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

Vijay Kumar Singh, Kanhu Charan Panda, Atish Sagar, Nadhir Al-Ansari, Huan Feng Duan, Pradosh Kumar Paramaguru, Dinesh Kumar Vishwakarma, Ashish Kumar, Devendra Kumar, P. S. Kashyap, R. M. Singh, Ahmed Elbeltagi

Research output: Journal article publicationJournal articleAcademic researchpeer-review

3 Citations (Scopus)

Abstract

Saturated hydraulic conductivity (Ks) 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 Ks. 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.

Original languageEnglish
Pages (from-to)1082-1099
Number of pages18
JournalEngineering Applications of Computational Fluid Mechanics
Volume16
Issue number1
DOIs
Publication statusPublished - 2022

Keywords

  • genetic algorithm
  • Hydraulic conductivity
  • Multilayer Perceptron
  • Pedotransfer Functions
  • support vector machine

ASJC Scopus subject areas

  • Computer Science(all)
  • Modelling and Simulation

Fingerprint

Dive into the research topics of 'Novel Genetic Algorithm (GA) based hybrid machine learning-pedotransfer Function (ML-PTF) for prediction of spatial pattern of saturated hydraulic conductivity'. Together they form a unique fingerprint.

Cite this