Electrical Conductivity Estimation in the Medina River, Texas, USA: An Integrated Approach Using Wavelet Analysis and Machine Learning Techniques DOI Creative Commons

Salar Khani,

Neda Khademi Shiraz

IntechOpen eBooks, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 11, 2024

Electrical conductivity (EC) is an important indicator for monitoring water quality in riverine systems. EC inherently associated with the concentration of dissolved ionic compounds present aqueous environments, including various salts and minerals. estimations are crucial environmental overall health assessment aquatic ecosystems. The study investigated application discrete wavelet transform (DWT) conjunction artificial neural networks (ANNs) multiple linear regression (MLR) models to predict daily river EC. For this purpose, discharge (Q) time series from a hydrology station on Medina River San Antonio, Texas, USA, were used. DWT was used decompose data into several subseries. Then, estimate one-day-ahead values, these subseries introduced ANN MLR models. To assess prediction accuracy improved wavelet-neural network (WANN) wavelet-regression (WR) models, estimation also carried out using original data. Both WANN WR techniques outperformed single methods. A comparison results indicated that model had superior performance than WANN, MLR, prediction. R2 values WR, 0.92, 0.87, 0.74, respectively. model, root-mean-square error (RMSE) 45.55, 46.08, 25.19% less those presented by ANN, By method, accurate estimator formula obtained as well. satisfactorily simulated hysteresis EC, demonstrating effectiveness analysis extracting essential information embedded

Language: Английский

Performance evaluation of convolutional neural network and vision transformer models for groundwater potential mapping DOI
Behnam Sadeghi, Ali Asghar Alesheikh,

Ali Jafari

et al.

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 132840 - 132840

Published: Feb. 1, 2025

Language: Английский

Citations

0
A State-of-the-art Novel Approach to Predict Potato Crop Coefficient (Kc) by Integrating Advanced Machine Learning Tools DOI Creative Commons
Saad Javed Cheema, Masoud Karbasi,

Gurjit S. Randhawa

et al.

Smart Agricultural Technology, Journal Year: 2025, Volume and Issue: unknown, P. 100896 - 100896

Published: March 1, 2025

Language: Английский

Citations

0
Enhancing multi-temporal drought forecasting accuracy for Iran: Integrating an innovative hidden pattern identifier, recursive feature elimination, and explainable ensemble learning DOI
Mahnoosh Moghaddasi,

Mansour Moradi,

Mehdi Mohammadi Ghaleni

et al.

Journal of Hydrology Regional Studies, Journal Year: 2025, Volume and Issue: 59, P. 102382 - 102382

Published: April 17, 2025

Language: Английский

Citations

0
A hybrid TCN-XGBoost model for agricultural product market price forecasting DOI Creative Commons
Tianwen Zhao, Guoqing Chen, Sujitta Suraphee

et al.

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(5), P. e0322496 - e0322496

Published: May 2, 2025

Price volatility in agricultural markets is influenced by seasonality, supply-demand fluctuations, policy changes, and climate. These factors significantly impact production the broader macroeconomy. Traditional time series models, limited linear assumptions, often fail to capture nonlinear nature of price fluctuations. To address this limitation, we propose an integrated forecasting model that combines TCN XGBoost improve accuracy predictions. captures both short-term long-term dependencies using convolutional operations, while enhances its ability relationships. The uses 65,750 historical data points from rice, wheat, corn, with a sliding window technique construct features. Experimental results demonstrate TCN-XGBoost outperforms traditional models such as ARIMA (RMSE = 0.36, MAPE 8.9%) LSTM 0.34, 8.1%). It also other hybrid Transformer-XGBoost 0.23) CNN-XGBoost 0.29). Specifically, achieves RMSE 0.26 5.3%, underscoring superior performance. Moreover, shows robust performance across various market conditions, particularly during significant During dramatic movements, 0.28 6.1%, effectively capturing trends magnitudes changes. By leveraging TCN’s strength temporal feature extraction XGBoost’s capability complex relationships, offers efficient solution for prices. This has broad applicability, decision-making risk management.

