Evaluation of deep machine learning-based models of soil cumulative infiltration

Infiltration is the process by which water enters the soil, and it plays a significant role in the hydrologic cycle. Direct measurement of infiltration is time consuming; however, empirical and physical models are inaccurate. In this study, we compared the results of a deep learning-based convolutio...

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Bibliographic Details
Published in:Earth science informatics 2022-09, Vol.15 (3), p.1861-1877
Main Authors: Sepahvand, Alireza, Golkarian, Ali, Billa, Lawal, Wang, Kaiwen, Rezaie, Fatemeh, Panahi, Somayeh, Samadianfard, Saeed, Khosravi, Khabat
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Bulk density
Correlation coefficient
Correlation coefficients
Deep learning
Earth and Environmental Science
Earth Sciences
Earth System Sciences
Errors
Evaluation
Group method of data handling
Heuristic methods
Hydrologic cycle
Hydrology
Infiltration
Information Systems Applications (incl.Internet)
Machine learning
Mathematical models
Modelling
Moisture content
Moisture effects
Neural networks
Ontology
Parameters
Particle swarm optimization
Prediction models
Predictions
Research Article
Simulation and Modeling
Soil density
Soil moisture
Soil moisture content
Soil water
Space Exploration and Astronautics
Space Sciences (including Extraterrestrial Physics
Support vector machines
Time measurement
Variables
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Summary:Infiltration is the process by which water enters the soil, and it plays a significant role in the hydrologic cycle. Direct measurement of infiltration is time consuming; however, empirical and physical models are inaccurate. In this study, we compared the results of a deep learning-based convolutional neural network (CNN) algorithm with those of models based on standalone support vector regression (SVR) and group method of data handling (GMDH) algorithms. We also tested a hybridized SVR and GMDH-based model enhanced by three metaheuristic algorithms: gray wolf optimization (GWO), bat algorithm (BA), and particle swarm optimization (PSO). Measured variables including measurement time; sand, clay and silt contents; bulk density; and soil moisture content were used as model inputs to predict cumulative infiltration as an output. Finally, models were evaluated using the Pearson correlation coefficient, root mean square error, mean absolute error, Nash–Sutcliffe efficiency, percentage of bias, and relative error. Weighting of the model input parameters/variables demonstrated that time was the most effective variable for predicting cumulative infiltration, whereas the most powerful parameter input combination required all seven variables. The prediction model evaluation results showed that the hybridized models improved the predictive capability of the standalone models.
ISSN:1865-0473
1865-0481
DOI:10.1007/s12145-022-00830-7