Journal of Hydroinformatics最新文献_第5页

Assessing the impact of an arch-dam breach magnitude and reservoir inflow on flood maps 评估拱坝决口的规模和水库流入量对洪水地图的影响

Journal of Hydroinformatics Pub Date : 2023-11-23 DOI: 10.2166/hydro.2023.301

Daniela Elena Gogoașe Nistoran, C. Ionescu, Ș. Simionescu

{"title":"Assessing the impact of an arch-dam breach magnitude and reservoir inflow on flood maps","authors":"Daniela Elena Gogoașe Nistoran, C. Ionescu, Ș. Simionescu","doi":"10.2166/hydro.2023.301","DOIUrl":"https://doi.org/10.2166/hydro.2023.301","url":null,"abstract":"Different scenarios of an arch-dam breach and their impact on the time-space evolution of flood waves are analysed using numerical modelling. As the accidents involving this type of dam are among the most catastrophic ones, the 108 m in height Paltinu arch-dam, Romania, was chosen as a case study due to its problems in the past. Three dam breach magnitudes and two inflow hydrographs for the worst-case scenario of Normal Operating Pool elevation in the reservoir were chosen as variable parameters, to assess their influence on the dam break wave characteristics and downstream flooded areas. The flood was routed along the 18 km reach of the Doftana River down to the confluence with the Prahova River. A 2D numerical model was setup with the help of HEC-RAS software, which was also used to analyse the resulting hazard maps under a GIS environment. Comparison of inundation boundary, maximum depths and velocities, as well as the arrival time at control sections allow for conclusions to be drawn. These predictive results of shape, magnitude, and time to peak of the flood waves are essential for flood risk management to obtain the risk maps, estimated damage costs, and possible affected areas.","PeriodicalId":507813,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139245947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A GPU-based hydrodynamic numerical model for urban rainstorm inundation simulations 基于 GPU 的城市暴雨淹没模拟流体力学数值模型

Journal of Hydroinformatics Pub Date : 2023-11-22 DOI: 10.2166/hydro.2023.152

Hao Han, Jingming Hou, Zhao Jin, Pingping Luo, Guodong Li, Ye Zhang, Jiahui Gong, Da Luo, Siqi Yang

{"title":"A GPU-based hydrodynamic numerical model for urban rainstorm inundation simulations","authors":"Hao Han, Jingming Hou, Zhao Jin, Pingping Luo, Guodong Li, Ye Zhang, Jiahui Gong, Da Luo, Siqi Yang","doi":"10.2166/hydro.2023.152","DOIUrl":"https://doi.org/10.2166/hydro.2023.152","url":null,"abstract":"The response capacities of urban flood forecasting and risk control can be improved by strengthening the computational abilities of urban flood numerical models. In this work, a GPU-based hydrodynamic model is developed to simulate urban rainstorm inundations. By simulating rainstorm floods in a certain area of Xixian New City, the established model can implement high-resolution urban rainstorm inundation simulations with significantly accelerated computing performances. The accelerated computation efficiencies of the different rainstorm event simulations under resolutions of 5 and 2 m are quantitatively analysed, showing that the absolute and relative speedup ratios for all scenarios of applying two GPUs range from 10.8 to 12.6 and 1.32 to 1.68 times as much as those of a CPU and a single GPU, respectively. The application of a large-scale rainstorm inundation simulation shows the excellent acceleration performance of the model compared to previous research. In addition, the greater the number of computational grids included in the simulation, the more significant the effect on the acceleration computing performance. The proposed model efficiently predicts the spatial variation in the inundation water depth. The simulation results provide guidance for urban rainstorm inundation management, and it improves the time and efficiency of urban flood emergency decision-making.","PeriodicalId":507813,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139249539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Study on wavelet multi-scale analysis and prediction of landslide groundwater 滑坡地下水的小波多尺度分析与预测研究

Journal of Hydroinformatics Pub Date : 2023-11-20 DOI: 10.2166/hydro.2023.299

Tian-long Wang, Dingmao Peng, Xu Wang, Bin Wu, Rui Luo, Zhao-wei Chu, Hong-yue Sun

{"title":"Study on wavelet multi-scale analysis and prediction of landslide groundwater","authors":"Tian-long Wang, Dingmao Peng, Xu Wang, Bin Wu, Rui Luo, Zhao-wei Chu, Hong-yue Sun","doi":"10.2166/hydro.2023.299","DOIUrl":"https://doi.org/10.2166/hydro.2023.299","url":null,"abstract":"Current groundwater prediction models often exhibit low accuracy and complex parameter adjustment. To tackle these limitations, a novel prediction model, called improved Aquila optimizer bi-directional long-term and short-term memory (IAO-BiLSTM) network, is proposed. IAO-BiLSTM optimizes the hyperparameters of the BiLSTM network using an IAO algorithm. IAO incorporates three novel enhancements, including population initialization, population updating, and global best individual updating, to overcome the drawbacks of current optimization algorithms. Before making predictions, the challenge posed by the highly nonlinear and non-stationary characteristics of groundwater level signals was addressed through the application of a wavelet multi-scale analysis method. Using a landslide site in Zhejiang Province as an example, a monitoring system is established, and continuous wavelet transform, cross-wavelet transform, and wavelet coherence analysis are employed to perform multi-scale feature analysis on a 2-year dataset of rainfall and groundwater depth. The findings reveal that the groundwater depth of monitoring holes exhibits similar high energy resonating periods and phase relationships, strongly correlating with rainfall. Subsequently, IAO-BiLSTM is employed to predict groundwater depth, and its results are compared with seven popular machine learning regression models. The results demonstrate that IAO-BiLSTM achieves the highest accuracy, as evidenced by its root mean squared error of 0.25.","PeriodicalId":507813,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139256776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On the operational optimization of pump storage systems in water supply systems using PATs and time-differentiated energy prices 利用 PAT 和时差能源价格对供水系统中的水泵蓄能系统进行运行优化

