Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model

By Rahim Barzegar, Mohammad Taghi Aalami, Jan Adamowski. Stochastic environmental research and risk assessment (Print), 2020

478 citations recorded by the source corpus.

Subfields: Environmental Engineering, Industrial and Manufacturing Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/3f71ad1e460b10a9da3fe6e31b2ba46008d5882b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Environmental Engineering

1. Muhammad Mohsin Waqas
2. Usa Wannasingha Humphries

In Industrial and Manufacturing Engineering

1. Muhammad Mohsin Waqas
2. Usa Wannasingha Humphries

In Water Science and Technology

1. Muhammad Mohsin Waqas
2. Usa Wannasingha Humphries

Citations in the corpus

• A critical review of RNN and LSTM variants in hydrological time series predictions (2024, methodology): “Their findings demonstrated that the combined CNN-LSTM model outperformed standalone machine learning (ML) models such as CNN, LSTM, SVR, and decision trees (DTs) [ 53 ].”