Long-term prediction of Poyang Lake water level by combining multi-scale isometric convolution network with quantile regression

Journal of Hydrology Regional Studies, Год журнала: 2025, Номер 59, С. 102365 - 102365

Опубликована: Апрель 17, 2025

Язык: Английский

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Studying Kangen Technology Dissemination in Indian Subcontinent: A Mathematical Modeling Framework

International Journal of Mathematical Engineering and Management Sciences, Год журнала: 2025, Номер 10(4), С. 856 - 873

Опубликована: Апрель 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

Язык: Английский

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Relative humidity quantification using interpretable machine learning based- stacking approach: representative case study in Ethiopia

Theoretical and Applied Climatology, Год журнала: 2025, Номер 156(6)

Опубликована: Май 9, 2025

Язык: Английский

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AS-SOMTF: A novel Multi-task Learning Model for Water Level Prediction by Satellite Remoting

Digital Communications and Networks, Год журнала: 2025, Номер unknown

Опубликована: Май 1, 2025

Язык: Английский

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Dynamic quantitative assessment of multiple uncertainty sources in future hydropower generation prediction of cascade reservoirs with hydrological variations

Energy, Год журнала: 2024, Номер 299, С. 131447 - 131447

Опубликована: Май 2, 2024

Язык: Английский

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

Опубликована: Апрель 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.

Язык: Английский

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Flood prediction with optimized gated recurrent unit-temporal convolutional network and improved KDE error estimation

AIMS Mathematics, Год журнала: 2024, Номер 9(6), С. 14681 - 14696

Опубликована: Янв. 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>

Язык: Английский

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

Water, Год журнала: 2024, Номер 16(10), С. 1447 - 1447

Опубликована: Май 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.

Язык: Английский

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

Limnological Review, Год журнала: 2024, Номер 24(3), С. 217 - 234

Опубликована: Июль 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.

Язык: Английский

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

Natural Hazards, Год журнала: 2024, Номер unknown

Опубликована: Июль 13, 2024

Язык: Английский

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