Article
Engineering, Civil
Haibo Chu, Jiahua Wei, Wenyan Wu, Yuan Jiang, Qi Chu, Xiujing Meng
Summary: The paper introduces an integrated framework for daily streamflow forecasting based on different flow regimes, which improves modeling performance through regime identification, input selection, nonlinear relationship mapping, and rigorous validation. Application of the framework to three streamflow stations in the USA shows significant performance improvement compared to single data-driven models.
JOURNAL OF HYDROLOGY
(2021)
Article
Computer Science, Artificial Intelligence
Caixia Jing, Liqing Qiu, Xiangbo Tian, Tingyu Hao
Summary: This paper proposes an Influencer Affected Attention Fusion (IAAF) method for publication classification prediction and a corresponding IAAF-DBN model. Experimental results show that the model outperforms other models in terms of accuracy and can replace manual classification, reducing the workload. Furthermore, the study suggests a more accurate method for publication similarity, providing new insights for citation recommendation and data recommendation research.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Engineering, Civil
Haibo Chu, Jiahua Wei, Yuan Jiang
Summary: This study proposed a framework that integrates lasso, DBN, and bootstrap methods to improve the performance and stability of streamflow forecasting. The results indicated that the lasso-DBN-bootstrap model produced more accurate forecasting results in a case study in the Three-River Headwaters Region, providing valuable information for water resources management and planning.
WATER RESOURCES MANAGEMENT
(2021)
Article
Biology
Hanjie Chen, Saptarshi Das, John M. Morgan, Koushik Maharatna
Summary: This paper proposes a novel method for predicting and classifying ventricular arrhythmias. The method has been validated using data from two open databases and showed promising results in terms of prediction time and accuracy. This method has the potential to advance technologies such as implantable cardioverter defibrillators and help prevent sudden cardiac death.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Computer Science, Artificial Intelligence
Yang-Geng Fu, Ji-Feng Ye, Ze-Feng Yin, Long-Jiang Chen, Ying-Ming Wang, Geng-Geng Liu
Summary: This study proposes a novel EBRB system based on fuzzy C-means clustering to address class imbalance by oversampling positive samples and undersampling negative samples, improving inference accuracy for imbalanced data.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Interdisciplinary Applications
S. Sadeghi Tabas, N. Humaira, S. Samadi, N. C. Hubig
Summary: This paper presents a dynamical neural network framework called FlowDyn for understanding catchment systems' response to daily rainfall-runoff processes. Testing on more than 180 gauging stations worldwide, the researchers found that different DNN models could learn both regionally consistent and location-specific hydrological behaviors. Hence, they advocate for the application of the FlowDyn package in the field of daily rainfall-runoff prediction.
ENVIRONMENTAL MODELLING & SOFTWARE
(2023)
Article
Green & Sustainable Science & Technology
Ahmadhon Akbarkhonovich Kamolov, Suhyun Park
Summary: Implementing AI and machine learning algorithms in the marine world, particularly in tasks like measuring seabed depth, plays a crucial role in solving complex problems. This article introduces a case study on training crowdsourced bathymetry data using the fuzzy c-means (FCM) clustering algorithm, demonstrating its effectiveness and accuracy.
Article
Computer Science, Information Systems
Ali Danandeh Mehr, Amir H. Gandomi
Summary: This paper introduces a new multi-stage genetic programming (MSGP) technique called MSGP-LASSO, which has been successfully applied for univariate streamflow forecasting in the Sedre River in Turkey. MSGP-LASSO has been verified to be more reliable and effective in flood forecasting compared to traditional genetic programming techniques.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Xiaowei Gu, Plamen P. Angelov, Qiang Shen
Summary: In this article, a novel zero-order EFS model with a unique belief structure is proposed for data stream classification. The model can handle interclass overlaps and better capture the underlying structure of data streams through prototypes. Experimental results demonstrate the superior performance of the model on various classification problems.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Ecology
Marcio Carneiro Brito Pache, Diego Andre Sant Ana, Joao Victor Araujo Rozales, Vanessa Aparecida de Moraes Weber, Adair da Silva Oliveira Junior, Vanir Garcia, Hemerson Pistori, Marco Hiroshi Naka
Summary: This study developed a system for predicting the body biomass of live fingerlings using a computer vision system, which proved to be more convenient and accurate compared to traditional invasive estimation methods. Results showed that using automatic frame selection based on Euclidean distance and data augmentation through rotation optimized the prediction of fish biomass.
