the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Prediction of Hysteretic Matric Potential Dynamics Using Artificial Intelligence: Application of Autoencoder Neural Networks
Abstract. Information on soil water potential is essential to assess soil moisture state, to prevent soil compaction in weak soils, and to optimize crop management. In lack of direct measurements, the soil water potential values must be deduced from soil water content dynamics that can be monitored at plot scale or obtained at larger scale from remote sensing information. Because the relationship between water content and soil water potential in natural field soils is highly ambiguous, the prediction of soil water potential from water content data is a big challenge. The hysteretic relationship observed in nine soil profiles in the region of Solothurn (Switzerland) is not a simple function of texture or wetting and drainage cycles but depends on seasonal patterns that may be related to soil structural dynamics. Because the physical mechanisms governing seasonal hysteresis are unclear, we developed a deep neural network model that predicts water potential changes using rainfall, potential evapotranspiration, and water content time series as inputs. To adapt the model for multiple locations, we incorporated a Deep Autoencoder Neural Network as a classifier. The autoencoder compresses the water content time series into a site-specific feature that is highly representative of the underlying water content dynamics of each site and quantifies the similarity of dynamic patterns. By adding the Autoencoder's output as an additional input and training the neural network model with three stations located in three major classes founded by the autoencoder, we predict matric potential for other sites. This method has the potential to deduce the dynamics of matric potential from water content data (including satellite data) despite strong seasonal effects that cannot be captured by standard methods.
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Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Preprint
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
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- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2024-407', Ilhan Özgen-Xian, 29 Apr 2024
Summary
Aqel et al. present a neural network (NN) based approach to predict matric potential from soil water content observations. Using an autoencoder, they extract the most relevant features of the soil water retention dynamics. They input their results into a deep neural network (DNN), which increases the transferability of the DNN.
Assessment
The approach presented in this paper is convincing. The manuscript is well-written. Prediction of hysteresis in soil water retention is of interest to the soil hydrology and soil physics community.
I don't have major comments. Thus, I recommend accepting the manuscript after minor revisions. I have some minor comments below.
Nash-Sutcliffe efficiency
The Nash-Sutcliffe coefficient tends to emphasise maxima in a time series, which might bias the results. An additional interesting metric would be the Kling-Gupta efficiency (Knoben et al., 2019; doi: 10.5194/hess-23-4323-2019).
Local nature of the model
The results obtained are from sites that share similar climate and topography. I wonder if this workflow would work as well in different regions of the world, or if further adjustments must be made.
Physically-based modelling
I partially agree that the reductionist mechanistic models might be unable to account for the full complexity inherent in the soil water retention process. Input-agnostic approaches such as neural networks surely have an advantage when it comes to predicting matric potential. However, physically-based modelling is also a tool for process understanding that could potentially help us disentangling the effects of all the interacting processes that control soil water retention. I know that there are efforts to make machine learning a tool for process understanding as well. Perhaps the authors could comment briefly on this and place their work in this discussion?
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Citation: https://doi.org/10.5194/egusphere-2024-407-RC1 -
AC1: 'Reply on RC1', Nedal Aqel, 30 Jun 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-407/egusphere-2024-407-AC1-supplement.pdf
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AC1: 'Reply on RC1', Nedal Aqel, 30 Jun 2024
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RC2: 'Comment on egusphere-2024-407', Anonymous Referee #2, 07 Jun 2024
General Comments
This manuscript presents an approach for predicting soil water potential and its hysteresis under natural field conditions by combining deep neural networks (DNN) with autoencoder neural networks. This integration leverages the strengths of both methods, with the autoencoder effectively compressing and capturing site-specific features of soil moisture dynamics, and the DNN utilizing these features to enhance prediction accuracy.
Overall, the method is promising and convincing, and the manuscript is well-organized and clearly written. I have only a few concerns and suggestions, primarily regarding the model's generalization capability to clay soils and regions with significantly different climatic conditions, and the model's interpretability.
Specific Comments
- Lines 106-109: Generalization Capability: Autoencoders are highly dependent on the quality and diversity of the training data. As shown in Figure 1, the selected region has relatively similar climatic conditions and soil types, mainly loams with a clay fraction less than 50%. I am curious about the model's generalization capability to different regions with varying climatic conditions and soil types, especially for clayey soils. Suggest expanding Section 4.1 to discuss this point and potential approaches to address this issue. Additionally, suggest discussing the possibility of using other autoencoders, such as variational autoencoder (VAE).
- Lines 218: Model Interpretability: The interpretability of the autoencoder's hidden layer representations is typically challenging. Suggest Including a discussion in the results analysis or discussion section on potential techniques to visualize the features learned by the autoencoder's hidden layers, which can help readers understand the model's internal workings.
