultural vehicles. A statistical forecasting model, Vector AutoRegression (VAR), and a Long Short-Term Memory network (LSTM) were developed to forecast the subsurface soil moisture at a 20 cm depth using 9 years of historical weather data and subsurface soil moisture data from Fort Wayne, Indiana, USA. A time series analysis showed that the weather data and soil moisture have a stationary seasonal tendency and demonstrated that soil moisture can be forecasted from weather data. The VAR model estimates volumetric soil moisture of one-day ahead with an R 2 , MAE (m 3 m −3 ), MSE (m 6 m −6 ), and RMSE (m 3 m −3 ) of 0.698, 0.0561, 0.0046, and 0.0382 for 2021 corn cropping season, whereas the LSTM model using inputs of previous seven days yielded R 2 , MAE (m 3 m −3 ), MSE (m 6 m −6 ), and RMSE (m 3 m −3 ) of 0.998, 0.00237, 0.00002, and 0.00382, respectively as tested for cropping season of 2020 and 0.973, 0.00368, 0.00003 and 0.00577 as tested for the cropping season of 2021. The LSTM model presents a viable data-driven alternative to traditional statistical models for forecasting subsurface soil moisture.

Suggested Citation

  • Md. Samiul Basir & Samuel Noel & Dennis Buckmaster & Muhammad Ashik-E-Rabbani, 2024. "Enhancing Subsurface Soil Moisture Forecasting: A Long Short-Term Memory Network Model Using Weather Data," Agriculture, MDPI, vol. 14(3), pages 1-24, February.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:3:p:333-:d:1342371
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    References listed on IDEAS

    as
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