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Andrea Momblanch, Ian P. Holman and Sanjay K. Jain
Global change is expected to have a strong impact in the Himalayan region. The climatic and orographic conditions result in unique modelling challenges and requirements. This paper critically appraises recent hydrological modelling applications in Himala...
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Evangelos Rozos, Vasilis Bellos, John Kalogiros and Katerina Mazi
This paper presents an efficient flood early warning system developed for the city of Mandra, Greece which experienced a devastating flood event in November 2017 resulting in significant loss of life. The location is of particular interest due to both it...
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Krishna Raj Raghavendran and Ahmed Elragal
In the context of developing machine learning models, until and unless we have the required data engineering and machine learning development competencies as well as the time to train and test different machine learning models and tune their hyperparamet...
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Michel Craninx, Koen Hilgersom, Jef Dams, Guido Vaes, Thomas Danckaert and Jan Bronders
Worldwide, climate change increases the frequency and intensity of heavy rainstorms. The increasing severity of consequent floods has major socio-economic impacts, especially in urban environments. Urban flood modelling supports the assessment of these i...
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Fazlullah Akhtar, Usman Khalid Awan, Christian Borgemeister and Bernhard Tischbein
The Kabul River Basin (KRB) in Afghanistan is densely inhabited and heterogenic. The basin?s water resources are limited, and climate change is anticipated to worsen this problem. Unfortunately, there is a scarcity of data to measure the impacts of clima...
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Ziauddin Safari, Sayed Tamim Rahimi, Kamal Ahmed, Ahmad Sharafati, Ghaith Falah Ziarh, Shamsuddin Shahid, Tarmizi Ismail, Nadhir Al-Ansari, Eun-Sung Chung and Xiaojun Wang
An approach is proposed in the present study to estimate the soil erosion in data-scarce Kokcha subbasin in Afghanistan. The Revised Universal Soil Loss Equation (RUSLE) model is used to estimate soil erosion. The satellite-based data are used to obtain ...
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David Matamoros, Mijail Arias-Hidalgo, Maria del Pilar Cornejo-Rodriguez and Mercy J. Borbor-Cordova
Urban flooding is a major problem in many coastal cities around the world, mainly caused by factors such as poor urban planning, outdated sewer capacity or high frequent extreme events. In developing countries such as Ecuador, lack of monitoring, financi...
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Anita Nag and Basudev Biswal
Construction of flow duration curves (FDCs) is a challenge for hydrologists as most streams and rivers worldwide are ungauged. Regionalization methods are commonly followed to solve the problem of discharge data scarcity by transforming hydrological info...
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Sanjaya Devkota, Narendra Man Shakya, Karen Sudmeier-Rieux, Michel Jaboyedoff, Cees J. Van Westen, Brian G. Mcadoo and Anu Adhikari
Intense monsoonal rain is one of the major triggering factors of floods and mass movements in Nepal that needs to be better understood in order to reduce human and economic losses and improve infrastructure planning and design. This phenomena is better u...
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Iuliia Radchenko, Lutz Breuer, Irina Forkutsa and Hans-Georg Frede
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Iuliia Radchenko, Lutz Breuer, Irina Forkutsa and Hans-Georg Frede
Glaciers and snowmelt supply the Naryn and Karadarya rivers, and about 70% of the water available for the irrigated agriculture in the Ferghana Valley. Nineteen smaller catchments contribute the remaining water mainly from annual precipitation. The latte...
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Igor Ryazanov, Amanda T. Nylund, Debabrota Basu, Ida-Maja Hassellöv and Alexander Schliep
Driven by the unprecedented availability of data, machine learning has become a pervasive and transformative technology across industry and science. Its importance to marine science has been codified as one goal of the UN Ocean Decade. While increasing a...
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Ali Mursid
Pág. in press
Abstract This study aims to investigate the effects of positive and negative sentiment on impulsive buying behavior among Indonesia people based on the theory of stimulus organism response (S-O-R). First, it examines how COVID-19 information, information...
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Abir M. Badr, Fadi Abdelradi, Abdelazim Negm and Elsayed M. Ramadan
Middle East and North Africa (MENA) regions are increasingly concerned about water scarcity. Egypt, one of the arid MENA nations that relies primarily on Nile water, faces a water scarcity issue because of a mismatch between demand and supply. This study...
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Jacqueline Noga and Gregor Wolbring
Perceptions of water and water related issues still render many under-researched topics. This study aims to further our knowledge regarding people?s perceptions of water and our understanding about the different ways individuals use water. The authors as...
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Min Ma, Shanrong Liu, Shufei Wang and Shengnan Shi
Automatic modulation classification (AMC) plays a crucial role in wireless communication by identifying the modulation scheme of received signals, bridging signal reception and demodulation. Its main challenge lies in performing accurate signal processin...
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Yong Liu, Jialin Zhou, Dong Zhang, Shaoyu Wei, Mingshun Yang and Xinqin Gao
To solve the problem of low diagnostic accuracy caused by the scarcity of fault samples and class imbalance in the fault diagnosis task of box-type substations, a fault diagnosis method based on self-attention improvement of conditional tabular generativ...
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Thaiënne A. G. P. Van Dijk, Marc Roche, Xavier Lurton, Ridha Fezzani, Stephen M. Simmons, Sven Gastauer, Peer Fietzek, Chris Mesdag, Laurent Berger, Mark Klein Breteler and Dan R. Parsons
For health and impact studies of water systems, monitoring underwater environments is essential, for which multi-frequency single- and multibeam echosounders are commonly used state-of-the-art technologies. However, the current scarcity of sediment refer...
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Nadia Brancati and Maria Frucci
To support pathologists in breast tumor diagnosis, deep learning plays a crucial role in the development of histological whole slide image (WSI) classification methods. However, automatic classification is challenging due to the high-resolution data and ...
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Mohamad Abou Ali, Fadi Dornaika and Ignacio Arganda-Carreras
Deep learning (DL) has made significant advances in computer vision with the advent of vision transformers (ViTs). Unlike convolutional neural networks (CNNs), ViTs use self-attention to extract both local and global features from image data, and then ap...
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