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Studies on predicting soil moisture levels at Andhra Loyola College, India, using SARIMA and LSTM models

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Abstract

Time series modeling is a way to predict future values by examining temporal data. The present study analyzes the monthly mean soil moisture data at various depths: surface, profile, and root soil moisture, spanning from 1981 to 2022. The analysis employs two distinct approaches: the statistical seasonal autoregressive integrated moving average (SARIMA) and a deep learning long short-term memory (LSTM). The models are trained on a data set, covering the period from 1981 to 2021, acquired from the agricultural site at Andhra Loyola College in Vijayawada, Andhra Pradesh, India. Subsequently, the data from 2021 to 2022 is reserved for testing purposes. The study provides comprehensive insights into the design of both SARIMA and LSTM models, along with an evaluation of their performance using established error metrics such as the model mean absolute percentage error (MAPE), mean absolute error (MAE), and root mean squared error (RMSE). In the context of surface soil moisture prediction, the LSTM model demonstrates superior performance compared to SARIMA. Specifically, LSTM achieves a notably lower MAPE of 0.0615 in contrast to SARIMA’s 0.1541, a reduced MAE of 0.0316 compared to 0.0871, and a diminished RMSE of 0.0412 as opposed to 0.1021. This pattern of enhanced accuracy persists across profile and root soil moisture predictions, further establishing LSTM’s supremacy in predictive capability across diverse soil moisture levels.

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All data generated or analyzed during this study are included in this published article.

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Authors and Affiliations

  1. Department of CSE, Dhanekula Institute of Engineering and Technology, Vijayawada, 521139, India

    M. Tanooj Kumar

  2. Department of Physics, Andhra Loyola College, Vijayawada, 520008, India

    M. C. Rao

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  1. M. Tanooj Kumar
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  2. M. C. Rao
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Contributions

MTK participated in data analysis, carried out sequence alignments, and drafted the manuscript and revised it critically. MCR participated in the design of the study and statistical analyses of the data and approved the final version. Both the authors gave final approval for publication.

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Correspondence to M. Tanooj Kumar.

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Kumar, M.T., Rao, M.C. Studies on predicting soil moisture levels at Andhra Loyola College, India, using SARIMA and LSTM models. Environ Monit Assess 195, 1426 (2023). https://doi.org/10.1007/s10661-023-12080-1

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