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Machine learning-based modeling of saturated hydraulic conductivity in soils of tropical semi-arid zone of India

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Abstract

Saturated hydraulic conductivity (Kfs) is the major parameter that affects the movement of water and solutes in soil strata. Although one can estimate the Kfs directly by using various field or laboratory methods, they turn out to be more time-consuming and painstaking while characterizing the spatial variability of Kfs. For this reason, some recent researches employ indirect approaches such as pedotransfer functions (PTF) and surface modeling methods for estimating Kfs of several scales. Pedotransfer functions are often developed by relating the Kfs with readily available soil properties such as bulk density, porosity, sand content, silt content, and organic material. The present research explores the suitability of Extreme Learning Machine (ELM) in developing PTF's for Kfs by using basic soil properties. In-situ field tests and laboratory experiments on collected samples were performed to acquire the datasets necessary for the analysis. Three competitive soft computing approaches, namely the ELM, Support Vector Machine (SVM), and Adaptive Neuro-Fuzzy Inference System (ANFIS) based on Fuzzy C-means Clustering optimized by Genetic Algorithm were exercised for developing the Kfs models. Further, the performance of these approaches in modeling Kfs was evaluated using various statistical mertics. The performance of ELM was found to be good in comparison to the other two models, with sufficiently good NSE values. The ELM model provided Kfs predictions at the Murarji Peth and Punanaka sites with an NSE of 0.90 and 0.83, respectively, while at the Mulegoan site, the ANFIS model was better with R = 0.80 and NSE = 0.64.

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Abbreviations

Kfs :

Saturated Hydraulic Conductivity

lnKfs :

Log Transformed Saturated Hydraulic Conductivity values

PTF:

Pedotransfer Functions

ELM:

Extreme Learning Machine

SVM:

Support Vector Machine

ANFIS:

Adaptive Neuro-Fuzzy Inference System

GMDH:

Group Method of Data Handling

FFMLNN:

Feedforward Multilayer Neural Network

SLFN:

Single-Layered Feedforward Network

SRM:

Structural Risk Minimization

FCM:

Fuzzy C-means

GA:

Genetic Algorithm

Q-Q:

Quantile-Quantile

BD:

Bulk Density

n:

Porosity

S:

Sand %

Si:

Silt %

C:

Clay %

G:

Particle Density

OM:

Organic Matter

RBF:

Radial Basis Function

R:

Coefficient of Correlation

RMSE:

Root Mean Square Error

NRMSE:

Normalized Root Mean Square Error

MRE:

Mean Relative Error

NSE:

Nash–Sutcliffe Efficiency

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Funding

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Authors and Affiliations

  1. Department of Civil Engineering, N. K. Orchid College of Engineering & Technology, Solapur, 413002, Maharashtra, India

    Satish Bhaurao More

  2. Department of Water Resources and Ocean Engineering, National Institute of Technology Karnataka, Surathkal, Mangalore, 575025, India

    Paresh Chandra Deka

  3. Department of Civil Engineering, Annasaheb Dange College of Engineering and Technology, Ashta, 416301, Maharashtra, India

    Amit Prakash Patil

  4. Department of Civil Engineering, Siddaganga Institute of Technology, Tumakuru, 572103, Karnataka, India

    Sujay Raghavendra Naganna

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  1. Satish Bhaurao More
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  2. Paresh Chandra Deka
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  4. Sujay Raghavendra Naganna
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Correspondence to Amit Prakash Patil.

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All the authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.

Appendix 1

Appendix 1

 

Table 7 Appendix 1. Summary of statistics of soil parameters (raw data) sampled at all three sites and all three depths (15 cm, 30 cm and 45 cm).
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More, S.B., Deka, P.C., Patil, A.P. et al. Machine learning-based modeling of saturated hydraulic conductivity in soils of tropical semi-arid zone of India. Sādhanā 47, 26 (2022). https://doi.org/10.1007/s12046-022-01805-6

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