Modeling Dissolved Oxygen Under Data Scarcity Situation Using Time-Series Generative Adversarial Network Combined with Long Short-Term Memory Network DOI
Gang Li, Cheng Chen, Siyang Yao

et al.

Published: Jan. 1, 2024

Language: Английский

Long-term prediction of Poyang Lake water level by combining multi-scale isometric convolution network with quantile regression DOI
Ying Jian, Yong Zheng,

Gang Li

et al.

Journal of Hydrology Regional Studies, Journal Year: 2025, Volume and Issue: 59, P. 102365 - 102365

Published: April 17, 2025

Language: Английский

Citations

0

Studying Kangen Technology Dissemination in Indian Subcontinent: A Mathematical Modeling Framework DOI
Saurabh Pandey, Deepti Aggrawal

International Journal of Mathematical Engineering and Management Sciences, Journal Year: 2025, Volume and Issue: 10(4), P. 856 - 873

Published: April 18, 2025

Much research has been done on the character, types, and nature of adopters present in market, wherein discussion revolved around adoption process entirety, but less attention given to characterizing types these market. By integrating principles from seminal models such as Rogers' Diffusion Innovations Bass Model, work helps identification predicting trends that delves into diffusion dynamics Kangen, a technology which is notable innovation water purification ionization industries provides better hydration, helping with energy focus. Critical insights Kangen dissemination across India are offered by results applying Innovation Modeling Framework. The highlight not only rate extent also shed light profiles different categories imitators within

Language: Английский

Citations

0

Relative humidity quantification using interpretable machine learning based- stacking approach: representative case study in Ethiopia DOI
Gebre Gelete, Tesfalem Abraham, Zaher Mundher Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

et al.

Theoretical and Applied Climatology, Journal Year: 2025, Volume and Issue: 156(6)

Published: May 9, 2025

Language: Английский

Citations

0

AS-SOMTF: A novel Multi-task Learning Model for Water Level Prediction by Satellite Remoting DOI Creative Commons
Xin Su, Zhi Zhen Qin,

Weikang Feng

et al.

Digital Communications and Networks, Journal Year: 2025, Volume and Issue: unknown

Published: May 1, 2025

Language: Английский

Citations

0

Dynamic quantitative assessment of multiple uncertainty sources in future hydropower generation prediction of cascade reservoirs with hydrological variations DOI Creative Commons
Shuai Zhou, Yimin Wang, Hui Su

et al.

Energy, Journal Year: 2024, Volume and Issue: 299, P. 131447 - 131447

Published: May 2, 2024

Language: Английский

Citations

2

Flood Propagation Characteristics in a Plain Lake: The Role of Multiple River Interactions DOI Open Access

Qiuqin Wu,

Zhichao Wang,

Xinfa Xu

et al.

Published: April 15, 2024

Plain lakes play a crucial role in the hydrological cycle of watershed, but their interactions with adjacent rivers and downstream water bodies can create complex river-lake relationships, often leading to frequent flooding disasters. Taking Poyang Lake as an example, this paper delves into its interaction Yangtze River, revealing spatiotemporal patterns flood propagation within lake impact on surrounding control measures. The aim is provide insights for management similar environments worldwide. This study employs comprehensive approach combining statistical analysis two-dimensional hydrodynamic modeling, based extensive hydrological, topographic, socio-economic data. results indicate that annual maximum outflow from primarily controlled by floods while highest level predominantly influenced River. peak discharge typically reaches outlet 48 hours, taking slightly longer at 54 hours. However, storage shorten time arrive. When converging may be delayed up 10 days due top-supporting interaction. Furthermore, "Five Rivers" propagate differently lake, affecting various regions differing degrees. Notably, Fu River cause most significant rise under same flow rate. effect also significantly impacts surface slope Lake. far exceeds (i.e., when intensity f value 4), becomes flat reservoir. During major difference increase dramatically, potentially creating "dynamic capacity" 840 million cubic meters.

Language: Английский

Citations

1

Flood prediction with optimized gated recurrent unit-temporal convolutional network and improved KDE error estimation DOI Creative Commons
Chenmin Ni, Muhammad Fadhil Marsani, Fam Pei Shan

et al.

