4.7 Article

Neuroforecasting of daily streamflows in the UK for short- and medium-term horizons: A novel insight

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Engineering, Environmental

Spatio-temporal analysis of drought in Southern Italy: a combined clustering-forecasting approach based on SPEI index and artificial intelligence algorithms

Fabio Di Nunno et al.

Summary: By dividing Southern Italy into homogeneous drought regions using three clustering algorithms (K-mean, Hierarchical, and Expectation-Maximization), reliable prediction of spatio-temporal drought variation can be achieved. This study utilized gridded data of the Standardized Precipitation Evapotranspiration Index (SPEI6) to assess drought trends and developed Machine Learning (ML) algorithms for forecasting drought events. The hybrid M5P-SVR model outperformed individual M5P and SVR models, making it suitable for forecasting long and severe drought events.

STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT (2023)

Review Engineering, Multidisciplinary

A review of the hybrid artificial intelligence and optimization modelling of hydrological streamflow forecasting

Karim Sherif Mostafa Hassan Ibrahim et al.

Summary: Artificial Intelligence has been widely applied and researched in the field of hydrology, leading to the development of hybrid models with optimization techniques. This review paper categorizes and studies AI models and optimization techniques, summarizing their advantages and disadvantages. The focus of the research is the forecasting of streamflows, and future recommendations and conclusions are provided.

ALEXANDRIA ENGINEERING JOURNAL (2022)

Article Engineering, Environmental

River flow rate prediction in the Des Moines watershed (Iowa, USA): a machine learning approach

Ahmed Elbeltagi et al.

Summary: Predicting the flow rate in rivers is crucial for water resource planning and management. This study demonstrates that accurate streamflow prediction models can be developed using a Machine Learning approach. The Des Moines watershed, including both the Des Moines River and Raccoon River, was selected for a case study. Four machine learning algorithms were used, and the M5P algorithm produced the best predictions with R-2 values of 0.970 and 0.960 for two stations on the East Fork Des Moines River at Dakota City and Des Moines River near Tracy, respectively. Accurate predictions were also achieved for the Raccoon River, with R-2 values of 0.938 and 0.887 for two stations, North Raccoon River near Jefferson and Raccoon River at Van Meter, respectively.

STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT (2022)

Article Computer Science, Artificial Intelligence

Streamflow modelling and forecasting for Canadian watersheds using LSTM networks with attention mechanism

Lakshika Girihagama et al.

Summary: This study investigates the performance of sequence-to-sequence machine learning architectures in developing streamflow forecasting tools for Canadian watersheds. The attention-based encoder-decoder LSTM model outperformed the standard model in simulating overall hydrograph patterns. The results suggest that the attention mechanism in ML architectures is important and useful for hydrological applications, and the encoder-decoder LSTM with attention mechanism is a powerful choice for streamflow forecasting systems.

NEURAL COMPUTING & APPLICATIONS (2022)

Article Geosciences, Multidisciplinary

Using a long short-term memory (LSTM) neural network to boost river streamflow forecasts over the western United States

Kieran M. R. Hunt et al.

Summary: Accurate river streamflow forecasts are crucial for water security, flood preparedness, agriculture, and industry. Traditional physics-based models have improved over time, but are limited by empirical relationships and lack of data. Artificial neural networks, particularly long short-term memory (LSTM) networks, have shown promise in simulating non-linear systems. Hybrid forecasting systems combining physics-based approaches and statistical techniques have been explored for hydrological applications. This study evaluates the performance of LSTM networks in predicting streamflow at 10 river gauge stations in the western United States and compares it with physics-based models. The results demonstrate the potential of LSTM networks in improving hydrological forecasting.

HYDROLOGY AND EARTH SYSTEM SCIENCES (2022)

Article Computer Science, Artificial Intelligence

Super ensemble learning for daily streamflow forecasting: large-scale demonstration and comparison with multiple machine learning algorithms

Hristos Tyralis et al.

Summary: Traditional daily streamflow forecasting using a single machine learning algorithm is limited to few case studies. This study proposes super learning, combining 10 machine learning algorithms, and demonstrates superior predictive performance over other methods using a large dataset.

