Cumulative infiltration and infiltration rate prediction using optimized deep learning algorithms: A study in Western Iran

Journal of Hydrology: Regional Studies

Khabat Khosravi¹  Somayeh Panahi²  Ebrahim Omidvar³  Sajjad Ahmad⁴  Chang-Wook Lee⁵  Mahdi Panahi⁶  Assefa M Melesse⁷  Salim Heddam⁸ 

[1] Geoscience Platform Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124, Gwahak-ro Yuseong-gu, Daejeon, 34132, South Korea;Department of Civil and Environmental Engineering and Construction, University of Nevada, Las Vegas, USA;Department of Earth and Environment, Florida International University, Miami, USA;Department of Watershed Management Engineering, Ferdowsi University of Mashhad, Mashhad, Iran;Department of Watershed Management Engineering, University of Kashan, Kashan, Iran;Division of Science Education, Kangwon National University, College of Education,4-301, Gangwondaehak-gil, Chuncheon-si, Gangwon-do, 24341, South Korea;Laboratory of Research in Biodiversity Interaction Ecosystem and Biotechnology, University 20 Août 1955, Route El Hadaik, BP 26, Skikda, Algeria;Young Researchers and Elites Club, North Tehran Branch, Islamic Azad University, Tehran, Iran;

关键词: Cumulative infiltration;    Infiltration rate;    Deep learning;    CNN;    Metaheuristic;    Iran;   

DOI  :  

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown