Developing Demand Forecasting Models for E-Commerce: Analyzing the Impact of Time Lags on Model Performance DOI
Alim Toprak Fırat,

Onur Aygün,

Mustafa Göğebakan

et al.

Scientific journal of Mehmet Akif Ersoy University., Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 24, 2024

Time series are an important analytical tool used in many problems today. Particularly favored regression such as demand forecasting, time enable more accurate modeling of the impact past data on future values through various lag options. is a method analysis or machine learning models to examine effect (lagged) variable current values. options play crucial role, particularly success forecasts. This study aims develop forecasting that help e-commerce businesses gain competitive advantage by accurately predicting and comprehensively analyzing delay performance. In this context, interface with hyperparametric flexibility has been developed, effects "Use Best N," Correlation," All Delays," "Selected Delay Lag" performance have analyzed using models. Models created for two different months three products. The developed evaluated Mean Absolute Percentage Error (MAPE) metric. lowest MAPE value July obtained MQRNN model product A, while August MLP B.

Language: Английский

Enhanced streamflow forecasting using hybrid modelling integrating glacio-hydrological outputs, deep learning and wavelet transformation DOI Creative Commons
Jamal Hassan Ougahi, John S. Rowan

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

Published: Jan. 22, 2025

Abstract Understanding snow and ice melt dynamics is vital for flood risk assessment effective water resource management in populated river basins sourced inaccessible high-mountains. This study provides an AI-enabled hybrid approach integrating glacio-hydrological model outputs (GSM-SOCONT), with different machine learning deep techniques framed as alternative ‘computational scenarios, leveraging both physical processes data-driven insights enhanced predictive capabilities. The standalone (CNN-LSTM), relying solely on meteorological data, outperformed its counterpart equivalents. Hybrid models (CNN-LSTM1 to CNN-LSTM15) were trained using data augmented representing snow-melt contributions streamflow. (CNN-LSTM14), only glacier-derived features, performed best high NSE (0.86), KGE (0.80), R (0.93) values during calibration, the highest (0.83), (0.88), (0.91), lowest RMSE (892) MAE (544) validation. Finally, a multi-scale analysis feature permutations was explored wavelet transformation theory, these into final (CNN-LSTM19), which significantly enhances accuracy, particularly high-flow events, evidenced by improved (from 0.83 0.97) reduced 892 442) comparative illustrates how AI-enhanced hydrological improve accuracy of runoff forecasting provide more reliable actionable managing resources mitigating risks - despite paucity direct measurements.

Language: Английский

Citations

3
Fine-tuning long short-term memory models for seamless transition in hydrological modelling: From pre-training to post-application DOI Creative Commons
Xueying Chen, Yuhang Zhang, Aizhong Ye

et al.

Environmental Modelling & Software, Journal Year: 2025, Volume and Issue: unknown, P. 106350 - 106350

Published: Jan. 1, 2025

Language: Английский

Citations

0
A generalised hydrological model for streamflow prediction using wavelet Ensembling DOI
Chinmaya Panda, Kanhu Charan Panda,

Ram Mandir Singh

et al.

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 132883 - 132883

Published: Feb. 1, 2025

Language: Английский

Citations

0
A machine learning model integrating spatiotemporal attention and residual learning for predicting periodic air pollutant concentrations DOI
Farun An,

Dong Yang,

Xiaoyue Sun

et al.

Environmental Modelling & Software, Journal Year: 2025, Volume and Issue: unknown, P. 106438 - 106438

Published: March 1, 2025

Language: Английский

Citations

0
The enhanced integration of proven techniques to quantify the uncertainty of forecasting extreme flood events based on numerical weather prediction models DOI Creative Commons
Mitra Tanhapour, Jaber Soltani, Hadi Shakibian

et al.

Weather and Climate Extremes, Journal Year: 2025, Volume and Issue: unknown, P. 100767 - 100767

Published: April 1, 2025

Language: Английский

Citations

0
Real-time Rainfall Estimation Using Deep Learning: Influence of Background and Rainfall Intensity DOI

Xiaodong Qin,

Qian Zhu,

Junran Shen

et al.

Environmental Modelling & Software, Journal Year: 2025, Volume and Issue: unknown, P. 106496 - 106496

Published: April 1, 2025

Language: Английский

Citations

0
Variable sensitivity analysis in groundwater level projections under climate change adopting a hybrid machine learning algorithm DOI
Ali Sharghi, Mehdi Komasi, Masoud Ahmadi

et al.

Environmental Modelling & Software, Journal Year: 2024, Volume and Issue: 183, P. 106264 - 106264

Published: Nov. 13, 2024

Language: Английский

Citations

1
Nitrogen nutritional diagnosis of summer maize (Zea mays L.) based on a hyperspectral data collaborative approach-evaluation of the estimation potential of three-dimensional spectral indices DOI
Zijun Tang, Yaohui Cai,

Youzhen Xiang

et al.

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 229, P. 109713 - 109713

Published: Dec. 10, 2024

Language: Английский

Citations

1
Enhancing Streamflow Forecasting in Glacierized Basins: A Hybrid Model Integrating Glacio-Hydrological Outputs, Deep Learning, and Wavelet Transformation DOI Creative Commons
Jamal Hassan Ougahi, John S. Rowan

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 15, 2024

Abstract Understanding snow and ice melt dynamics is vital for flood risk assessment effective water resource management in highly populated river basins rising inaccessible high-mountains. This study evaluated AI-enhanced hydrological modelling using a hybrid approach integrating glacio-hydrological model (GSM-SOCONT), with advanced machine learning deep techniques framed as alternative ‘scenarios’, leveraging both physical processes data-driven insights enhanced predictive capabilities. The standalone (CNN-LSTM), relying solely on meteorological data, outperformed the model. Additionally, series of models (CNN-LSTM1 to CNN-LSTM15) were trained data along three additional feature groups derived from outputs, providing detailed into streamflow simulation. (CNN-LSTM14), which relied glacier-derived features, demonstrated best performance high NSE (0.86), KGE (0.80), R (0.93) values during calibration, highest (0.83), (0.88), (0.91), lowest RMSE (892) MAE (544) validation. Furthermore, proposed hybridization framework involves applying permutation importance identify key wavelet transform decompose them multi-scale analysis, these (CNN-LSTM19), significantly enhances accuracy, particularly high-flow events, evidenced by improved (from 0.83 0.97) reduced 892 442) comparative analysis illustrates how improve accuracy runoff forecasting provide more reliable actionable managing resources mitigating risks - despite relative paucity direct measurements.

Language: Английский

Citations

0
Developing Demand Forecasting Models for E-Commerce: Analyzing the Impact of Time Lags on Model Performance DOI
Alim Toprak Fırat,

Onur Aygün,

Mustafa Göğebakan

et al.

Scientific journal of Mehmet Akif Ersoy University., Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 24, 2024

Time series are an important analytical tool used in many problems today. Particularly favored regression such as demand forecasting, time enable more accurate modeling of the impact past data on future values through various lag options. is a method analysis or machine learning models to examine effect (lagged) variable current values. options play crucial role, particularly success forecasts. This study aims develop forecasting that help e-commerce businesses gain competitive advantage by accurately predicting and comprehensively analyzing delay performance. In this context, interface with hyperparametric flexibility has been developed, effects "Use Best N," Correlation," All Delays," "Selected Delay Lag" performance have analyzed using models. Models created for two different months three products. The developed evaluated Mean Absolute Percentage Error (MAPE) metric. lowest MAPE value July obtained MQRNN model product A, while August MLP B.

Language: Английский

Citations

0