AIMS Mathematics, Journal Year: 2024, Volume and Issue: 9(6), P. 14681 - 14696

Published: Jan. 1, 2024

<abstract> <p>Flood time series forecasting stands a critical challenge in precise predictive models and reliable error estimation methods. A novel approach utilizing hybrid deep learning model for both point interval flood prediction is presented, enhanced by improved kernel density (KDE) comparison simulation. Firstly, an optimized gated recurrent unit-time convolutional network (GRU-TCN) constructed tuning the internal structure of TCN, activation function, L2 regularization, optimizer. Then, Pearson Correlation used feature selection, hyperparameters GRU-TCN are subtraction-average-based optimizer (SABO). To further assess uncertainty, predictions provided via Non-parametric KDE, with bandwidth setting accurate distribution Experimental comparisons made on 5-year hydro-meteorological daily data from two stations along Yangtze River. The proposed surpasses long short-term memory (LSTM), GRU, TCN-LSTM, GRU-TCN, reduction more than 13% root mean square (RMSE) approximately 15% absolute (MAE), resulting better control. curves errors closer to value confidence intervals, reflecting trend distribution. This research enhances accuracy reliability improves capacity humans cope climate environmental changes.</p> </abstract>

Language: Английский

Citations

1

Flood Propagation Characteristics in a Plain Lake: The Role of Multiple River Interactions DOI Open Access

Qiuqin Wu,

Zhichao Wang,

Xinfa Xu

et al.

Water, Journal Year: 2024, Volume and Issue: 16(10), P. 1447 - 1447

Published: May 19, 2024

Plain lakes play a crucial role in the hydrological cycle of watershed, but their interactions with adjacent rivers and downstream water bodies can create complex river–lake relationships, often leading to frequent flooding disasters. Taking Poyang Lake as an example, this paper delves into its interaction Yangtze River, revealing spatiotemporal patterns flood propagation within lake impact on surrounding control measures. The aim is provide insights for management similar environments worldwide. This study employs comprehensive approach combining statistical analysis two-dimensional hydrodynamic modeling, based extensive hydrological, topographic, socio-economic data. results indicate that annual maximum outflow from primarily controlled by floods while highest level predominantly influenced River. peak discharge typically reaches outlet 48 h, taking slightly longer at 54 h. However, storage shorten time it takes arrive. When converging may be delayed up 10 days, due top-supporting interaction. Furthermore, “Five Rivers” propagate differently lake, affecting various regions differing degrees. Notably, Fu River cause most significant rise lake’s under same flow rate. effect also significantly impacts surface slope Lake. exceeds (i.e., when intensity value, f, four), becomes flat reservoir. During major difference increase dramatically, potentially creating “dynamic capacity” 840 million cubic meters.

Language: Английский

Citations

1

Design and Implementation of a Deep Learning Model and Stochastic Model for the Forecasting of the Monthly Lake Water Level DOI Creative Commons

Waleed Al-Nuaami,

Lamiaa Abdul-Jabbar Dawod,

Golam Kibria

et al.

Limnological Review, Journal Year: 2024, Volume and Issue: 24(3), P. 217 - 234

Published: July 10, 2024

Freshwater is becoming increasingly vulnerable to pollution due both climate change and an escalation in water consumption. The management of resource systems relies heavily on accurately predicting fluctuations lake levels. In this study, artificial neural network (ANN), a deep learning (DL) model, autoregressive integrated moving average (ARIMA) model were employed for the level forecasting St. Clair Ontario Lakes from 1981 2021. To develop models, we utilized mutual information incorporated lag periods up 6 months identify optimal inputs assessment lakes. results compared terms root mean square error (RMSE), coefficient correlation (r), absolute percentage (MAPE) graphical criteria. Upon evaluating results, it was observed that values models insignificant at designated stations: Lake Clair—0.16606 m < RMSE 1.0467 Ontario—0.0211 0.7436 m. developed increased accuracy by 5% 3.5% Ontario, respectively. Moreover, violin plot each most similar data. Hence, outperformed ANN ARIMA lake.

Language: Английский

Citations

1

Short-term prediction of water level based on deep learning in the downstream area of the Three Gorges Reservoir DOI

Xianghu Mao,

Biao Xiong, Xin Luo

et al.

Natural Hazards, Journal Year: 2024, Volume and Issue: unknown

Published: July 13, 2024

Language: Английский

Citations

1