Example of Journal of Hydroinformatics format
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Example of Journal of Hydroinformatics format Example of Journal of Hydroinformatics format Example of Journal of Hydroinformatics format Example of Journal of Hydroinformatics format Example of Journal of Hydroinformatics format Example of Journal of Hydroinformatics format Example of Journal of Hydroinformatics format Example of Journal of Hydroinformatics format Example of Journal of Hydroinformatics format Example of Journal of Hydroinformatics format Example of Journal of Hydroinformatics format Example of Journal of Hydroinformatics format Example of Journal of Hydroinformatics format Example of Journal of Hydroinformatics format Example of Journal of Hydroinformatics format
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Example of Journal of Hydroinformatics format Example of Journal of Hydroinformatics format Example of Journal of Hydroinformatics format Example of Journal of Hydroinformatics format Example of Journal of Hydroinformatics format Example of Journal of Hydroinformatics format Example of Journal of Hydroinformatics format Example of Journal of Hydroinformatics format Example of Journal of Hydroinformatics format Example of Journal of Hydroinformatics format Example of Journal of Hydroinformatics format Example of Journal of Hydroinformatics format Example of Journal of Hydroinformatics format Example of Journal of Hydroinformatics format Example of Journal of Hydroinformatics format
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Journal of Hydroinformatics — Template for authors

Publisher: IWA Publishing
Categories Rank Trend in last 3 yrs
Geotechnical Engineering and Engineering Geology #58 of 195 down down by 22 ranks
Water Science and Technology #68 of 225 down down by 17 ranks
Civil and Structural Engineering #97 of 318 down down by 42 ranks
Atmospheric Science #57 of 124 down down by 13 ranks
journal-quality-icon Journal quality:
Good
calendar-icon Last 4 years overview: 327 Published Papers | 1215 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 23/09/2022
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FAQ

Related Journals

open access Open Access

Taylor and Francis

Quality:  
High
CiteRatio: 5.4
SJR: 0.95
SNIP: 1.307
open access Open Access

NRC Research Press

Quality:  
High
CiteRatio: 6.1
SJR: 2.032
SNIP: 2.259
open access Open Access

Springer

Quality:  
High
CiteRatio: 6.1
SJR: 1.292
SNIP: 1.67

Journal Performance & Insights

Impact Factor

CiteRatio

Determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

A measure of average citations received per peer-reviewed paper published in the journal.

1.728

9% from 2018

Impact factor for Journal of Hydroinformatics from 2016 - 2019
Year Value
2019 1.728
2018 1.908
2017 1.797
2016 1.634
graph view Graph view
table view Table view

3.7

6% from 2019

CiteRatio for Journal of Hydroinformatics from 2016 - 2020
Year Value
2020 3.7
2019 3.5
2018 3.5
2017 3.4
2016 3.6
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has decreased by 9% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has increased by 6% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

0.654

6% from 2019

SJR for Journal of Hydroinformatics from 2016 - 2020
Year Value
2020 0.654
2019 0.616
2018 0.665
2017 0.727
2016 0.73
graph view Graph view
table view Table view

1.039

16% from 2019

SNIP for Journal of Hydroinformatics from 2016 - 2020
Year Value
2020 1.039
2019 0.894
2018 1.194
2017 1.141
2016 1.021
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has increased by 6% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 16% in last years.
  • This journal’s SNIP is in the top 10 percentile category.
Journal of Hydroinformatics

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IWA Publishing

Journal of Hydroinformatics

Approved by publishing and review experts on SciSpace, this template is built as per for Journal of Hydroinformatics formatting guidelines as mentioned in IWA Publishing author instructions. The current version was created on 23 Sep 2022 and has been used by 674 authors to write and format their manuscripts to this journal.

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Last updated on
23 Sep 2022
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ISSN
1464-7141
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Sherpa RoMEO Archiving Policy
Yellow faq
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Plagiarism Check
Available via Turnitin
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Endnote Style
Download Available
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Bibliography Name
plainnat
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Citation Type
Author Year
(Blonder et al., 1982)
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Bibliography Example
G E Blonder, M Tinkham, and T M Klapwijk. Transition from metallic to tunneling regimes in superconducting microconstrictions: Excess current, charge imbalance, and supercurrent conversion. Phys. Rev. B, 25(7):4515– 4532, 1982.

