Document Type
Article
Publication Date
5-7-2021
Publication Title
Journal of Hydrology: Regional Studies
Volume
35
First page number:
1
Last page number:
19
Abstract
Study region: Sixteen different sites from two provinces (Lorestan and Illam) in the western part of Iran were considered for the field data measurement of cumulative infiltration, infiltration rate, and other effective variables that affect infiltration process. Study focus: Soil infiltration is recognized as a fundamental process of the hydrologic cycle affecting surface runoff, soil erosion, and groundwater recharge. Hence, accurate prediction of the infiltration process is one of the most important tasks in hydrological science. As direct measurement is difficult and costly, and empirical models are inaccurate, the current study proposed a standalone, and optimized deep learning algorithm of a convolutional neural network (CNN) using gray wolf optimization (GWO), a genetic algorithm (GA), and an independent component analysis (ICA) for cumulative infiltration and infiltration rate prediction. First, 154 raw datasets were collected including the time of measuring; sand, clay, and silt percent; bulk density; soil moisture percent; infiltration rate; and cumulative infiltration using field survey. Next, 70 % of the dataset were used for model building and the remaining 30 % was used for model validation. Then, based on the correlation coefficient between input variables and outputs, different input combinations were constructed. Finally, the prediction power of each developed algorithm was evaluated using different visually-based (scatter plot, box plot and Taylor diagram) and quantitatively-based [root mean square error (RMSE), mean absolute error (MAE), the Nash-Sutcliffe efficiency (NSE), and percentage of bias (PBIAS)] metrics. New Hydrological Insights for the Region: Finding revealed that the time of measurement is more important for cumulative infiltration, while soil characteristics (i.e. silt content) are more significant in infiltration rate prediction. This shows that in the study area, silt parameter, which is the dominant constituent parameter, can control infiltration process more effectively. Effectiveness of the variables in the present study, in the order of importance are time, silt, clay, moisture content, sand, and bulk density. This can be related to the fact that most of study area is rangeland and thus, overgrazing leads to compaction of the silt soil that can lead to a slow infiltration process. Soil moisture content and bulk density are not highly effective in our study because these two factors do not significantly change across the study area. Findings demonstrated that the optimum input variable combination, is the one in which all input variables are considered. The results illustrated that CNN algorithms have a very high performance, while a metaheuristic algorithm enhanced the performance of a standalone CNN algorithm (from 7% to 28 %). The results also showed that a CNN-GWO algorithm outperformed the other algorithms, followed by CNN-ICA, CNN-GA, and CNN for both cumulative infiltration and infiltration rate prediction. All developed algorithms underestimated cumulative infiltration, while overestimating infiltration rates.
Keywords
CNN; Cumulative infiltration; Deep learning; Infiltration rate; Iran; Metaheuristic
Disciplines
Hydrology
File Format
File Size
9346 KB
Language
English
Rights
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Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Repository Citation
Panahi, M.,
Khosravi, K.,
Ahmad, S.,
Panahi, S.,
Heddam, S.,
Melesse, A.,
Omidvar, E.,
Lee, C.
(2021).
Cumulative Infiltration and Infiltration Rate Prediction Using Optimized Deep Learning Algorithms: A Study in Western Iran.
Journal of Hydrology: Regional Studies, 35
1-19.
http://dx.doi.org/10.1016/j.ejrh.2021.100825
Corrigendum to “Cumulative infiltration and infiltration rate prediction using optimized deep learning algorithms: A study in Western Iran” [J. Hydrol. Reg. Stud. 35 (2021) 100825]
Comments
A corrigendum regarding the Acknowledgement section of this article was published December 2021. Corrigendum is attached to this record as supplemental content.