Comparing the Soil Conservation Service model with new machine learning algorithms for predicting cumulative infiltration in semi-arid regions

Water infiltration into soil is an important process in hydrologic cycle; however, its measurement is difficult, time-consuming and costly. Empirical and physical models have been developed to predict cumulative infiltration (CI), but are often inaccurate. In this study, several novel standalone mac...

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Bibliographic Details
Published in:	Pedosphere Vol. 32; no. 5; pp. 718 - 732
Main Authors:	KHOSRAVI, Khabat, NGO, Phuong T.T., BARZEGAR, Rahim, QUILTY, John, AALAMI, Mohammad T., BUI, Dieu T.
Format:	Journal Article
Language:	English
Published:	Beijing Elsevier Ltd 01-10-2022 Elsevier Science Ltd Faculty of Civil Engineering,University of Tabriz,Tabriz 51 Iran%Department of Civil and Environmental Engineering,University of Waterloo,Waterloo N2L 3G1 Canada%Faculty of Civil Engineering,University of Tabriz,Tabriz 51 Iran%Department of Business and IT,University of South-Eastern Norway,Notodden 3603 Norway Department of Watershed Management Engineering,Ferdowsi University of Mashhad,Mashhad 93 Iran Department of Earth and Environment,Florida International University,Miami 33199 USA%Institute of Research and Development,Duy Tan University,Da Nang 550000 Vietnam%Department of Bioresource Engineering,McGill University,Ste Anne de Bellevue QC H9X Canada
Subjects:	additive regression Algorithms Arid regions Arid zones Bulk density Datasets empirical model Error analysis hybrid algorithms Hydrologic cycle Hydrology Infiltration Learning algorithms Machine learning Moisture content Moisture effects Optimization Predictions Root-mean-square errors Semi arid areas Semiarid lands Soil conservation Soil density Soil moisture Soil water soil water infiltration Subgroups Time measurement Water content Water infiltration weighted instances handler wrapper empirical model soil water infiltration additive regression hybrid algorithms weighted instances handler wrapper
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Summary:	Water infiltration into soil is an important process in hydrologic cycle; however, its measurement is difficult, time-consuming and costly. Empirical and physical models have been developed to predict cumulative infiltration (CI), but are often inaccurate. In this study, several novel standalone machine learning algorithms (M5Prime (M5P), decision stump (DS), and sequential minimal optimization (SMO)) and hybrid algorithms based on additive regression (AR) (i.e., AR-M5P, AR-DS, and AR-SMO) and weighted instance handler wrapper (WIHW) (i.e., WIHW-M5P, WIHW-DS, and WIHW-SMO) were developed for CI prediction. The Soil Conservation Service (SCS) model developed by the United States Department of Agriculture (USDA), one of the most popular empirical models to predict CI, was considered as a benchmark. Overall, 154 measurements of CI (explanatory/input variables) were taken from 16 sites in a semi-arid region of Iran (Illam and Lorestan provinces). Six input variable combinations were considered based on Pearson correlations between candidate model inputs (time of measuring and soil bulk density, moisture content, and sand, clay, and silt percentages) and CI. The dataset was divided into two subgroups at random: 70% of the data were used for model building (training dataset) and the remaining 30% were used for model validation (testing dataset). The various models were evaluated using different graphical approaches (bar charts, scatter plots, violin plots, and Taylor diagrams) and quantitative measures (root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe efficiency (NSE), and percent bias (PBIAS)). Time of measuring had the highest correlation with CI in the study area. The best input combinations were different for different algorithms. The results showed that all hybrid algorithms enhanced the CI prediction accuracy compared to the standalone models. The AR-M5P model provided the most accurate CI predictions (RMSE = 0.75 cm, MAE = 0.59 cm, NSE = 0.98), while the SCS model had the lowest performance (RMSE = 4.77 cm, MAE = 2.64 cm, NSE = 0.23). The differences in RMSE between the best model (AR-M5P) and the second-best (WIHW-M5P) and worst (SCS) were 40% and 84%, respectively.
ISSN:	1002-0160 2210-5107
DOI:	10.1016/j.pedsph.2022.06.009