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

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.