Predictive Modeling of Water Table Depth, Drilling Duration, and Soil Layer Classification Using Adaptive Ensemble Learning for Cost-Effective Percussion Water Borehole Drilling-Reference-Cited by-同舟云学术

Predictive Modeling of Water Table Depth, Drilling Duration, and Soil Layer Classification Using Adaptive Ensemble Learning for Cost-Effective Percussion Water Borehole Drilling

Published:2023 Issue: Volume:11 Page:76703-76721
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Khan Qazi Waqas¹^ORCID,Kim Bong Wan²^ORCID,Ahmed Rashid³^ORCID,Rizwan Atif¹^ORCID,Khan Anam Nawaz¹^ORCID,Kim Kwangsoo²,Kim Do-Hyeun¹^ORCID

Affiliation:

1. Department of Computer Engineering, Jeju National University, Jejusi, Republic of Korea

2. Electronics and Telecommunications Research Institute (ETRI), Daejeon, Republic of Korea

3. Department of Computer Science, COMSATS University Islamabad, Attock Campus, Attock, Pakistan

Funder

Energy Cloud R&D Program through the National Research Foundation of Korea

Ministry of Science, ICT

Korea Agency for Infrastructure Technology Advancement

Ministry of Land, Infrastructure and Transport

Brain Pool program

Ministry of Science and ICT through the National Research Foundation of Korea

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10190628.pdf?arnumber=10190628

Reference57 articles.

1. Classification Based on Decision Tree Algorithm for Machine Learning

2. Modelling groundwater level variations by learning from multiple models using fuzzy logic

3. Understanding the role of individual units in a deep neural network

4. Using artificial neural network models for groundwater level forecasting and assessment of the relative impacts of influencing factors

5. Developing a Long Short-Term Memory (LSTM) based model for predicting water table depth in agricultural areas

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