☆ 4.6 Article

Assessing the Performance of Deep Learning Algorithms for Short-Term Surface Water Quality Prediction

SUSTAINABILITY (2021)

相关参考文献

注意：仅列出部分参考文献，下载原文获取全部文献信息。

Article Environmental Sciences

Water quality assessment of a river using deep learning Bi-LSTM methodology: forecasting and validation

Sakshi Khullar et al.

Summary: Water quality of rivers has been adversely affected by harmful wastes and pollutants, where machine learning techniques show potential in improving water quality modelling. Introducing a deep learning-based Bi-LSTM model can enhance the accuracy of water quality factor predictions, outperforming other advanced techniques in terms of accuracy and error rates based on experimental results.

ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH (2022)

添加到收藏夹

Article Engineering, Environmental

From Hydrometeorology to River Water Quality: Can a Deep Learning Model Predict Dissolved Oxygen at the Continental Scale?

Wei Zhi et al.

Summary: The study used an LSTM model to analyze DO and hydrometeorology data, finding that the model could learn the theory of DO solubility, capture the trend of DO decreasing with water temperature, but perform poorly when in-stream biogeochemical processes are enhanced. The results suggest the need for increased data collection at DO peaks and troughs, as well as in sparsely monitored areas, to overcome the challenge of data scarcity in the water quality community.

ENVIRONMENTAL SCIENCE & TECHNOLOGY (2021)

添加到收藏夹

Article Environmental Sciences

Deep Learning Framework with Time Series Analysis Methods for Runoff Prediction

Zhenghe Li et al.

Summary: The study combined deep learning with time series analysis methods for daily runoff prediction in the middle Yangtze River, finding that the proposed model demonstrated advantages in improving accuracy, simplicity, and effectiveness.

WATER (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Reliability assessment of water quality index based on guidelines of national sanitation foundation in natural streams: integration of remote sensing and data-driven models

Mohammad Najafzadeh et al.

Summary: The study employed four different Data-Driven Models to predict the Water Quality Index (WQI) for Karun River, with the FS-M5 MT model demonstrating the best performance in WQI classification estimation. According to the reliability analysis of WQI, there is only a 19% chance for a specimen from Karun River to have a better quality index.

ARTIFICIAL INTELLIGENCE REVIEW (2021)

添加到收藏夹

Article Engineering, Civil

The performance of classification and forecasting Dong Nai River water quality for sustainable water resources management using neural network techniques

Nguyen Hien Than et al.

Summary: This study classified and predicted water quality for the Dong Nai river using neural network models and different time series. The LSTM-MA hybrid model showed high accuracy and reliability in forecasting future water quality.

JOURNAL OF HYDROLOGY (2021)

添加到收藏夹

Article Environmental Sciences

Water Quality Prediction in the Luan River Based on 1-DRCNN and BiGRU Hybrid Neural Network Model

Jianzhuo Yan et al.

Summary: The study proposed a new comprehensive deep learning water quality prediction algorithm, which achieved effective prediction of water quality parameters through data processing and model integration. Experimental results on actual water quality data demonstrated that the method has higher prediction accuracy and generalization ability, outperforming existing LSTM, GRU, and BiGRU models.

WATER (2021)

添加到收藏夹

Article Environmental Sciences

Comparison of Forecasting Models for Real-Time Monitoring of Water Quality Parameters Based on Hybrid Deep Learning Neural Networks

Jian Sha et al.

Summary: This study compared the performances of different deep learning models in real-time water quality prediction, with results showing that CNN-LSTM performed the best, CEEMDAN-based input data outperformed the original input data, especially for non-periodic parameters such as TN.

WATER (2021)

添加到收藏夹

Article Geosciences, Multidisciplinary

A Novel Multiple-Kernel Support Vector Regression Algorithm for Estimation of Water Quality Parameters

Mohammad Najafzadeh et al.

Summary: This paper introduces a novel Multiple-Kernel Support Vector Regression (MKSVR) algorithm for estimating hard-to-measure water quality parameters, utilizing the Particle Swarm Optimization (PSO) algorithm to solve the optimization problem. Results show that MKSVR provides a more accurate prediction compared to SVR and RFR algorithms.

