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

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Pedosphere 2022年第5期32卷 718-732页

作者： Khabat KHOSRAVI Phuong t.t.NGO Rahim BARZEGAR John QUILtY mohammad t.aalami Dieu t.BUI Department of Watershed Management Engineering Ferdowsi University of MashhadMashhad 93(Iran) Department of Earth and Environment Florida International UniversityMiami 33199(USA) Institute of Research and Development Duy Tan UniversityDa Nang 550000(Vietnam) Department of Bioresource Engineering McGill UniversitySte Anne de Bellevue QC H9X(Canada) Faculty of Civil Engineering University of TabrizTabriz 51(Iran) Department of Civil and Environmental Engineering University of WaterlooWaterloo N2L 3G1(Canada) Department of Business and It University of South-Eastern NorwayNotodden 3603(Norway)

Water infiltration into soil is an important process in hydrologic cycle;however,its measurement is difficult,time-consuming and *** and physical models have been developed to predict cumulative infiltration(CI),but are often *** 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 *** 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 ***,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 *** 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 *** best input combinations were different for different *** results showed that all hybrid algorithms enhanced the CI prediction accuracy compared to the standalone *** 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=

关键词： additive regression hybrid algorithms empirical model soil water infiltration weighted instances handler wrapper

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Multi-step ahead soil temperature forecasting at different depths based on meteorological data:Integrating resampling algorithms and machine learning models

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Pedosphere 2023年第3期33卷 479-495页

作者： Khabat KHOSRAVI Ali GOLKARIAN Rahim BARZEGAR mohammad t.aalami Salim HEDDAM Ebrahim OMIDVAR Saskia D.KEESStRA Manuel LÓPEZ-VICENtE Department of Earth and Environment Florida International UniversityMiami 33199(USA) Department of Watershed Management Engineering Ferdowsi University of MashhadMashhad 91779-48974(Iran) Department of Bioresource Engineering McGill University21111 LakeshoreSte Anne de BellevueMontreal H9X 3V9(Canada) Department of Geography&Environmental Studies Wilfrid Laurier UniversityWaterloo N2L 3G1(Canada) Faculty of Civil Engineering University of Tabriz29 Bahman BlvTabriz 51666-16471(Iran) Laboratory of Research in Biodiversity Interaction Ecosystem and Biotechnology University 20 Août 1955Route El HadaikBP 26Skikda 21000(Algeria) Department of Watershed Management Engineering University of KashanKashan 87317-53153(Iran) team Soil Water and Land UseWageningen Environmental ResearchDroevendaalsesteeg 3Wageningen 6708RC(Netherlands) Departamento de Análisis Geográfico Regional y Geografía Física Facultad de Filosofía y LetrasCampus Universitario de CartujaUniversidad de GranadaGranada 18071(Spain) Group Aquaterra CICAUniversidade da CoruñaAs Carballeiras s/nCampus de ElviñaA Coruña 15071(Spain)

Direct soil temperature(St)measurement is time-consuming and costly;thus,the use of simple and cost-effective machine learning(ML)tools is *** this study,ML approaches,including KStar,instance-based K-nearest learning(IBK),and locally weighted learning(LWL),coupled with resampling algorithms of bagging(BA)and dagging(DA)(BA-IBK,BA-KStar,BA-LWL,DA-IBK,DA-KStar,and DA-LWL)were developed and tested for multi-step ahead(3,6,and 9 d ahead)St *** addition,a linear regression(LR)model was used as a benchmark to evaluate the results.A dataset was established,with daily St time-series at 5 and 50 cm soil depths in a farmland as models’output and meteorological data as models’input,including mean(t_(mean)),minimum(tmin),and maximum(t_(max))air temperatures,evaporation(Eva),sunshine hours(SSH),and solar radiation(SR),which were collected at Isfahan Synoptic Station(Iran)for 13 years(1992–2005).Six different input combination scenarios were selected based on Pearson’s correlation coefficients between inputs and outputs and fed into the *** used 70%of the data to train the models,with the remaining 30%used for model evaluation via multiple visual and quantitative ***?ndings showed that t_(mean)was the most effective input variable for St forecasting in most of the developed models,while in some cases the combinations of variables,including t_(mean)and t_(max)and t_(mean),t_(max),tmin,Eva,and SSH proved to be the best input *** the evaluated models,BA-KStar showed greater compatibility,while in most cases,BA-IBK and-LWL provided more accurate results,depending on soil *** the 5 cm soil depth,BA-KStar had superior performance(i.e.,Nash-Sutcliffe efficiency(NSE)=0.90,0.87,and 0.85 for 3,6,and 9 d ahead forecasting,respectively);for the 50 cm soil depth,DA-KStar outperformed the other models(i.e.,NSE=0.88,0.89,and 0.89 for 3,6,and 9 d ahead forecasting,respectively).the results con?rmed that all hybrid models had higher prediction

