Journal of Hydroinformatics最新文献

{"title":"Discharge estimation in compound channels with converging and diverging floodplains an using an optimised Gradient Boosting Algorithm","authors":"Shashank Shekhar Sandilya, Bhabani Shankar Das, Dr. Sébastien Proust, Divyanshu Shekhar","doi":"10.2166/hydro.2024.292","DOIUrl":"https://doi.org/10.2166/hydro.2024.292","url":null,"abstract":"\u0000 River discharge estimation is vital for effective flood management and infrastructure planning. River systems consist of a main channel and floodplains, collectively forming a compound channel, posing challenges in discharge calculation, particularly when floodplains converge or diverge. Numerical models for discharge prediction require the solution of complex non-linear equations while traditional approaches often yield unreliable results with significant errors. To solve these complex non-linear problems, various machine learning (ML) approaches becoming popular. In the present study, ML algorithms, such as XGBoost, CatBoost and LightGBM, were developed to predict discharge in a compound channel. The PSO algorithm is applied for the optimisThe eesults show that all three gradient boosting algorithms effectively predict discharge in compound channels and are further enhanced by the application of the PSO algorithm. The R2 values for XGBoost, PSO-XGBoost, CatBoost and PSO-CatBoost exceed 0.95, whereas they are above 0.85 for LightBoost and PSO-LightBoost.The findings of this study validate the suitability of the proposed models, especially optimised with PSO is recommended for predicting discharge in a compound channel.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140717639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hydrovars: an R tool to collect hydrological variables","authors":"Alejandro Sánchez-Gómez, Katrin Bieger, Christoph Schürz, S. Martínez-Pérez, Hendrik Rathjens, Eugenio Molina-Navarro","doi":"10.2166/hydro.2024.293","DOIUrl":"https://doi.org/10.2166/hydro.2024.293","url":null,"abstract":"\u0000 \u0000 Hydrological models can benefit from soft calibration, a process by which the proper simulation of hydrological variables is proved while or before addressing hard calibration. Soft calibration reduces the probability of obtaining a statistically accurate but unrealistic model. However, it requires soft data, which is often hard to acquire or unavailable. This work presents HydRoVars, an R tool developed to facilitate the estimation of data which can be implemented in a soft calibration procedure. It allows to estimate two key hydrological indices (the runoff coefficient and the baseflow index) and weather-related variables at the catchment scale for one or numerous basins. The runoff coefficient is calculated automatically from precipitation and streamflow datasets. Groundwater contribution is estimated through a semi-automatic process based on a baseflow filter which considers hydrogeological properties. Modellers would benefit from incorporating soft calibration in their calibration procedures, and this tool might help to estimate these relevant hydrological variables in their modelled area. The tool has been tested in 19 subbasins of the Tagus River basin (Spain) located in different geological regions. In the test cases, we demonstrate the usefulness of this tool to improve the model representation and gain an understanding of the catchments' hydrology.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140722937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Utilizing waveform synthesis in harmonic oscillator seasonal trend model for short- and long-term streamflow drought modeling and forecasting","authors":"K. Raczyński, J. Dyer","doi":"10.2166/hydro.2024.229","DOIUrl":"https://doi.org/10.2166/hydro.2024.229","url":null,"abstract":"\u0000 This study introduces an improved version of the harmonic oscillator seasonal trend (HOST) model framework to accurately simulate medium- and long-term changes in extreme events, focusing on streamflow droughts in the Mobile River catchment. Performance of the model relative to the initial framework was enhanced through the inclusion of new mathematical models and waveform synthesis. The updated framework successfully captures long-term and seasonal patterns with a Kling–Gupta efficiency exceeding 0.5 for seasonal fluctuations and over 0.9 for trends. The best-fit model explains around 98% of long-term and approximately 55% of seasonal variance. Test sets show slightly lower accuracies, with about 20% of nodes underperforming due to the absence of drought during the test phase resulting in false-positive model forecasts. The newly developed weighted occurrence classification outperforms the binary classification occurrence model. In addition, application of an automatic period multiplier for decomposition using the seasonal trend decomposition using LOESS method improves test dataset performance and reduces false-positive forecasts. The improved framework provides valuable insights for extreme flow distribution, offering potential for improved water management planning, and the combination of the HOST model with physical models can address short-term drivers of extreme events, enhancing drought occurrence forecasting and water resource management strategies.