Journal of Hydroinformatics Pub Date : 2023-11-15 DOI: 10.2166/hydro.2023.191

Flávio Lourenço, Ana Luísa Reis, A. Andrade-Campos

{"title":"On the operational optimization of pump storage systems in water supply systems using PATs and time-differentiated energy prices","authors":"Flávio Lourenço, Ana Luísa Reis, A. Andrade-Campos","doi":"10.2166/hydro.2023.191","DOIUrl":"https://doi.org/10.2166/hydro.2023.191","url":null,"abstract":"Power generation from fossil fuels has long had a negative impact on the environment. Nowadays, a paradigm shift in power generation is being witnessed, with increasing investment in renewable energy sources. Despite this progress, efficient energy storage is still limited. Given this challenge, pumped storage technology can be one of the viable solutions. This involves storing gravitational energy by pumping water into a reservoir at a higher altitude, which is later converted into electrical energy using a turbine. This paper studies a pump hydro storage system (PHS) operation in water supply systems (WSSs), with the aim of minimizing operating costs and evaluating its effectiveness. Replacing conventional pumps with pump-as-turbines (PATs) provides a flexible and cost-effective approach. The proposed methodology aims to optimize the operation of these PATs considering dynamic energy prices. The developed computational model was applied to different operational scenarios and analyzed in terms of cost-effectiveness. The results show that the lower the average ratio between time-differentiated purchase and fixed sell energy tariffs, the greater the optimization potential of using PAT. In the WSS case study analyzed, energy cost reductions of 43.4–68.1% were achieved, demonstrating the effectiveness of PHS in WSS particularly for energy tariffs with large variations.","PeriodicalId":507813,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139272521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

EWT_Informer: a novel satellite-derived rainfall–runoff model based on informer EWT_Informer：基于informer的新型卫星降雨-径流模型

Journal of Hydroinformatics Pub Date : 2023-11-15 DOI: 10.2166/hydro.2023.228

Shuyu Wang, Yu Chen, Mohamed Ahmed

{"title":"EWT_Informer: a novel satellite-derived rainfall–runoff model based on informer","authors":"Shuyu Wang, Yu Chen, Mohamed Ahmed","doi":"10.2166/hydro.2023.228","DOIUrl":"https://doi.org/10.2166/hydro.2023.228","url":null,"abstract":"An accurate rainfall–runoff observation is critical for giving a warning of a potential damage early enough to allow appropriate response to the disaster. The long short-term memory (LSTM)-based rainfall–runoff model has been proven to be effective in runoff prediction. Previous research has typically utilized multiple information sources as the LSTM training data. However, when there are many sequences of input data, the LSTM cannot get nonlinear valid information between consecutive data. In this paper, a novel informer neural network using empirical wavelet transform (EWT) was first proposed to predict the runoff based only on the single rainfall data. The use of EWT reduced the non-linearity and non-stationarity of runoff data, which increased the accuracy of prediction results. In addition, the model introduced the Fractal theory to divide the rainfall and runoff into three parts, by which the interference caused by excessive data fluctuations could be eliminated. Using 15-year precipitation from the GPM satellite and runoff from the USGS, the model performance was tested. The results show that the EWT_Informer model outperforms the LSTM-based models for runoff prediction. The PCC and training time in EWT_Informer were 0.937, 0.868, and 1 min 3.56 s, respectively, while those provided by the LSTM-based model were 0.854, 0.731, and 4 min 25.9 s, respectively.","PeriodicalId":507813,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139275528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multivariate spatio-temporal modeling of drought prediction using graph neural network 利用图神经网络建立干旱预测的多变量时空模型

Journal of Hydroinformatics Pub Date : 2023-11-12 DOI: 10.2166/hydro.2023.134

Jiaxin Yu, Tinghuai Ma, Li Jia, Huan Rong, Yuming Su, M. M. A. Wahab

{"title":"Multivariate spatio-temporal modeling of drought prediction using graph neural network","authors":"Jiaxin Yu, Tinghuai Ma, Li Jia, Huan Rong, Yuming Su, M. M. A. Wahab","doi":"10.2166/hydro.2023.134","DOIUrl":"https://doi.org/10.2166/hydro.2023.134","url":null,"abstract":"Drought is a serious natural disaster that causes huge losses to various regions of the world. To effectively cope with this disaster, we need to use drought indices to classify and compare the drought conditions of different regions. We can take appropriate measures according to the category of drought to mitigate the impact of drought. Recently, deep learning models have shown promising results in this domain. However, these models few consider the relationships between different areas, which limits their ability to capture the complex spatio-temporal dynamics of droughts. In this study, we propose a novel multivariate spatio-temporal sensitive network (MSTSN) for drought prediction, which incorporates both geographical and temporal knowledge in the network and improves its predictive power. We obtained the standardized precipitation evapotranspiration index and meteorological data from the climatic research unit dataset, covering the period from 1961 to 2018. Specially, this is the first deep learning method that embeds geographical knowledge in drought prediction. We also provide a solid foundation for comparing our method with other deep learning baselines and evaluating their performance. Experiments show that our method consistently outperforms the existing state-of-the-art methods on various metrics, validating the effectiveness of geospatial and temporal information.","PeriodicalId":507813,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139279635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0