A novel additive regression model for streamflow forecasting in German rivers

Results in Engineering, Год журнала: 2024, Номер 22, С. 102104 - 102104

Опубликована: Апрель 10, 2024

Forecasting streamflows, essential for flood mitigation and the efficient management of water resources drinking, agriculture hydroelectric power generation, presents a formidable challenge in most real-world scenarios. In this study, two models, first based on Additive Regression Radial Basis Function Neural Networks (AR-RBF) second stacking with Pace Multilayer Perceptron Random Forest (MLP-RF-PR), were compared prediction short-term (1–3 days ahead) medium-term (7 daily streamflow rates three different rivers Germany: Elbe River at Wittenberge, Leine Herrenhausen, Saale Hof The lagged values rate, precipitation temperature considered modeling. Moreover, Bayesian Optimization (BO) algorithm was used to assess optimal number hyperparameters. Both models showed accurate predictions forecasting, R2 1-day ahead ranging from 0.939 0.998 AR-RBF 0.930 0.996 MLP-RF-PR, while MAPE ranged 2.02 % 8.99 2.14 9.68 when exogeneous variables included. As forecast horizon increased, reduction forecasting accuracy observed. However, both could still predict overall flow pattern, even 7-day-ahead predictions, 0.772 0.871 0.703 0.840 10.60 20.45 10.44 19.65 MLP-RF-PR. Overall, outcomes study suggest that MLP-RF-PR can be reliable tools short- rate prediction, requiring short parameters optimized, making them easy implement reducing calculation time required.

Язык: Английский

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Development and optimization of geopolymer concrete with compressive strength prediction using particle swarm-optimized extreme gradient boosting

Applied Soft Computing, Год журнала: 2025, Номер unknown, С. 113149 - 113149

Опубликована: Апрель 1, 2025

Язык: Английский

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Revolutionizing the Future of Hydrological Science: Impact of Machine Learning and Deep Learning amidst Emerging Explainable AI and Transfer Learning

Applied Computing and Geosciences, Год журнала: 2024, Номер 24, С. 100206 - 100206

Опубликована: Ноя. 9, 2024

Язык: Английский

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Enhancing hydrological time series forecasting with a hybrid Bayesian-ConvLSTM model optimized by particle swarm optimization

Acta Geophysica, Год журнала: 2025, Номер unknown

Опубликована: Март 24, 2025

Язык: Английский

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Improved streamflow prediction accuracy in Boreal climate watershed using a LSTM model: A comparative study

PLOS Water, Год журнала: 2025, Номер 4(4), С. e0000359 - e0000359

Опубликована: Апрель 21, 2025

Streamflow plays a vital role in water resource management and environmental impact assessment. This study is novel application of the Long Short-Term Memory (LSTM) model, type recurrent neural network, for real-time streamflow prediction Upper Humber River Watershed western Newfoundland. It also compares performance LSTM model with physically based SWAT model. The was optimized by tuning hyperparameters adjusting window size to balance capturing historical data ensuring stability. Using single input variables such as daily average temperature or precipitation, achieved high Nash-Sutcliffe Efficiency (NSE) 0.95. In comparison, results show that delivers more competitive performance, achieving an NSE 0.95 versus SWAT’s 0.77, percent bias (PBIAS) 0.62 compared 8.26. Unlike SWAT, does not overestimate flows excels predicting low flows. Additionally, successfully predicted using data. Despite challenges interpretability generalizability, demonstrated strong particularly during extreme events, making it valuable tool cold climates where accurate forecasts are crucial effective management. highlights potential model’s

Язык: Английский

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Evolution of ensemble machine learning approaches in water resources management: a review

Earth Science Informatics, Год журнала: 2025, Номер 18(2)

Опубликована: Май 24, 2025

Язык: Английский

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A hybrid model of ARIMA and MLP with a Grasshopper optimization algorithm for time series forecasting of water quality

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Окт. 13, 2024

Water quality monitoring of rivers is necessary in order to properly manage their basins so that steps can be taken control the amount pollutants and bring them allowable level. The ARIMA (autoregressive integrated moving average) model does not consider nonlinear patterns modeling water components. Also, using MLP (Multilayer Perceptrons) model, both linear pattern are controlled equally. Therefore, present study, time series models (ARIMA), a hybrid optimized by Grasshopper optimization algorithm used predict components statistical period 2011–2019. In proposed method, ability exploited. Observational data for forecasting method include dissolved oxygen, temperature, boron over 108 months. Since, capable realizing essence complicated series, it makes more reliable forecasts. correlation coefficients between observational predicted values 0.9 0.91 boron. To compare three ARIMA, MLP, models, accuracy indices each calculated. results show model's higher compared with other two models.

Язык: Английский

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Ensemble deep learning techniques for time series analysis: a comprehensive review, applications, open issues, challenges, and future directions

Cluster Computing, Год журнала: 2024, Номер 28(1)

Опубликована: Ноя. 8, 2024

Язык: Английский

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Applying Data-Driven Modeling for Streamflow Prediction in Semi-Arid Watersheds: A Comparative Evaluation of Machine Learning and Deep Learning Methodologies

Pure and Applied Geophysics, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 13, 2024

Язык: Английский

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