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140729282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning approaches for anomalous storm pattern identification","authors":"David Sharp, A. P. Barnes","doi":"10.2166/hydro.2024.238","DOIUrl":"https://doi.org/10.2166/hydro.2024.238","url":null,"abstract":"\u0000 Anomaly detection is used to explore the link between data-driven anomalous storms and their socio-economic impact on countries within the North-West Pacific. Three anomaly detection models are trialled using three distinct algorithms on the storm tracks and temperature profiles of storms. A feature-based comparison of the top 5% of anomalous storms from each model is used to reveal variations in anomalous storm activity. Further to this, the socio-economic impact of the anomalous storms is assessed, revealing a link between the anomalous behaviour of storms and the impact experienced by countries on their path. A final cross-comparison shows that the k-Nearest Neighbour and Isolation Forest algorithms succeeded at identifying high-impacting storms. However, the agglomerative clustering model found many unique storms that had low impact. This highlights the importance of considering both trajectory and temperature in determining the severity and impact of erroneous storms.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140735690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of the discharge capacity of piano key weirs using artificial neural networks","authors":"Mujahid Iqbal, Usman Ghani","doi":"10.2166/hydro.2024.303","DOIUrl":"https://doi.org/10.2166/hydro.2024.303","url":null,"abstract":"\u0000 The discharge capacity of the piano key weir (PKW) is an important flow feature which ultimately decides the type and geometric design of PKWs. In the present research work, the different architecture of artificial neural networks (ANNs) was employed to predict the discharge capacity of the trapezoidal piano key weir (TPKW) by varying geometric parameters (Si/So, Wi/Wo, Bi/Bo, L/W and α). Furthermore, adaptive neuro-fuzzy interference system (ANFIS), support vector machines (SVMs) and non-linear regression (RM) techniques were also applied to compare the performance of best ANN models. The performance of each model was evaluated using statistical indices including scatter index (SI); coefficient of determination (R2), and mean square error (MSE). The prediction capability of all the models was found to be satisfactory. However, results predicted by ANN-22(H-15) [R2 = 0.998, MSE = 0.0024, SI = 0.0177] was more accurate than ANFIS (R2 = 0.995, MSE = 0.00039, SI = 0.0256), SVM (R2 = 0.982, MSE = 0.000864, SI = 0.0395) and RM (R2 = 0.978, MSE = 0.001, SI = 0.0411). It was observed that Si/So, Wi/Wo and L/W geometric ratios have the greatest effect on the discharge performance of TPKW. Furthermore, sensitivity analysis confirmed that h/P is the most influencing ratio which may considerably affect the discharge efficiency of the TPKW. It was found that ANN models having a single hidden layer and keeping neurons three times of input parameters in hidden layers generated better results.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140739246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Definition and application of the Péclet number threshold for water quality analysis in water distribution networks","authors":"Stefania Piazza, Mariacrocetta Sambito, Gabriele Freni","doi":"10.2166/hydro.2024.102","DOIUrl":"https://doi.org/10.2166/hydro.2024.102","url":null,"abstract":"\u0000 To assess the water quality within the distribution networks, simplified models are used, which adopt an advective–reactive approach and neglect diffusion–dispersion phenomena. Although such simplifications can be sufficiently accurate in complete turbulent uniform flow regimes, literature works demonstrated that they could produce wrong results in laminar and transitional regimes that are relevant when analysing low flows, dead-end pipes in looped distribution networks or service connections. On the other hand, advective simplification allows for considerable computational savings during the simulation of large networks. Therefore, a criterion