ECOLOGICAL INFORMATICS
(2022)
Article
Computer Science, Artificial Intelligence
Prasannavenkatesan Theerthagiri, A. Usha Ruby, J. George Chellin Chandran
Summary: This article introduces a mineral classification and identification system based on deep computer vision technology and deep residual neural network models. By using convolutional feature selection and various pooling algorithms, the model extracts features from mineral images and achieves better classification results. After evaluation, the model achieved an accuracy of 91%.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Alex J. DeGrave, Joseph D. Janizek, Su-In Lee
Summary: Recent deep learning systems to detect COVID-19 from chest radiographs may rely on confounding factors rather than medical pathology, leading to accuracy issues when tested in new hospitals. The approach to obtain training data for these AI systems introduces a nearly ideal scenario for learning spurious shortcuts, raising concerns in medical-imaging AI. Evaluation of models on external data is insufficient to ensure reliance on medically relevant pathology, highlighting the importance of explainable AI for clinical deployment of machine-learning healthcare models.
NATURE MACHINE INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Zuo-Wei Zhang, Zhe Liu, Zong-Fang Ma, Yiru Zhang, Hao Wang
Summary: This paper proposes a belief-based incomplete pattern unsupervised classification method for clustering incomplete patterns. By grouping and editing complete patterns, this method can effectively address the challenge of clustering incomplete patterns.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Environmental Sciences
Bo Ke, Hoang Nguyen, Xuan-Nam Bui, Hoang-Bac Bui, Trung Nguyen-Thoi
Summary: The FCM-BPNN model showed promising results in predicting the sorption efficiency of heavy metals onto biochar, outperforming the BPNN model alone. The FCM algorithm significantly improved the performance of the BPNN model in this study.
JOURNAL OF ENVIRONMENTAL MANAGEMENT
(2021)
Article
Computer Science, Artificial Intelligence
Dhivya Elavarasan, P. M. Durai Raj Vincent
Summary: The article introduces a crop yield prediction system based on deep learning, using deep belief networks and fuzzy neural network systems. By utilizing pre-training techniques and feature vector generation, the model's accuracy and efficiency are enhanced, outperforming other models.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Meteorology & Atmospheric Sciences
Olusola O. Ayantobo, Jiahua Wei, Beiming Kang, Guangqian Wang
Summary: The study found that the integrated moisture transport (IVT) in Eastern and Southern China, as well as Southeastern China, showed significant variations, while in Northern China, the variations were smaller. Changes in atmospheric circulation patterns could potentially affect weather conditions in China.
THEORETICAL AND APPLIED CLIMATOLOGY
(2022)
Article
Environmental Sciences
Keyi Wang, Li Zhang, Tiejian Li, Xiang Li, Biyun Guo, Guoxin Chen, Yuefei Huang, Jiahua Wei
Summary: Self-similarity and plane-filling are intrinsic structure properties of natural river networks. This paper introduces a new type of nonstochastic quasi-uniform iterative binary tree networks (QU-IBTNs) that satisfy these properties through the use of generator series and a quasi-uniform iteration rule. The study also demonstrates the inherent consistency between QU-IBTNs and natural river networks through case studies at low Horton-Strahler orders.
FRONTIERS IN ENVIRONMENTAL SCIENCE
(2022)
Article
Meteorology & Atmospheric Sciences
Olusola O. O. Ayantobo, Jiahua Wei, Guangqian Wang
Summary: In the Yangtze River Basin, Atmospheric rivers (ARs) are found to be related to Extreme Precipitation Events (EPEs). The frequency of ARs and the atmospheric circulation system have been analyzed to understand their nexus. The study reveals that ARs play a significant role in shaping the global hydrological cycle.
ATMOSPHERIC RESEARCH
(2022)
Article
Environmental Sciences
Yuerong Zhou, Wenyan Wu, Rory Nathan, Q. J. Wang
Summary: In this study, a new approach is proposed to simulate the temporal and spatial variation of flood inundation for a floodplain with complex flow paths. The combination of a 1D convolutional neural network model and a U-Net method achieves accurate water depth simulation and flood surface reconstruction.
