An advanced hybrid deep learning model for predicting total dissolved solids and electrical conductivity (EC) in coastal aquifers
For more than one billion people living in coastal regions, coastal aquifers provide a water resource. In coastal regions, monitoring water quality is an important issue for policymakers. Many studies mentioned that most of the conventional models were not accurate for predicting total dissolved sol...
Saved in:
Main Authors: | Jamshidzadeh, Zahra, Latif, Sarmad Dashti, Ehteram, Mohammad, Khozani, Zohreh Sheikh, Ahmed, Ali Najah, Sherif, Mohsen, El-Shafie, Ahmed |
---|---|
Format: | Article |
Published: |
Springer
2024
|
Subjects: | |
Online Access: | http://eprints.um.edu.my/45726/ https://doi.org/10.1186/s12302-024-00850-8 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Graph convolutional network-Long short term memory neural network- multi layer perceptron- Gaussian progress regression model: A new deep learning model for predicting ozone concertation
by: Ehteram, Mohammad, et al.
Published: (2023) -
Reservoir water balance simulation model utilizing machine learning algorithm
by: Latif, Sarmad Dashti, et al.
Published: (2021) -
Rainfall prediction using multiple inclusive models and large climate indices
by: Mohamadi, Sedigheh, et al.
Published: (2022) -
Characterization of groundwater quality in the coastal aquifer of Terengganu, Malaysia
by: Hamzah, Zahidi
Published: (2017) -
Estimation of total dissolved solids (TDS) using new hybrid machine learning models
by: Banadkooki, Fatemeh Barzegari, et al.
Published: (2020)