is needed for better discriminate pipes in which the advective approach is sufficient or the diffusive approach is required. The present study aims to investigate the use of the Péclet number to discriminate the use of advective simplification both adopting the two-dimensional (2D) advection–dispersion equation and the one-dimensional (1D) cross-section averaged advection–dispersion equation. The numerical analysis was applied to a linear pipeline using the EPANET, 1D advective–dispersive–reactive, and EPANET-DD (Dynamic–Dispersion) models. The results showed the inadequacy of the Péclet number in discriminating the dominance of the advective–dispersive process in real systems, as it is linked to the pipe's length, regardless of the flow regime occurring on the pipeline.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140738491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning for automated trash screen blockage detection using cameras: Actionable information for flood risk management","authors":"Rémy Vandaele, Sarah Lance Dance, Varun Ojha","doi":"10.2166/hydro.2024.013","DOIUrl":"https://doi.org/10.2166/hydro.2024.013","url":null,"abstract":"\u0000 \u0000 Trash screens are used to prevent debris from entering critical parts of rivers. However, debris can accumulate on the screen and generate floods. This makes their monitoring critical both for maintenance and flood modeling purposes (e.g., local forecasts may change because the trash screen is blocked). We developed three novel deep learning methods for trash screen maintenance management consisting of automatically detecting trash screen blockage using cameras: a method based on image classification, a method based on image similarity matching, and a method based on anomaly detection. To facilitate their use by end users, these methods are designed so that they can be directly applied to any new trash screen camera installed by the end users. We have built a new dataset of labeled trash screen images to train and evaluate the efficiency of our methods, in terms of both accuracy and implications for end users. This dataset consists of 80,452 trash screen images from 54 cameras installed by the Environment Agency (UK). This work demonstrates that trash screen blockage detection can be automated using trash screen cameras and deep learning, which could have an impact on both trash screen management and flood modeling.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140769080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Water quality emergency monitoring networks: a method for identifying non-critical variables based on Shannon's entropy","authors":"Fábio Cruz, Talita Fernanda das Graças Silva","doi":"10.2166/hydro.2024.256","DOIUrl":"https://doi.org/10.2166/hydro.2024.256","url":null,"abstract":"\u0000 \u0000 In the occurrence of environmental disasters involving water resources, deploying an emergency monitoring network for assessing water quality is within the first measures to be taken. Emergency networks usually cover a large set of water quality variables and monitoring stations along the watershed. Focusing on variables that represent greater risk to the environment and have less predictable spatial and temporal distribution is a strategy to optimize efforts on monitoring. The goal of this study is to assess the use of Shannon's entropy to identify non-critical water quality variables in an emergency monitoring network implemented in a watershed impacted by the collapse of a mining iron tailing dam, the Doce River watershed (Brazil). Monitoring stations were grouped into water quality subregions through cluster analysis and Shannon's entropy was used to estimate information redundancy of monitored variables. From information redundancy and after checking for compliance with environment normative, non-critical water quality variables were identified. Results indicated that non-critical variables represent 32–50% of the variables monitored. Emergency network managers find in this method a robust tool to improve the network performance. However, special attention should be paid to outliers' presence that can bias analyses based on Shannon's entropy.