IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v14y2024i3p333-d1342371.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/14/3/333/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/14/3/333/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Holtz-Eakin, Douglas & Newey, Whitney & Rosen, Harvey S, 1988. "Estimating Vector Autoregressions with Panel Data," Econometrica, Econometric Society, vol. 56(6), pages 1371-1395, November.
    2. Ali, Mukhtar M, 1987. "Durbin-Watson and Generalized Durbin-Watson Tests for Autocorrelations and Randomness," Journal of Business & Economic Statistics, American Statistical Association, vol. 5(2), pages 195-203, April.
    3. Chao, John C. & Phillips, Peter C. B., 1999. "Model selection in partially nonstationary vector autoregressive processes with reduced rank structure," Journal of Econometrics, Elsevier, vol. 91(2), pages 227-271, August.
    4. Inder, Brett, 1986. "An Approximation to the Null Distribution of the Durbin-Watson Statistic in Models Containing Lagged Dependent Variables," Econometric Theory, Cambridge University Press, vol. 2(3), pages 413-428, December.
    5. Johansen, Soren, 1991. "Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models," Econometrica, Econometric Society, vol. 59(6), pages 1551-1580, November.
    6. Hirotugu Akaike, 1969. "Fitting autoregressive models for prediction," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 21(1), pages 243-247, December.
    7. Stiltner, G. J. & Holtan, H. N. & Lopez, N. C. & Henson, W. H., 1975. "USDAHL 74 Revised Model of Watershed Hydrology: A United States Contribution to the International Hydrological Decade," Technical Bulletins 158531, United States Department of Agriculture, Economic Research Service.
    8. Franses, Philip Hans, 1994. "A multivariate approach to modeling univariate seasonal time series," Journal of Econometrics, Elsevier, vol. 63(1), pages 133-151, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Valérie Mignon & Christophe Hurlin, 2007. "Une synthèse des tests de cointégration sur données de panel," Économie et Prévision, Programme National Persée, vol. 180(4), pages 241-265.
    2. Ramona Dumitriu & Razvan Stefanescu, 2015. "The Relationship Between Romanian Exports And Economic Growth After The Adhesion To European Union," Risk in Contemporary Economy, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, pages 17-26.
    3. Dhruv Rawat & Sujay Patni & Ram Mehta, 2021. "Stock prices and Macroeconomic indicators: Investigating a correlation in Indian context," Papers 2112.08071, arXiv.org, revised Feb 2022.
    4. Geweke, J. & Joel Horowitz & Pesaran, M.H., 2006. "Econometrics: A Bird’s Eye View," Cambridge Working Papers in Economics 0655, Faculty of Economics, University of Cambridge.
    5. MAÏ ASSAN CHEDI, Maman, 2022. "Does Defence Expenditure Affect Education and Health expenditures in Saharan Africa?," African Journal of Economic Review, African Journal of Economic Review, vol. 10(4), September.
    6. Haldrup, Niels & Hylleberg, Svend & Pons, Gabriel & Sanso, Andreu, 2007. "Common Periodic Correlation Features and the Interaction of Stocks and Flows in Daily Airport Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 21-32, January.
    7. Athanasopoulos, George & de Carvalho Guillén, Osmani Teixeira & Issler, João Victor & Vahid, Farshid, 2011. "Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions," Journal of Econometrics, Elsevier, vol. 164(1), pages 116-129, September.
    8. Chu, Amanda M.Y. & Lv, Zhihui & Wagner, Niklas F. & Wong, Wing-Keung, 2020. "Linear and nonlinear growth determinants: The case of Mongolia and its connection to China," Emerging Markets Review, Elsevier, vol. 43(C).
    9. Peter Winker & Dietmar Maringer, 2004. "Optimal Lag Structure Selection in VEC-Models," Contributions to Economic Analysis, in: New Directions in Macromodelling, pages 213-234, Emerald Group Publishing Limited.
    10. Anatoli Colicev & Ashwin Malshe & Koen Pauwels, 2018. "Social Media and Customer-Based Brand Equity: An Empirical Investigation in Retail Industry," Administrative Sciences, MDPI, vol. 8(3), pages 1-16, September.
    11. Eleni Thanou & Dikaios Tserkezos, 2008. "Nonlinear Diachronic Effects Between Stock Returns and Mutual Fund Flows: Additional Empirical Evidence from the Athens Stocks Exchange," Working Papers 0826, University of Crete, Department of Economics.
    12. Li, Chao & Shang, Pengjian, 2018. "Complexity analysis based on generalized deviation for financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 118-128.
    13. Yan Qian & Zijun Wang, 2021. "A model selection approach to jointly testing for structural breaks and cointegration with application to the Eurocurrency interest rates market," Empirical Economics, Springer, vol. 61(2), pages 799-825, August.
    14. K. Lebedeva, 2015. "An Empirical Analysis of the Russian Financial Markets’ Liquidity and Returns," Review of Business and Economics Studies // Review of Business and Economics Studies, Финансовый Университет // Financial University, vol. 3(3), pages 5-31.
    15. Costantini, Valeria & Martini, Chiara, 2010. "The causality between energy consumption and economic growth: A multi-sectoral analysis using non-stationary cointegrated panel data," Energy Economics, Elsevier, vol. 32(3), pages 591-603, May.
    16. Henrik Hansen & John Rand, 2006. "On the Causal Links Between FDI and Growth in Developing Countries," The World Economy, Wiley Blackwell, vol. 29(1), pages 21-41, January.
    17. Franses, Philip Hans & Boswijk, H. Peter, 1996. "Temporal aggregation in a periodically integrated autoregressive process," Statistics & Probability Letters, Elsevier, vol. 30(3), pages 235-240, October.
    18. Mezgebo, Taddese, 2009. "A multivariate approach for identification of optimal locations with in Ethiopia’s wheat market to tackle soaring inflation on food price," MPRA Paper 18663, University Library of Munich, Germany.
    19. Jacobo Campo & Henry Mendoza, 2018. "Public expenditure and economic growth: a regional analysis for Colombia, 1984-2012," Lecturas de Economía, Universidad de Antioquia, Departamento de Economía, issue 88, pages 77-108, Enero - J.
    20. GONZALO, Jesus & PITARAKIS, Jean-Yves, 1994. "Comovements in Large Systems," LIDAM Discussion Papers CORE 1994065, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:14:y:2024:i:3:p:333-:d:1342371. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.