Journal Article

Modelling of infiltration of sandy soil using gaussian process regression

Ranjan, Subodh, Sihag, Parveen, Tiwari, N. K.

2017

Overview

The aim of this paper to assesses the potential of machine learning approaches, i.e. multi-linear regression (MLR), support vector regression (SVR), Gaussian process (GP) regression of cumulative infiltration and compares their performances with three traditional models [Kostiakov model, US-Soil Conservation Service (SCS) model and Philip’s model]. Data set as many as 413 were obtained by conducting experiments in laboratory of NIT Kurukshetra. It is observed from the experiments that moisture content influences the cumulative infiltration of soil. Out of 413 data set 289 arbitrary selected were used for training the models, whereas remaining 124 were used for testing. Input data set consist of time, sand, rice husk ash, fly ash, suction head, bulk density and moisture content where as cumulative infiltration was considered as output. Two kernel function i.e. Pearson VII and radial based kernel function were used with both SVR and GP regression. The results after comparison suggests that the GP regression based approach works better than SVR, MLR, Kostiakov model, SCS model and Philip’s model approaches and it could be successfully used in prediction of cumulative infiltration data.

• Publisher: Springer International Publishing,Springer Nature B.V
• Subject: Bulk density / Chemistry and Earth Sciences / Computer Science / Data / Datasets / Earth and Environmental Science / Earth Sciences / Earth System Sciences / Ecosystems / Environment / Fly ash / Gaussian process / Infiltration / Learning algorithms / Machine learning / Math. Appl. in Environmental Science / Mathematical Applications in the Physical Sciences / Modelling / Moisture content / Original Article / Physics / Regression / Regression analysis / Sandy soils / Soil / Soil conservation / Soil infiltration / Soil moisture / Soil suction / Soils / Statistics for Engineering / Support vector machines / Training