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140082430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of characteristic index and prediction of river bottom tearing scour in the Yellow River","authors":"Longfei Sun, Yanhui Liu, Yuanjian Wang, Qinghao Dong, Wanjie Zhao","doi":"10.2166/hydro.2024.247","DOIUrl":"https://doi.org/10.2166/hydro.2024.247","url":null,"abstract":"<div><div data- reveal-group-><div><img alt=\"graphic\" data-src=\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/jh/26/3/10.2166_hydro.2024.247/2/m_hydro-d-23-00247gf01.png?Expires=1714738699&amp;Signature=PJnFubWAYWjMkyJaokTkc4qSRYjm4eWNYjpTIP1GH7R~jo7MfFn-Ls8KecoZNPNIt0GhaJkoFCsLePZzT0-HdoZDMGyjNAvX9phNB9m226KgoyPpYr54aduvWKES3lKt7u8leKHhTk9bFdnGybok1Q~Y3DbB-ih6JGVuaK3EHfSKV8vtt-ISfa4bR8eNtxOEHMSE8~1H4XA65odwmBqUkLKL6JwywQArhRQLfu1QBk~E5Bv08l6iaDSmFTFrqT1W5vTIIf0L9h2IHhhSphH1LNokg26bxMgDJYgF4EZogjzK56K648SPhhbosgr5bYm-kJm36umew7pShhOMImT0Eg__&amp;Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\" path-from-xml=\"hydro-d-23-00247gf01.tif\" src=\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/jh/26/3/10.2166_hydro.2024.247/2/m_hydro-d-23-00247gf01.png?Expires=1714738699&amp;Signature=PJnFubWAYWjMkyJaokTkc4qSRYjm4eWNYjpTIP1GH7R~jo7MfFn-Ls8KecoZNPNIt0GhaJkoFCsLePZzT0-HdoZDMGyjNAvX9phNB9m226KgoyPpYr54aduvWKES3lKt7u8leKHhTk9bFdnGybok1Q~Y3DbB-ih6JGVuaK3EHfSKV8vtt-ISfa4bR8eNtxOEHMSE8~1H4XA65odwmBqUkLKL6JwywQArhRQLfu1QBk~E5Bv08l6iaDSmFTFrqT1W5vTIIf0L9h2IHhhSphH1LNokg26bxMgDJYgF4EZogjzK56K648SPhhbosgr5bYm-kJm36umew7pShhOMImT0Eg__&amp;Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\"/><div>View largeDownload slide</div></div></div><div content- data-reveal=\"data-reveal\"><div><img alt=\"graphic\" data-src=\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/jh/26/3/10.2166_hydro.2024.247/2/m_hydro-d-23-00247gf01.png?Expires=1714738699&amp;Signature=PJnFubWAYWjMkyJaokTkc4qSRYjm4eWNYjpTIP1GH7R~jo7MfFn-Ls8KecoZNPNIt0GhaJkoFCsLePZzT0-HdoZDMGyjNAvX9phNB9m226KgoyPpYr54aduvWKES3lKt7u8leKHhTk9bFdnGybok1Q~Y3DbB-ih6JGVuaK3EHfSKV8vtt-ISfa4bR8eNtxOEHMSE8~1H4XA65odwmBqUkLKL6JwywQArhRQLfu1QBk~E5Bv08l6iaDSmFTFrqT1W5vTIIf0L9h2IHhhSphH1LNokg26bxMgDJYgF4EZogjzK56K648SPhhbosgr5bYm-kJm36umew7pShhOMImT0Eg__&amp;Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\" path-from-xml=\"hydro-d-23-00247gf01.tif\" src=\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/jh/26/3/10.2166_hydro.2024.247/2/m_hydro-d-23-00247gf01.png?Expires=1714738699&amp;Signature=PJnFubWAYWjMkyJaokTkc4qSRYjm4eWNYjpTIP1GH7R~jo7MfFn-Ls8KecoZNPNIt0GhaJkoFCsLePZzT0-HdoZDMGyjNAvX9phNB9m226KgoyPpYr54aduvWKES3lKt7u8leKHhTk9bFdnGybok1Q~Y3DbB-ih6JGVuaK3EHfSKV8vtt-ISfa4bR8eNtxOEHMSE8~1H4XA65odwmBqUkLKL6JwywQArhRQLfu1QBk~E5Bv08l6iaDSmFTFrqT1W5vTIIf0L9h2IHhhSphH1LNokg26bxMgDJYgF4EZogjzK56K648SPhhbosgr5bYm-kJm36umew7pShhOMImT0Eg__&amp;Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\"/><div>View largeDownload slide</div></div><i> </i><span>Close modal</span></div></div><p>River bottom tearing scour (RBTS) has a strong effect on the scouring and moulding of channel in the Yellow River. Due to the special forming conditions, complex influencing factors, and limited observed data, it is difficult to predict whether RBTS will occur accurately. By collecting and disposing of the hydrodynamic, sediment, and initial boundary data of 246 flood events related to RBTS in three typical reaches of the Yellow River basin, the correlation between different characteristi","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140322433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding the impact of population dynamics on water use utilizing multi-source big data","authors":"Guihuan Zhou, Zhanjie Li, Wei Wang, Qianyang Wang, Jingshan Yu","doi":"10.2166/hydro.2024.179","DOIUrl":"https://doi.org/10.2166/hydro.2024.179","url":null,"abstract":"<p>Population movement, such as commuting, can affect water supply pressure and efficiency in modern cities. However, there is a gap in the research concerning the relationship between water use and population mobility, which is of great significance for urban sustainable development. In this study, we analyzed the spatial–temporal dynamics of the population and its underlying mechanisms, using multi-source geospatial big data, including Baidu heat maps (BHMs), land use parcels, and point of interest. Combined with water consumption, sewage volume, and river depth data, the impact of population dynamics on water use was investigated. The results showed that there were obvious differences in population dynamics between weekdays and weekends with a ratio of 1.11 for the total population. Spatially, the population concentration was mainly observed in areas associated with enterprises, industries, shopping, and leisure activities during the daytime, while at nighttime, it primarily centered around residential areas. Moreover, the population showed a significant impact on water use, resulting in co-periods of 24 h and 7 days, and the water consumption as well as the wastewater production were observed to be proportional to the population density. This study can offer valuable implications for urban water resource allocation strategies.</p>","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140322494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}