NATURAL RESOURCES RESEARCH (2021)

添加到收藏夹

Article Computer Science, Information Systems

Evaluation of Deep Learning Models for Multi-Step Ahead Time Series Prediction

Rohitash Chandra et al.

Summary: This study evaluates the performance of deep learning models for multi-step ahead time series prediction, and finds that bidirectional and encoder-decoder LSTM networks show the best accuracy in given time series problems.

IEEE ACCESS (2021)

添加到收藏夹

Article Engineering, Environmental

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

Rahim Barzegar et al.

STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT (2020)

添加到收藏夹

Article Engineering, Civil

Receiving More Accurate Predictions for Longitudinal Dispersion Coefficients in Water Pipelines: Training Group Method of Data Handling Using Extreme Learning Machine Conceptions

Farid Sabed-Movahed et al.

WATER RESOURCES MANAGEMENT (2020)

添加到收藏夹

Article Geography, Physical

Deep learning-based water quality estimation and anomaly detection using Landsat-8/Sentinel-2 virtual constellation and cloud computing

Kyle T. Peterson et al.

GISCIENCE & REMOTE SENSING (2020)

添加到收藏夹

Article Environmental Sciences

A Prediction Model Based on Deep Belief Network and Least Squares SVR Applied to Cross-Section Water Quality

Jianzhuo Yan et al.

WATER (2020)

添加到收藏夹

Article Engineering, Civil

Real-time probabilistic forecasting of river water quality under data missing situation: Deep learning plus post-processing techniques

Yanlai Zhou

JOURNAL OF HYDROLOGY (2020)

添加到收藏夹

Article Environmental Sciences

Prediction of Water Level and Water Quality Using a CNN-LSTM Combined Deep Learning Approach

Sang-Soo Baek et al.

WATER (2020)

添加到收藏夹

Article Water Resources

Deep neural network analyses of water quality time series associated with water sensitive urban design (WSUD) features

Ho Huu Loc et al.

JOURNAL OF APPLIED WATER ENGINEERING AND RESEARCH (2020)

添加到收藏夹

Article Chemistry, Analytical

A Water Quality Prediction Method Based on the Deep LSTM Network Considering Correlation in Smart Mariculture

Zhuhua Hu et al.

SENSORS (2019)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Evaluation of conjugate depths of hydraulic jump in circular pipes using evolutionary computing

Mohammad Najafzadeh

SOFT COMPUTING (2019)

添加到收藏夹

Article Green & Sustainable Science & Technology

Analysis and Prediction of Water Quality Using LSTM Deep Neural Networks in IoT Environment

Ping Liu et al.

SUSTAINABILITY (2019)

添加到收藏夹

Article Environmental Sciences

Prediction of water quality parameters using evolutionary computing-based formulations

M. Najafzadeh et al.

INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY (2019)

添加到收藏夹

Article Engineering, Civil

Modelling daily dissolved oxygen concentration using least square support vector machine, multivariate adaptive regression splines and M5 model tree

Salim Heddam et al.

JOURNAL OF HYDROLOGY (2018)

添加到收藏夹

Article Environmental Sciences

Improved Prediction of Harmful Algal Blooms in Four Major South Korea's Rivers Using Deep Learning Models

Sangmok Lee et al.

INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH (2018)

添加到收藏夹

Article Environmental Sciences

Water Quality Prediction Method Based on IGRA and LSTM

Jian Zhou et al.

WATER (2018)

添加到收藏夹

Proceedings Paper Automation & Control Systems

Water Quality Prediction Model Combining Sparse Auto-encoder and LSTM Network

Zhenbo Li et al.

IFAC PAPERSONLINE (2018)

添加到收藏夹

Article Automation & Control Systems

A hybrid neural network and ARIMA model for water quality time series prediction

Durdu Oemer Faruk

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2010)

添加到收藏夹

© Peeref 2019-2024. All rights reserved.