Modelling of the impact of water quality on the infiltration rate of the soil

Singh, Balraj; Sihag, Parveen; Deswal, Surinder

doi:10.1007/s13201-019-0892-1

Cited by 20 publications

(15 citation statements)

References 35 publications

(23 reference statements)

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“…Figure 9 shows result of sensitivity analysis of input parameters. The most sensitive parameters were time (T), water depth (d) (it is in accordance with the results available in literature (Singh et al 2019)) and number of plant (n). The least sensitive parameter was inflow to rain garden (r).…”

Section: Sensitivity Analysissupporting

confidence: 89%

“…In present study, for GBM and DL the CC values obtained for training and testing data set are 0.999, 0.933 and 0.995, 0.976, respectively. Thus, the accuracy of GBM and DL are much better as compared to other models used by Singh et al (2019).…”

Section: Comparison Of Performancesmentioning

confidence: 93%

“…Kumar & Sihag (2019) proposed the application of machine learning for predicting the infiltration rate through different soil media. Singh et al (2019) employed regression techniques for predicting effect of water quality on infiltration rate. Angelaki et al (2021) compared the performance of ANN, SVM and FUZZY for estimating the infiltration rate through soil.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Rain garden infiltration rate modeling using gradient boosting machine and deep learning techniques

Kumar

Singh

2021

Water Science and Technology

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Rain garden are effective in reducing storm water runoff, whose efficiency depends upon several parameters such as soil type, vegetation and metrological factors. Evaluation of rain gardens has been done by various researchers. However, knowledge for sound design of rain gardens is still very limited, particularly the accurate modeling of infiltration rate and how much it differs from infiltration of natural ground surface. The present study uses experimentally observed infiltration rate of rain gardens with different types of vegetation (grass, candytuft, marigold and daisy with different plant densities) and flow conditions. After that, modeling has been done by the popular infiltration model i.e. Philip's model (which is valid for natural ground surface) and soft computing tools viz. Gradient Boosting Machine (GBM) and Deep Learning (DL). Results suggest a promising performance (in terms of CC, RMSE, MAE, MSE and NSE) by GBM and DL in comparison to the relation proposed by Philip's model (1957). Most of the values predicted by both GBM and DL are within scatter limits of ±5%, whereas the values by Philips model are within the range of ±25% error lines and even outside. GBM performs better than DL as the values of the correlation coefficients and Nash-Sutcliffe model efficiency (NSE) coefficient are the highest and the root mean square error is the lowest. The results of the study will be useful in selection of plant type and their density of the rain garden in the urban area.

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Section: Sensitivity Analysissupporting

confidence: 89%

Section: Comparison Of Performancesmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

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Rain garden infiltration rate modeling using gradient boosting machine and deep learning techniques

Kumar

Singh

2021

Water Science and Technology

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“…Some latest studies suggested successful application of soft computing techniques, viz. SVM, GPR, M5 tree and random forest regression to the field of groundwater hydrology (Singh et al 2017(Singh et al , 2019aAngelaki et al 2018;Sihag et al 2018a, b, c;Vand et al 2018;Sihag et al 2019b, c), water resources (Kumar et al 2018;Sepahvand et al 2019;Singh et al 2018a, b;Tiwari and Sihag 2018;Tiwari et al 2019) and engineering (Nain et al 2018(Nain et al , 2019Mehdipour et al 2018;Mohanty et al 2019). Keeping in view the importance of M5 tree and random forest regression techniques, the present research deals with the implementation of these techniques in an attempt to relate unsaturated hydraulic conductivity of the field data measured from 20 locations of Kurukshetra district, Haryana, with the soil physical properties.…”

Section: Introductionmentioning

confidence: 99%

Random forest, M5P and regression analysis to estimate the field unsaturated hydraulic conductivity

2019

Self Cite

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Hydraulic conductivity of soil reveals its influencing role in the studies related to management of surface and subsurface flow, e.g. irrigation and drainage projects, and solute mass transport models. Direct measurements of hydraulic conductivity have many difficulties due to spatial variation of the property in the field. Pertaining to this problem, in this study, estimation models have been developed using machine learning methods (M5 tree model and random forest model) in an attempt to estimate the accurate values of unsaturated hydraulic conductivity related to basic soil properties (clay, silt and sand content, bulk density and moisture content). Data set was collected from the experimental measurements of cumulative infiltration using mini disc infiltrometer at the study area (Kurukshetra, India). A multivariate nonlinear regression (MNLR) relationship was derived, and the performance of this model was compared with the machine learning-based models. The evaluation of the results, based on statistical criteria (R 2 , RMSE, MAE), suggested that random forest regression model is superior in accurate estimations of the unsaturated hydraulic conductivity of field data relative to M5 model tree and MNLR.

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“…Many researchers have developed various conventional models to estimate the IR (Kostiakov, 1932;Philip, 1957;Sihag et al, 2017). Alternatively, the IR can be modelled using soft computing techniques such as the artificial neural network, adaptive neuro-fuzzy inference system, and fuzzy logic system (FLS) approaches (Singh et al, 2018;Singh et al, 2019). The conventional models are site specific and require model parameters, whereas the soft computing-based models are used generally for the study area.…”

Section: Introductionmentioning

confidence: 99%

Developing a fuzzy logic model for predicting soil infiltration rate based on soil texture properties

Dewidar

Al-Ghobari

Alataway

2019

WSA

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The prediction of the soil infiltration rate is advantageous in hydrological design, watershed management, irrigation, and other agricultural studies. Various techniques have been widely used for this with the aim of developing more accurate models; however, the improvement of the prediction accuracy is still an acute problem faced by decision makers in many areas. In this paper, an intelligent model based on a fuzzy logic system (FLS) was developed to obtain a more accurate predictive model for the soil infiltration rate than that generated by conventional methods. The input variables that were considered in the fuzzy model included the silt and clay contents. The developed fuzzy model was tested against both the observed data and multiple linear regression (MLR). The comparison of the developed fuzzy model and MLR model indicated that the fuzzy model can simulate the infiltration process quite well. The coefficient of determination, root mean square error, mean absolute error, model efficiency, and overall index of the fuzzy model were 0.953, 1.53, 1.28, 0.953, and 0.954, respectively. The corresponding MLR model values were 0.913, 2.37, 1.92, 0.913, and 0.914, respectively. The sensitivity results indicated that the clay content is the most influential factor when the FLS-based modelling approach is used for predicting the soil infiltration rate.

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Modelling of the impact of water quality on the infiltration rate of the soil

Cited by 20 publications

References 35 publications

Rain garden infiltration rate modeling using gradient boosting machine and deep learning techniques

Rain garden infiltration rate modeling using gradient boosting machine and deep learning techniques

Random forest, M5P and regression analysis to estimate the field unsaturated hydraulic conductivity

Developing a fuzzy logic model for predicting soil infiltration rate based on soil texture properties

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