Modelling of soil permeability using different data driven algorithms based on physical properties of soil

•The W-RF algorithm have the highest accuracy and efficiency for soil permeability prediction.•The results of sensitivity analysis show that clay > sand > OC > BD > PD were the most sensitive parameters for soil permeability prediction.•The RF algorithm was the best performing algorithm...

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Vydané v:Journal of hydrology (Amsterdam) Ročník 580; s. 124223
Hlavní autori: Singh, Vijay Kumar, Kumar, Devendra, Kashyap, P.S., Singh, Pramod Kumar, Kumar, Akhilesh, Singh, Sudhir Kumar
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 01.01.2020
Predmet:
CANFIS
clay
decision support systems
Decision Tree
drainage
fuzzy logic
groundwater
MLP
neural networks
prediction
Random Forest
runoff
sand
Sensitivity
silt
Soil permeability
support vector machines
SVM
Wavelet
Wavelet
MLP
Sensitivity
Soil permeability
Decision Tree
SVM
CANFIS
Random Forest
ISSN:0022-1694, 1879-2707
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Shrnutí:•The W-RF algorithm have the highest accuracy and efficiency for soil permeability prediction.•The results of sensitivity analysis show that clay > sand > OC > BD > PD were the most sensitive parameters for soil permeability prediction.•The RF algorithm was the best performing algorithm for permeability prediction based on wavelet and non-wavelet algorithms. Soil permeability is an important parameter for assessment of infiltration, runoff, ground water, drainage and structures design. In the current research, five different data driven algorithms namely Multilayer Perceptron (MLP), Co-Active Neuro-Fuzzy Inference System (CANFIS), Support Vector Machine (SVM), Decision Tree (DT) and Random Forest (RF) algorithms and also, their wavelets (W-MLP, W-CANFIS, W-SVM, W-DT and W-RF algorithms) were used to predict soil permeability based on physical properties of soil. Also, reliable information/input vectors were assessed based on Gamma Test (GT). Sand, silt, clay and organic content (OC) parameters were chosen as information vectors based on gamma test. The potential of data driven algorithms were evaluated based on different statistical indices during model development and validation phase. It was found that wavelet based algorithms viz. W-MLP, W-CANFIS, W-SVM, W-DT and W-RF simulated better results of soil permeability compared to non-wavelet (MLP, CANFIS, SVM, DT and RF) algorithms. Among all wavelet and non-wavelet algorithms, W-RF algorithm had the highest accuracy and efficiency of model. The results of sensitivity analysis indicated that clay > silt > sand > OC > BD > PD was the order of sensitive parameters for soil permeability prediction based on data driven algorithms.
Bibliografia:ObjectType-Article-1
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content type line 23
ISSN:0022-1694
1879-2707
DOI:10.1016/j.jhydrol.2019.124223