Water Flow Forecasting Based on River Tributaries Using Long Short-Term Memory Ensemble Model

Silva, Diogo F. Costa; Filho, Arlindo Rodrigues Galvão; Carvalho, Rafael Viana de; Ribeiro, Filipe de Souza Lima; Coelho, Clarimar José

doi:10.3390/en14227707

Cited by 10 publications

(2 citation statements)

References 41 publications

(39 reference statements)

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“…focusing on periods characterized by potential rainfall scarcity and low flows (Silva et al, 2021). It takes into account previous precipitation and flow data as input.…”

Section: Flow Estimation and River Salinitymentioning

confidence: 99%

Decision-making system for rice production: a case study in the Araranguá River Valley

Giassi,

Morales,

D’aquino

et al. 2024

OLEL

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This paper addresses the challenges faced by small-scale rice producers in Santa Catarina, including water consumption, salinity issues, and high production costs. To support these producers, a computer-assisted decision-making system is proposed to enhance the production process. The system utilizes a computational model and simulations based on real crop data for validation. The contributions of this study include optimizing water and electricity usage, minimizing losses from excess irrigation and saline water collection. This is achieved through a system that forecasts future flow, determines the optimal timing and amount of irrigation, and evaluates water availability in the river to mitigate salinity risks caused by low rainfall.

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“…focusing on periods characterized by potential rainfall scarcity and low flows (Silva et al, 2021). It takes into account previous precipitation and flow data as input.…”

Section: Flow Estimation and River Salinitymentioning

confidence: 99%

Decision-making system for rice production: a case study in the Araranguá River Valley

Giassi,

Morales,

D’aquino

et al. 2024

OLEL

View full text Add to dashboard Cite

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“…In addition, more advanced models have been developed that incorporate upstream data to enhance stream ow prediction. These models may either merely forecast river ow from different study areas (Costa Silva et al 2021) or can also predict ow while including precipitation (Ding et This nding highlights the LSTM's effectiveness in accurately predicting ood events, especially in capturing the magnitude of peak ows. In order to predict runoff for the Lech and Danube Rivers con uence, (Ding et al 2019) developed an advanced LSTM-based hydrological forecast model, which incorporated ECMWF projections of multi-variables (precipitation, soil moisture, etc.).…”

Section: Introductionmentioning

confidence: 99%

Streamflow prediction using Long Short-term Memory networks

Nguyen,

Kha,

Ninh

et al. 2023

Preprint

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Accurate river streamflow prediction is crucial for hydropower operations, agricultural planning, and effective water resources management. However, forecasting reliable streamflow poses challenges due to the intricate nature of weather patterns and non-linear runoff generation mechanisms. The long short-term memory (LSTM) network has gained prominence for effectively simulating non-linear patterns. Despite its popularity, the performance of LSTM in river flow prediction remains insufficiently understood. This study assesses LSTM's effectiveness and explores how different network structures and hyperparameters impact short-term daily streamflow prediction at Kratie stations, a vital hydrological site in the Vietnam Mekong Delta. Training LSTM on historical streamflow data, we find that the size of the training dataset significantly influences network training, recommending a dataset spanning 2013 to 2022 for optimal results. Incorporating a hidden layer with a non-linear activation function enhances learning efficiency, and adding a fully connected layer slightly improves prediction ability. Careful tuning of parameters such as epochs, dropout, and the number of LSTM units enhances predictive accuracy. The stacked LSTM with sigmoid activation stands out, demonstrating excellent performance with a high Nash–Sutcliffe Efficiency (NSE) of 0.95 and a low root relative mean square error (rRMSE) of approximately 0.002%. Moreover, the model excels in forecasting streamflow for 5 to 15 antecedent days, with five days exhibiting particularly high accuracy.

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Exploring the applicability of the experiment-based ANN and LSTM models for streamflow estimation

Akiner,

Kartal,

Guzeler

et al. 2024

Earth Sci Inform

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The Yeşilırmak River Basin in northern Türkiye is crucial for the region’s water supply, agriculture, hydroelectric power generation, and clean drinking water. The primary goal of this study is to determine which modeling approach is most appropriate for various locations within the basin and how well meteorological data can predict river flow rates. Hydrological and meteorological forecasting both depend on the prediction of river flow rates. An artificial neural network (ANN), Univariate and Multivariate Long Short-Term Memory (LSTM) models have been utilized for streamflow forecasting. This research aims to determine the best model for several provinces in the basin area and give decision-makers a tool for reliable river flow rate estimates by combining LSTM and ANN models. According to research findings, the supervised multivariate LSTM model performed better than the unsupervised model in accuracy and precision. The sliding window methodology is suitable for estimating river flow based on meteorological datasets because it offers a primary method for reinterpreting time-series data in a supervised learning style. Compared to LSTM models, the ANN model that has been statistically optimized through experiments (DoE) design performs better in forecasting the river flow rate in the Yeşilırmak River basin (R2 = 0.98, RMSE = 0.18). The study’s findings provided prospective cognitive models for the strategic management of water resources by forecasting future data from flow monitoring stations.

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Water Flow Forecasting Based on River Tributaries Using Long Short-Term Memory Ensemble Model

Cited by 10 publications

References 41 publications

Decision-making system for rice production: a case study in the Araranguá River Valley

Decision-making system for rice production: a case study in the Araranguá River Valley

Streamflow prediction using Long Short-term Memory networks

Exploring the applicability of the experiment-based ANN and LSTM models for streamflow estimation

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