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

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 t...

Full description

Saved in:
Bibliographic Details
Published in:Agriculture (Basel) 2024-03, Vol.14 (3), p.333
Main Authors: Basir, Md. Samiul, Noel, Samuel, Buckmaster, Dennis, Ashik-E-Rabbani, Muhammad
Format: Article
Language:English
Subjects:
Accuracy
Agricultural equipment
Agricultural production
Agricultural vehicles
Agriculture
Artificial intelligence
Cereal crops
Datasets
Deep learning
forecastability
Forecasting
historical data
Humidity
Long short-term memory
LSTM
Machine learning
Mathematical models
Meteorological data
Neural networks
Physical properties
Precipitation
Radiation
Rain
Regression analysis
Root development
Sea level
Seasons
Soil moisture
Statistical analysis
Statistical models
Time series
time series analysis
Topography
VAR
Variables
Weather
weather data integration
Weather forecasting
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 R2, MAE (m3m−3), MSE (m6m−6), and RMSE (m3m−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 R2, MAE (m3m−3), MSE (m6m−6), and RMSE (m3m−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.
ISSN:2077-0472
2077-0472
DOI:10.3390/agriculture14030333