Earth Science Informatics, Volume (15), No (3), Year (2022-9) , Pages (1861-1877)

Title : ( Evaluation of deep machine learning-based models of soil cumulative infiltration )

Authors: Alireza Sepahvand , Ali Golkarian , Lawal Billa , Kaiwen Wang , Fatemeh Rezaie , Somayeh Panahi , Saeed Samadianfard , Khabat Khosravi ,

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Abstract

Infiltration is the process by which water enters the soil, and it plays a significant role in the hydrologic cycle. Direct measurement of infiltration is time consuming; however, empirical and physical models are inaccurate. In this study, we compared the results of a deep learning-based convolutional neural network (CNN) algorithm with those of models based on standalone support vector regression (SVR) and group method of data handling (GMDH) algorithms. We also tested a hybridized SVR and GMDH-based model enhanced by three metaheuristic algorithms: gray wolf optimization (GWO), bat algorithm (BA), and particle swarm optimization (PSO). Measured variables including measurement time; sand, clay and silt contents; bulk density; and soil moisture content were used as model inputs to predict cumulative infiltration as an output. Finally, models were evaluated using the Pearson correlation coefficient, root mean square error, mean absolute error, Nash–Sutcliffe efficiency, percentage of bias, and relative error. Weighting of the model input parameters/variables demonstrated that time was the most effective variable for predicting cumulative infiltration, whereas the most powerful parameter input combination required all seven variables. The prediction model evaluation results showed that the hybridized models improved the predictive capability of the standalone models.

Keywords

Soil cumulative infltration · Machine learning · Deep learning · Metaheuristic algorithms
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@article{paperid:1093103,
author = {علیرضا سپهوند and Golkarian, Ali and لاوال بیلا and کایون وانگ and فاطمه رضایی and سمیه پناهی and سعید صمدیان فرد and خه بات خسروی},
title = {Evaluation of deep machine learning-based models of soil cumulative infiltration},
journal = {Earth Science Informatics},
year = {2022},
volume = {15},
number = {3},
month = {September},
issn = {1865-0473},
pages = {1861--1877},
numpages = {16},
keywords = {Soil cumulative infltration · Machine learning · Deep learning · Metaheuristic algorithms},
}

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%0 Journal Article
%T Evaluation of deep machine learning-based models of soil cumulative infiltration
%A علیرضا سپهوند
%A Golkarian, Ali
%A لاوال بیلا
%A کایون وانگ
%A فاطمه رضایی
%A سمیه پناهی
%A سعید صمدیان فرد
%A خه بات خسروی
%J Earth Science Informatics
%@ 1865-0473
%D 2022

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