关键词： bootstrap aggregating algorithm data mining disjoint aggregating algorithm ensemble modeling hybrid model

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Evaluation of total load sediment transport formulas using ANN

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International Journal of Sediment Research 2009年第3期24卷 274-286页

作者： Chih ted YANG Reza MARSOOLI mohammad taghi aalami Water Resources Colorado State University Graduate student of Civil-Hydraulics Engineering University of Tabriz Civil-Hydraulics Engineering University of Tabriz

the calculated results from various sediment transport formulas often differ from each other and from measured data. Some parameters in the sediment transport formulas are more effective than others to estimate total sediment load. In this study, an Artificial Neural Network (ANN) model is trained using four dominant parameters of sediment transport formulas. ANN models are able to reveal hidden laws of natural phenomena such as sediment transport process. the results of ANN and some total bed material load sediment transport formulas have been compared to indicate the importance of variables which can be used in developing sediment transport formulas. to train ANN, average flow velocity, water surface slopes, average flow depth, and median particle diameter are used as dominant parameters to estimate total bed material load. two hundreds and fifty samples are used to train the ANN model. twenty-four sets of field data not used in the training nor calibration of ANN are used to compare or verify the accuracy of ANN and some well-known total bed material load formulas. the test results show that the ANN model developed in this study using minimum number of dominant factors is a reliable and uncomplicated method to predict total sediment transport rate or total bed material load transport rate. Results show that the accuracy of formulas in descending order are those by Yang (1973), Laursen (1958), Engelund and Hansen (1972), Ackers and White (1973), and toffaleti (1969). these results are similar to those made by ASCE (1982) based on laboratory and field data not used in this paper. Study results also show that the formulas based on physical laws of sediment transport, like those formulas that were developed based on power concept, are more accurate than other formulas for estimating total bed material sediment load in rivers.

关键词： Artificial Neural Network (ANN) total bed material load Sediment transport formulas

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textural, color and sensory attributes of peanut kernels as affected by infrared roasting method

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Information Processing in Agriculture 2019年第2期6卷 255-264页

作者： Hadi Bagheri Mahdi Kashaninejad Aman mohammad Ziaiifar Mehran aalami Faculty of Food Science&technology Gorgan University of Agricultural Sciences and Natural ResourcesGorganIran

Roasting is one of the widespread methods for processing of nuts that significantly enhances the flavor,color,texture and appearance of *** this research,the response surface methodology was used to optimize the roasting process over a range of infrared power(250–450 W)and roasting times(10–30 min).the moisture content,color parameters(L*,a*,b*and total color difference(DE)),textural characteristics(hardness and compressive energy),energy consumption and sensory evaluation(total acceptation)were determined after roasting and modeled by response surface methodology(RSM).Increasing in roasting IR power and time caused increasing in the energy consumption,a*,b*and DE *** L*value,moisture content,hardness and compressive energy also decreased with increasing roasting IR power and *** full quadratic model developed by RSM adequately described the changes in the b*value,moisture content and *** result of RSM analysis showed that all color and textural parameters could be used to monitor the roasting of peanut kernels in an infrared roaster,while application of RSM for developing a predictive model that described the total acceptance changes during roasting of peanut kernels was not *** obtain the desired color,moisture,texture and acceptation,the optimum roasting range for production of snack was determined as 370W for 20 min.

关键词： Peanut kernels IR roasting RSM Energy consumption

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