Novel Genetic Algorithm (GA) based hybrid machine learning-pedotransfer Function (ML-PTF) for prediction of spatial pattern of saturated hydraulic conductivity

• Published in: Engineering applications of computational fluid mechanics Vol. 16; no. 1; pp. 1082 - 1099
• Main Authors: Singh, Vijay Kumar, Panda, Kanhu Charan, Sagar, Atish, Al-Ansari, Nadhir, Duan, Huan-Feng, Paramaguru, Pradosh Kumar, Vishwakarma, Dinesh Kumar, Kumar, Ashish, Kumar, Devendra, Kashyap, P. S., Singh, R. M., Elbeltagi, Ahmed
• Format: Journal Article
• Language: English
• Published: Hong Kong Taylor & Francis 31-12-2022; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: genetic algorithm; Genetic algorithms; Geoteknik; Hydraulic conductivity; Hydraulics; Machine learning; Multilayer Perceptron; Multilayer perceptrons; Pedotransfer Functions; Performance prediction; Soil Mechanics; Soil water movement; Soils; support vector machine; Support vector machines
• ISSN: 1994-2060; 1997-003X; 1997-003X
• DOI: 10.1080/19942060.2022.2071994

Saturated hydraulic conductivity (K
s
) is an important soil characteristic that controls water moves through the soil. On the other hand, its measurement is difficult, time-consuming, and expensive; hence Pedotransfer Functions (PTFs) are commonly used for its estimation. Despite significant development over the years, the PTFs showed poor performance in predicting K
s
. Using Genetic Algorithm (GA), two hybrid Machine Learning based PTFs (ML-PTF), i.e. a combination of GA with Multilayer Perceptron (MLP-GA) and Support Vector Machine (SVM-GA), were proposed in this study. We compared the performances of four machine learning algorithms for different sets of predictors. The predictor combination containing sand, clay, Field Capacity, and Wilting Point showed the highest accuracy for all the ML-PTFs. Among the ML-PTFs, the SVM-GA algorithm outperformed the rest of the PTFs. It was noticed that the SVM-GA PTF demonstrated higher efficiency than the MLP-GA algorithm. The reference model for hydraulic conductivity prediction was selected as the SVM-GA PTF paired with the K-5 predictor variables. The proposed PTFs were compared with 160 models from past literature. It was found that the algorithms advocated were an improvement over these PTFs. The current model would help in efficient spatio-temporal measurement of hydraulic conductivity using pre-available databases.