NEURAL COMPUTING & APPLICATIONS (2021)

Article Engineering, Civil

A novel attention-based LSTM cell post-processor coupled with bayesian optimization for streamflow prediction

Babak Alizadeh et al.

Summary: The study introduces a novel deep learning model SAINA-LSTM, which improves streamflow forecasting performance by incorporating attention mechanism into LSTM cells. SAINA-LSTM outperforms other models in various climatological basins and for 1- to 7-day ahead forecasts in different flow ranges.

JOURNAL OF HYDROLOGY (2021)

Article Computer Science, Information Systems

Comparison of Deep Learning Techniques for River Streamflow Forecasting

Xuan-Hien Le et al.

Summary: This study evaluated the performance of DL models in streamflow forecasting by comparing six supervised learning models. The findings suggest that LSTM-based models outperformed FFNN and CNN models in terms of both performance and stability. However, the more complex StackedLSTM and BiLSTM models did not show improvements in performance. For streamflow forecasting, simple architecture LSTM and GRU models are sufficient for producing reliable forecasts while minimizing computation time.

IEEE ACCESS (2021)

Article Green & Sustainable Science & Technology

A Scalable River Flow Forecast and Basin Optimization System for Hydropower Plants

Serkan Buhan et al.

IEEE TRANSACTIONS ON SUSTAINABLE ENERGY (2020)

Article Engineering, Civil

Long lead-time daily and monthly streamflow forecasting using machine learning methods

M. Cheng et al.

JOURNAL OF HYDROLOGY (2020)

Article Meteorology & Atmospheric Sciences

Hydrological Forecasts and Projections for Improved Decision-Making in the Water Sector in Europe

Luis Samaniego et al.

BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY (2019)

Article Engineering, Civil

A hybrid support vector regression framework for streamflow forecast

Xiangang Luo et al.

JOURNAL OF HYDROLOGY (2019)

Article Engineering, Environmental

Comparison of stochastic and machine learning methods for multi-step ahead forecasting of hydrological processes

Georgia Papacharalampous et al.

STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT (2019)

Article Computer Science, Interdisciplinary Applications

Modeling of river flow rate as a function of rainfall and temperature using response surface methodology based on historical time series

Srdan Kostic et al.

JOURNAL OF HYDROINFORMATICS (2016)

Article Computer Science, Interdisciplinary Applications

Artificial neural network ensemble modeling with exploratory factor analysis for streamflow forecasting

Sung Eun Kim et al.

JOURNAL OF HYDROINFORMATICS (2015)

Review Engineering, Civil

Artificial intelligence based models for stream-flow forecasting: 2000-2015

Zaher Mundher Yaseen et al.

JOURNAL OF HYDROLOGY (2015)

Article Water Resources

Daily streamflow forecasting using a wavelet transform and artificial neural network hybrid models

Celso Augusto Guimaraes Santos et al.

HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES (2014)

Review Engineering, Civil

Applications of hybrid wavelet-Artificial Intelligence models in hydrology: A review

Vahid Nourani et al.

JOURNAL OF HYDROLOGY (2014)

Article Computer Science, Artificial Intelligence

Performance of radial basis and LM-feed forward artificial neural networks for predicting daily watershed runoff

Mohammad Zounemat-kermani et al.

APPLIED SOFT COMPUTING (2013)

Article Engineering, Civil

Daily streamflow forecasting by machine learning methods with weather and climate inputs

Kabir Rasouli et al.

JOURNAL OF HYDROLOGY (2012)

Article Engineering, Civil

Investigation of Internal Functioning of the Radial-Basis-Function Neural Network River Flow Forecasting Models

D. Achela K. Fernando et al.

JOURNAL OF HYDROLOGIC ENGINEERING (2009)

Article Computer Science, Artificial Intelligence

Framewise phoneme classification with bidirectional LSTM and other neural network architectures

A Graves et al.

NEURAL NETWORKS (2005)

Article Engineering, Civil

Performance of neural networks in daily streamflow forecasting

S Birikundavyi et al.

JOURNAL OF HYDROLOGIC ENGINEERING (2002)

Review Computer Science, Interdisciplinary Applications

Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications

HR Maier et al.

ENVIRONMENTAL MODELLING & SOFTWARE (2000)