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Saeed Samadianfard, Salar Jarhan, Ely Salwana, Amir Mosavi, Shahaboddin Shamshirband and Shatirah Akib
Advancement in river flow prediction systems can greatly empower the operational river management to make better decisions, practices, and policies. Machine learning methods recently have shown promising results in building accurate models for river flow...
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Jean Cardi, Antony Dussel, Clara Letessier, Isa Ebtehaj, Silvio Jose Gumiere and Hossein Bonakdari
The Ottawa River Watershed is a vast area that stretches across Ontario and Quebec and holds great importance for Canada?s people, economy, and collective history, both in the present and the future. The river has faced numerous floods in recent years du...
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Bojan Ðurin, Lucija Plantak, Ognjen Bonacci and Fabio Di Nunno
Forecasting upstream flow amount based on downstream flow values is a new way of managing flood risk. This kind of prediction of the flow, size, and intensity of rivers in the alluvial aquifers is, in most cases, a challenging task due to climate change,...
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Mohammed Abdul Bari, Gnanathikkam Emmanuel Amirthanathan, Fitsum Markos Woldemeskel and Paul Martinus Feikema
We analysed changes in magnitude and timing of the largest annual observed daily flow (Amax), in each water year, for 596 stations in high-value water resource catchments and flood risk locations across Australia. These stations are either included in th...
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Han Wang, Ping-an Zhong, Ervin Zsoter, Christel Prudhomme, Florian Pappenberger and Bin Xu
Our paper aims to improve flood forecasting by establishing whether a global hydrological forecast system could be used as an alternative to a regional system, or whether it could provide additional information. This paper was based on the operational Gl...
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Albert Larson, Abdeltawab Hendawi, Thomas Boving, Soni M. Pradhanang and Ali S. Akanda
The impact of climate change continues to manifest itself daily in the form of extreme events and conditions such as droughts, floods, heatwaves, and storms. Better forecasting tools are mandatory to calibrate our response to these hazards and help adapt...
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Yen-Chang Chen, Hui-Chung Yeh, Su-Pai Kao, Chiang Wei and Pei-Yi Su
In this study, a novel model that performs ensemble empirical mode decomposition (EEMD) and stepwise regression was developed to forecast the water level of a tidal river. Unlike more complex hydrological models, the main advantage of the proposed model ...
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Andre D. L. Zanchetta, Paulin Coulibaly and Vincent Fortin
The use of machine learning (ML) for predicting high river flow events is gaining prominence and among its non-trivial design decisions is the definition of the quantitative precipitation estimate (QPE) product included in the input dataset. This study p...
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Mahshid Khazaeiathar, Reza Hadizadeh, Nasrin Fathollahzadeh Attar and Britta Schmalz
The behavior of hydrological processes is periodic and stochastic due to the influence of climatic factors. Therefore, it is crucial to develop the models based on their periodicity and stochastic nature for prediction. Furthermore, forecasting the strea...
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Md Abdullah Al Mehedi, Marzieh Khosravi, Munshi Md Shafwat Yazdan and Hanieh Shabanian
River flow prediction is a pivotal task in the field of water resource management during the era of rapid climate change. The highly dynamic and evolving nature of the climatic variables, e.g., precipitation, has a significant impact on the temporal dist...
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