WATER RESOURCES RESEARCH
(2022)
Review
Environmental Sciences
Graeme Dandy, Wenyan Wu, Angus Simpson, Michael Leonard
Summary: Many studies have used optimization in water distribution systems, addressing variables such as cost, energy, greenhouse gas emissions, pressure deficit, and system reliability. However, few studies have considered uncertainty sources such as model, data, and human-related uncertainties. This paper reviews the importance of these uncertainties on optimization objectives and identifies gaps in the literature.
Article
Engineering, Marine
Scott A. Stephens, Wenyan Wu
Summary: The study assesses the dependence between extreme skew-surge and extreme rainfall, as well as extreme skew-surge and extreme river-flow in New Zealand. The results show a significant but not strong correlation between these variables. Weather types play a crucial role in driving regional patterns of dependence.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2022)
Article
Environmental Sciences
Wenyan Wu, Yuerong Zhou, Michael Leonard
Summary: The study compares different methods for incorporating uncertainty into multiobjective reservoir operation policy optimization, highlighting their advantages and limitations. Each method has its own pros and cons, and careful consideration is required when selecting the most suitable approach based on research needs.
ENVIRONMENTAL RESEARCH COMMUNICATIONS
(2022)
Review
Water Resources
C. Giudicianni, D. Mitrovic, W. Wu, G. Ferrarese, F. Pugliese, I Fernandez-Garcia, A. Campisano, F. De Paola, S. Malavasi, H. R. Maier, D. Savic, E. Creaco
Summary: Water distribution networks (WDNs) are significant energy consumers and contributors to greenhouse gas emissions. Energy recovery strategies (ERSs) have the potential to switch WDNs to renewable energy sources, reducing emissions and dependence on power-grids. This review provides a comprehensive analysis of available devices and applications for ERSs, highlighting achievements and identifying common issues and future research directions, including field testing, comparison of solutions, and addressing socio/political barriers to diffusion.
URBAN WATER JOURNAL
(2023)
Article
Environmental Sciences
Niels Fraehr, Quan J. J. Wang, Wenyan Wu, Rory Nathan
Summary: To address the issue of high computational cost in running high-resolution hydrodynamic models, the LSG model was introduced, which uses a combination of low-fidelity simulations, spatial analysis, and Gaussian process learning. However, the LSG model has only been tested on hydrodynamic models with structured grids and information on flood extent alone is often insufficient for accurate flood risk assessments.
WATER RESOURCES RESEARCH
(2023)
Article
Environmental Sciences
Yang Shi, Zhen Qiao, Guangqian Wang, Jiahua Wei
Summary: In this study, the cloud-precipitation interference effect was evaluated based on a monitoring system. The results show that acoustic waves have a significant impact on rainfall clouds, rain droplets, and microwave parameters. Opening the acoustic device significantly lowered the base height of the precipitation cloud, while raindrop agglomeration and de-agglomeration were in dynamic equilibrium. The reflectivity factor surged within 1200 m of the operation centre during acoustic interference.
Article
Environmental Sciences
Giri R. Kattel, Amelie Paszkowski, Yadu Pokhrel, Wenyan Wu, Dongfeng Li, Mukund P. P. Rao
Summary: The high-mountain system in the Himalaya, which supports ecosystem services for about 1.5 billion people, is facing unprecedented challenges due to climate change. Climate change and anthropogenic activities are causing hydro-meteorological transformations that require adaptive measures for sustainable waterways. Integrated waterway management, advancement of infrastructure technologies, and improved governance are crucial in protecting nature and society.
WILEY INTERDISCIPLINARY REVIEWS-WATER
(2023)
Article
Agronomy
Wenqian Zhang, Jiahua Wei, Lili Guo, Heng Fang, Xiaojuan Liu, Kehao Liang, Wenquan Niu, Fulai Liu, Kadambot H. M. Siddique
Summary: This study investigates the effects of two types of biochar (wood biochar and poultry biochar) on the growth and physiology of tomato seedlings under drought and salinity stress. The results show that biochar addition effectively improves the root water potential and osmotic potential of tomato plants, and significantly improves the leaf relative water content. Furthermore, biochar application reduces the concentration of abscisic acid in xylem sap under drought and salinity stress.