Language: Английский

Citations

0
Intelligent fault diagnosis and operation condition monitoring of transformer based on multi-source data fusion and mining DOI Creative Commons

Jingping Cui,

Wei Kuang,

Kai Geng

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 4, 2025

Transformers are important equipment in the power system and their reliable safe operation is an guarantee for high-efficiency of system. In order to achieve prognostics health management transformer, a novel intelligent fault diagnosis transformer based on multi-source data fusion correlation analysis proposed. Firstly, multiple components dissolved gases performed by improved entropy weighting method. Then, combination bidirectional long short-term memory network, attention mechanism, convolution neural network employed predict load rate, upper oil temperature, winding temperature data, indices gas transformer. Furthermore, Apriori rate layer, support confidence levels predictive assessment state. Finally, validity algorithm verified applying actual from monitoring platform. The results show that vicinity sample point 88, gas, not within normal range intervals, it presumed arc discharge phenomenon. average correct 100 diagnoses model proposed this paper 0.917, mean square error 0.018. can prediction accident early warning, prevent further expansion accident.

Language: Английский

Citations

0
Exploration of the Impact Mechanism of Government Credibility Based on Variable Screening Method DOI Open Access

Jiajun Wu,

Yuxiang Ma,

Helin Zou

et al.

Journal of Data Analysis and Information Processing, Journal Year: 2024, Volume and Issue: 12(03), P. 479 - 494

Published: Jan. 1, 2024

Language: Английский

Citations

0
Behavioral Pattern Identification of E-commerce Consumers’ Purchase Intention in Big Data Environment DOI Creative Commons
Feng Gao, Caigen Peng

Applied Mathematics and Nonlinear Sciences, Journal Year: 2024, Volume and Issue: 9(1)

Published: Jan. 1, 2024

Abstract Predicting user purchase behavior using shopping history data on e-commerce platforms helps to improve experience and marketing effect. Our paper uses the time-sliding window method construct features that mine users’ interest preferences in different periods based real interaction records between users products scenarios. Then, a model for predicting CNN-LSTM is proposed. By automatically extracting selecting attributes, product behavioral features, used predict purchasing behavior. An online retail platform implements precision this model. The results show calculated values of effect Attention Stage, Interest Stage Active Participation are [0.8-1.0], Precision Marketing “Excellent”. value action stage repeat [0.6-0.8], “good”. After implementation marketing, operating income A increasing, while expense ratio remains stable. This paper’s can effectively consumers’ intention, as evidenced by its findings.

Language: Английский

Citations

0
Assessment of landscape diversity in Inner Mongolia and risk prediction using CNN-LSTM model DOI Creative Commons
Yalei Yang, Hong Wang, Xiaobing Li

et al.

Ecological Indicators, Journal Year: 2024, Volume and Issue: 169, P. 112940 - 112940

Published: Dec. 1, 2024

Language: Английский

Citations

0
Electrical Conductivity Estimation in the Medina River, Texas, USA: An Integrated Approach Using Wavelet Analysis and Machine Learning Techniques DOI Creative Commons

Salar Khani,

Neda Khademi Shiraz

IntechOpen eBooks, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 11, 2024

Electrical conductivity (EC) is an important indicator for monitoring water quality in riverine systems. EC inherently associated with the concentration of dissolved ionic compounds present aqueous environments, including various salts and minerals. estimations are crucial environmental overall health assessment aquatic ecosystems. The study investigated application discrete wavelet transform (DWT) conjunction artificial neural networks (ANNs) multiple linear regression (MLR) models to predict daily river EC. For this purpose, discharge (Q) time series from a hydrology station on Medina River San Antonio, Texas, USA, were used. DWT was used decompose data into several subseries. Then, estimate one-day-ahead values, these subseries introduced ANN MLR models. To assess prediction accuracy improved wavelet-neural network (WANN) wavelet-regression (WR) models, estimation also carried out using original data. Both WANN WR techniques outperformed single methods. A comparison results indicated that model had superior performance than WANN, MLR, prediction. R2 values WR, 0.92, 0.87, 0.74, respectively. model, root-mean-square error (RMSE) 45.55, 46.08, 25.19% less those presented by ANN, By method, accurate estimator formula obtained as well. satisfactorily simulated hysteresis EC, demonstrating effectiveness analysis extracting essential information embedded

Language: Английский

Citations

0