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"Assessment of groundwater potential modeling using support vector machine optimization based on Bayesian multi-objective hyperparameter algorithm"
"Assessment of groundwater potential modeling using support vector machine optimization based on Bayesian multi-objective hyperparameter algorithm"
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Date
2023
Authors
Duong Tran Anh, Manish Pandey, Varun Narayan Mishra, Kiran Kumari Singh, Kourosh Ahmadi, Saeid Janizadeh, Thanh Thai Tran, Nguyen Thi Thuy Linh, Nguyen Mai Dang.
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Abstract
"Today, water supply in order to achieve sustainable development goals is one of the most important
concerns and challenges in most countries. For this reason, accurate identification of areas with
groundwater potential is one of the important tools in the protection, management and exploitation
of water resources. Accordingly, the present study was conducted with the aim of modeling and
predicting groundwater potential in Markazi province, Iran using Multivariate adaptive regression
spline (MARS) and Support vector machine (SVM) machine learning models and using two random
search (RS) and Bayesian optimization hyperparameter algorithms to optimize the parameters of
the SVM model. For this purpose, 18 variables affecting the groundwater potential and 3482 spring
locations were used to model the groundwater potential. Data for modeling were divided into two
categories of training (70%) and validation (30%). The receiver operating characteristics (ROC) were
used to evaluate the performance of the models. The results of evaluation models showed that using
hyperparameters random search and Bayesian optimization were improved SVM accuracy in training
and validation stages. Bayesian optimization methods are very efficient because they are consciously
choosing the parameters of the model that this strategy improves the performance of the model.
Evaluating accuracy in the validation stage showed that the AUC value is for MARS, SVM, RS-SVM and
B-SVM models 87.40%, 88.25%, 90.73% and 91.73%, respectively. The results of assessment variables
importance showed elevation, precipitation in the coldest month, soil and slope variables have the
most importance in modeling groundwater potential, while aspect, profile curvature and TWI variables,
have the least importance in predicting groundwater potential in Markazi province."
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Keywords
"Groundwater potential Markazi province Support vector machine Hyperparameters Random search Bayesian optimization"