Review
Computer Science, Interdisciplinary Applications
Wenyan Wu, Leila Eamen, Graeme Dandy, Saman Razavi, George Kuczera, Holger R. Maier
Summary: Traditionally, reservoir management has focused on optimizing the operation and control of engineering infrastructure systems. However, the impacts of reservoirs on society and the environment, as well as the uncertainties associated with societal values, risk appetite, and politics, have been often overlooked. This paper addresses these issues by reviewing reservoir management through the lens of wickedness, competing objectives, and uncertainty, highlighting the challenges and research efforts needed to ensure these systems best serve society and the environment.
ENVIRONMENTAL MODELLING & SOFTWARE
(2023)
Article
Engineering, Civil
Tristan D. J. Graham, Quan J. J. Wang, Yating Tang, Andrew Western, Wenyan Wu, Guy Ortlipp, Mark Bailey, Senlin Zhou, Kirsti Hakala, Qichun Yang
Summary: Water agencies allocate water based on agreed entitlement systems, often using historical climatology and a limited selection of climatic scenarios to issue seasonal water allocation outlooks. However, these outlooks have large uncertainties and lead to inefficient water use. This study investigates the use of ensemble seasonal inflow forecasts to improve the production of water allocation outlooks, resulting in outlooks that are closer to actual determinations and with reduced uncertainty. The integration of streamflow forecasts can lead to more efficient water use and water market participation.
JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT
(2023)
Article
Engineering, Civil
Qi Zhao, Wenyan Wu, Angus R. Simpson, Ailsa Willis
Summary: In order to meet the increasing energy demands, reduce environmental impacts, and increase economic benefits, many water utilities have installed behind-the-meter (BTM) energy systems. This paper proposes a systematic optimization approach for designing and evaluating water distribution systems (WDS) with BTM solar energy. The study demonstrates the trade-offs between economic and environmental costs, and the benefits of incorporating BTM solar energy into WDS.
JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT
(2023)
Article
Engineering, Civil
Arfan Arshad, Ali Mirchi, Javier Vilcaez, Muhammad Umar Akbar, Kaveh Madani
Summary: High-resolution, continuous groundwater data is crucial for adaptive aquifer management. This study presents a predictive modeling framework that incorporates covariates and existing observations to estimate groundwater level changes. The framework outperforms other methods and provides reliable estimates for unmonitored sites. The study also examines groundwater level changes in different regions and highlights the importance of effective aquifer management.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Lihua Chen, Jie Deng, Wenzhe Yang, Hang Chen
Summary: A new grid-based distributed karst hydrological model (GDKHM) is developed to simulate streamflow in the flood-prone karst area of Southwest China. The results show that the GDKHM performs well in predicting floods and capturing the spatial variability of karst system.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Faruk Gurbuz, Avinash Mudireddy, Ricardo Mantilla, Shaoping Xiao
Summary: Machine learning algorithms have shown better performance in streamflow prediction compared to traditional hydrological models. In this study, researchers proposed a methodology to test and benchmark ML algorithms using artificial data generated by physically-based hydrological models. They found that deep learning algorithms can correctly identify the relationship between streamflow and rainfall in certain conditions, but fail to outperform traditional prediction methods in other scenarios.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Yadong Ji, Jianyu Fu, Bingjun Liu, Zeqin Huang, Xuejin Tan
Summary: This study distinguishes the uncertainty in drought projection into scenario uncertainty, model uncertainty, and internal variability uncertainty. The results show that the estimation of total uncertainty reaches a minimum in the mid-21st century and that model uncertainty is dominant in tropical regions.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Z. R. van Leeuwen, M. J. Klaar, M. W. Smith, L. E. Brown
Summary: This study quantifies the effectiveness of leaky dams in reducing flood peak magnitude using a transfer function noise modelling approach. The results show that leaky dams have a significant but highly variable impact on flood peak magnitude, and managing expectations should consider event size and type.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Zeda Yin, Yasaman Saadati, M. Hadi Amini, Linlong Bian, Beichao Hu
Summary: Combined sewer overflows pose significant threats to public health and the environment, and various strategies have been proposed to mitigate their adverse effects. Smart control strategies have gained traction due to their cost-effectiveness but face challenges in balancing precision and computational efficiency. To address this, we propose exploring machine learning models and the inversion of neural networks for more efficient CSO prediction and optimization.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Qimou Zhang, Jiacong Huang, Jing Zhang, Rui Qian, Zhen Cui, Junfeng Gao