- Lines 289-290: Why adopt an NSE value > 0.80 as the criterion for an optimal model? Please provide the rationale for selecting this value.
- Figure 2: The common unit for matric potential is -kPa. Please explain the relationship between the -kPa and -cm used in this manuscript.
- Equation 1: Please ensure that all parameters are clearly defined after the equation, and that their mathematical notation (bold, italic) is consistent throughout the manuscript.
Citation: https://doi.org/10.5194/egusphere-2024-407-RC2 -
AC2: 'Reply on RC2', Nedal Aqel, 30 Jun 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-407/egusphere-2024-407-AC2-supplement.pdf
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2024-407', Ilhan Özgen-Xian, 29 Apr 2024
Summary
Aqel et al. present a neural network (NN) based approach to predict matric potential from soil water content observations. Using an autoencoder, they extract the most relevant features of the soil water retention dynamics. They input their results into a deep neural network (DNN), which increases the transferability of the DNN.
Assessment
The approach presented in this paper is convincing. The manuscript is well-written. Prediction of hysteresis in soil water retention is of interest to the soil hydrology and soil physics community.
I don't have major comments. Thus, I recommend accepting the manuscript after minor revisions. I have some minor comments below.
Nash-Sutcliffe efficiency
The Nash-Sutcliffe coefficient tends to emphasise maxima in a time series, which might bias the results. An additional interesting metric would be the Kling-Gupta efficiency (Knoben et al., 2019; doi: 10.5194/hess-23-4323-2019).
Local nature of the model
The results obtained are from sites that share similar climate and topography. I wonder if this workflow would work as well in different regions of the world, or if further adjustments must be made.
Physically-based modelling
I partially agree that the reductionist mechanistic models might be unable to account for the full complexity inherent in the soil water retention process. Input-agnostic approaches such as neural networks surely have an advantage when it comes to predicting matric potential. However, physically-based modelling is also a tool for process understanding that could potentially help us disentangling the effects of all the interacting processes that control soil water retention. I know that there are efforts to make machine learning a tool for process understanding as well. Perhaps the authors could comment briefly on this and place their work in this discussion?
Â
Citation: https://doi.org/10.5194/egusphere-2024-407-RC1 -
AC1: 'Reply on RC1', Nedal Aqel, 30 Jun 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-407/egusphere-2024-407-AC1-supplement.pdf
-
AC1: 'Reply on RC1', Nedal Aqel, 30 Jun 2024
-
RC2: 'Comment on egusphere-2024-407', Anonymous Referee #2, 07 Jun 2024
General Comments
This manuscript presents an approach for predicting soil water potential and its hysteresis under natural field conditions by combining deep neural networks (DNN) with autoencoder neural networks. This integration leverages the strengths of both methods, with the autoencoder effectively compressing and capturing site-specific features of soil moisture dynamics, and the DNN utilizing these features to enhance prediction accuracy.
Overall, the method is promising and convincing, and the manuscript is well-organized and clearly written. I have only a few concerns and suggestions, primarily regarding the model's generalization capability to clay soils and regions with significantly different climatic conditions, and the model's interpretability.
Specific Comments
- Lines 106-109: Generalization Capability: Autoencoders are highly dependent on the quality and diversity of the training data. As shown in Figure 1, the selected region has relatively similar climatic conditions and soil types, mainly loams with a clay fraction less than 50%. I am curious about the model's generalization capability to different regions with varying climatic conditions and soil types, especially for clayey soils. Suggest expanding Section 4.1 to discuss this point and potential approaches to address this issue. Additionally, suggest discussing the possibility of using other autoencoders, such as variational autoencoder (VAE).
- Lines 218: Model Interpretability: The interpretability of the autoencoder's hidden layer representations is typically challenging. Suggest Including a discussion in the results analysis or discussion section on potential techniques to visualize the features learned by the autoencoder's hidden layers, which can help readers understand the model's internal workings.
- Lines 289-290: Why adopt an NSE value > 0.80 as the criterion for an optimal model? Please provide the rationale for selecting this value.
- Figure 2: The common unit for matric potential is -kPa. Please explain the relationship between the -kPa and -cm used in this manuscript.
- Equation 1: Please ensure that all parameters are clearly defined after the equation, and that their mathematical notation (bold, italic) is consistent throughout the manuscript.
Citation: https://doi.org/10.5194/egusphere-2024-407-RC2 -
AC2: 'Reply on RC2', Nedal Aqel, 30 Jun 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-407/egusphere-2024-407-AC2-supplement.pdf
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Nedal Aqel
Lea Reusser
Stephan Margreth
Andrea Carminati
Peter Lehmann
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(2065 KB) - Metadata XML