Summary: This study developed a N-cycling model for lowland rural rivers covered by macrophytes and investigated the N imports, exports, and response to sediment dredging. The findings showed a considerable N retention ability in the study river, with significant N imports from connected rivers and surrounding polders. Sediment dredging increased particulate nitrogen resuspension and settling rates, while decreasing ammonia nitrogen release, denitrification, and macrophyte uptake rates.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Xue Li, Yingyin Zhou, Jian Sha, Man Zhang, Zhong-Liang Wang
Summary: High-resolution climate data is crucial for predicting regional climate and water environment changes. In this study, a two-step downscaling method was developed to enhance the spatial resolution of GCM data and improve the accuracy for small basins. The method combined medium-resolution climate data with high-resolution topographic data to capture spatial and temporal details. The downscaled climate data were then used to simulate the impacts of climate change on hydrology and water quality in a small basin. The results demonstrated the effectiveness of the downscaling method for spatially differentiated simulations.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Tongqing Shen, Peng Jiang, Jiahui Zhao, Xuegao Chen, Hui Lin, Bin Yang, Changhai Tan, Ying Zhang, Xinting Fu, Zhongbo Yu
Summary: This study evaluates the long-term interannual dynamics of permafrost distribution and active layer thickness on the Tibetan Plateau, and predicts future degradation trends. The results show that permafrost area has been decreasing and active layer thickness has been increasing, with an accelerated degradation observed in recent decades. This has significant implications for local water cycle processes, water ecology, and water security.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Chi Zhang, Xu Zhang, Qiuhong Tang, Deliang Chen, Jinchuan Huang, Shaohong Wu, Yubo Liu
Summary: Precipitation over the Tibetan Plateau is influenced by systems such as the Asian monsoons, the westerlies, and local circulations. The Indian monsoon, the westerlies, and local circulations are the main systems affecting precipitation over the entire Tibetan Plateau. The East Asian summer monsoon primarily affects the eastern Tibetan Plateau. The Indian monsoon has the greatest influence on precipitation in the southern and central grid cells, while the westerlies have the greatest influence on precipitation in the northern and western grid cells. Local circulations have the strongest influence on the central and eastern grid cells.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Manuel Almeida, Antonio Rodrigues, Pedro Coelho
Summary: This study aimed to improve the accuracy of Total Phosphorus export coefficient models, which are essential for water management. Four different models were applied to 27 agroforestry watersheds in the Mediterranean region. The modeling approach showed significant improvements in predicting the Total Phosphorus diffuse loads.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Yutao Wang, Haojie Yin, Ziyi Wang, Yi Li, Pingping Wang, Longfei Wang
Summary: This study investigated the distribution and transformation of dissolved organic nitrogen (DON) in riverbed sediments impacted by effluent discharge. The authors found that the spectral characteristics of dissolved organic matter (DOM) in surface water and sediment porewater could be used to predict DON variations in riverbed sediments. Random forest and extreme gradient boosting machine learning methods were employed to provide accurate predictions of DON content and properties at different depths. These findings have important implications for wastewater discharge management and river health.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Saba Mirza Alipour, Kolbjorn Engeland, Joao Leal
Summary: This study assesses the uncertainty associated with 100-year flood maps under different scenarios using Monte Carlo simulations. The findings highlight the importance of employing probabilistic approaches for accurate and secure flood maps, with the selection of probability distribution being the primary source of uncertainty in precipitation.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Janine A. de Wit, Marjolein H. J. van Huijgevoort, Jos C. van Dam, Ge A. P. H. van den Eertwegh, Dion van Deijl, Coen J. Ritsema, Ruud P. Bartholomeus
Summary: The study focuses on the hydrological consequences of controlled drainage with subirrigation (CD-SI) on groundwater level, soil moisture content, and soil water potential. The simulations show that CD-SI can improve hydrological conditions for crop growth, but the success depends on subtle differences in geohydrologic characteristics.
JOURNAL OF HYDROLOGY
(2024)
Article
Engineering, Civil
Constantin Seidl, Sarah Ann Wheeler, Declan Page
Summary: Water availability and quality issues will become increasingly important in the future due to climate change impacts. Managed Aquifer Recharge (MAR) is an effective water management tool, but often overlooked. This study analyzes global MAR applications and identifies the key factors for success, providing valuable insights for future design and application.
JOURNAL OF HYDROLOGY
(2024)