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.2166/HYDRO.2008.015
Data-driven modelling: some past experiences and new approaches
Dimitri Solomatine1, Avi Ostfeld2

Abstract:

Physically based (process) models based on mathematical descriptions of water motion are widely used in river basin management. During the last decade the so-called data-driven models are becoming more and more common. These models rely upon the methods of computational intelligence and machine learning, and thus assume the p... Physically based (process) models based on mathematical descriptions of water motion are widely used in river basin management. During the last decade the so-called data-driven models are becoming more and more common. These models rely upon the methods of computational intelligence and machine learning, and thus assume the presence of a considerable amount of data describing the modelled system9s physics (i.e. hydraulic and/or hydrologic phenomena). This paper is a preface to the special issue on Data Driven Modelling and Evolutionary Optimization for River Basin Management, and presents a brief overview of the most popular techniques and some of the experiences of the authors in data-driven modelling relevant to river basin management. It also identifies the current trends and common pitfalls, provides some examples of successful applications and mentions the research challenges. read more read less
View PDF
519 Citations
open accessOpen access Journal Article DOI: 10.2166/HYDRO.2007.023
OpenMI: Open modelling interface
J. B. Gregersen1, P. J. A. Gijsbers, S. J. P. Westen

Abstract:

Management issues in many sectors of society demand integrated analysis that can be supported by integrated modelling. Since all-inclusive modelling software is difficult to achieve, and possibly even undesirable, integrated modelling requires the linkage of individual models or model components that address specific domains.... Management issues in many sectors of society demand integrated analysis that can be supported by integrated modelling. Since all-inclusive modelling software is difficult to achieve, and possibly even undesirable, integrated modelling requires the linkage of individual models or model components that address specific domains. Emerging from the water sector, the OpenMI has been developed with the purpose of being the glue that can link together model components from various origins. The OpenMI provides a standardized interface to define, describe and transfer data on a time basis between software components that run simultaneously, thus supporting systems where feedback between the modelled processes is necessary in order to achieve physically sound results. The OpenMI allows the linking of models with different spatial and temporal representations: for example, linking river models and groundwater models, where the river model typically uses a one-dimensional grid and a short timestep and the groundwater model uses a two- or three-dimensional grid and a longer timestep. The OpenMI is designed to accommodate the easy migration of existing modelling systems, since their re-implementation may not be economically feasible due to the large investments that have been put into the development and testing of these systems. read more read less

Topics:

Component-based software engineering (53%)53% related to the paper, Grid (51%)51% related to the paper
View PDF
368 Citations
open accessOpen access Journal Article DOI: 10.2166/HYDRO.2006.020
A symbolic data-driven technique based on evolutionary polynomial regression
Orazio Giustolisi1, Dragan Savic2

Abstract:

This paper describes a new hybrid regression method that combines the best features of conventional numerical regression techniques with the genetic programming symbolic regression technique. The key idea is to employ an evolutionary computing methodology to search for a model of the system/process being modelled and to emplo... This paper describes a new hybrid regression method that combines the best features of conventional numerical regression techniques with the genetic programming symbolic regression technique. The key idea is to employ an evolutionary computing methodology to search for a model of the system/process being modelled and to employ parameter estimation to obtain constants using least squares. The new technique, termed Evolutionary Polynomial Regression (EPR) overcomes shortcomings in the GP process, such as computational performance; number of evolutionary parameters to tune and complexity of the symbolic models. Similarly, it alleviates issues arising from numerical regression, including difficulties in using physical insight and over-fitting problems. This paper demonstrates that EPR is good, both in interpolating data and in scientific knowledge discovery. As an illustration, EPR is used to identify polynomial formulae with progressively increasing levels of noise, to interpolate the Colebrook-White formula for a pipe resistance coefficient and to discover a formula for a resistance coefficient from experimental data. read more read less

Topics:

Symbolic regression (66%)66% related to the paper, Polynomial regression (65%)65% related to the paper, Genetic programming (55%)55% related to the paper, Polynomial (54%)54% related to the paper, Evolutionary computation (53%)53% related to the paper
View PDF
343 Citations
open accessOpen access Journal Article DOI: 10.2166/HYDRO.2010.032
A hybrid model coupled with singular spectrum analysis for daily rainfall prediction
Kwok Wing Chau1, C.L. Wu1

Abstract:

A hybrid model integrating artificial neural networks and support vector regression was developed for daily rainfall prediction. In the modeling process, singular spectrum analysis was first adopted to decompose the raw rainfall data. Fuzzy C-means clustering was then used to split the training set into three crisp subsets wh... A hybrid model integrating artificial neural networks and support vector regression was developed for daily rainfall prediction. In the modeling process, singular spectrum analysis was first adopted to decompose the raw rainfall data. Fuzzy C-means clustering was then used to split the training set into three crisp subsets which may be associated with low-, medium- and high-intensity rainfall. Two local artificial neural network models were involved in training and predicting low- and medium-intensity subsets whereas a local support vector regression model was applied to the high-intensity subset. A conventional artificial neural network model was selected as the benchmark. The artificial neural network with the singular spectrum analysis was developed for the purpose of examining the singular spectrum analysis technique. The models were applied to two daily rainfall series from China at 1-day-, 2-day- and 3-day-ahead forecasting horizons. Results showed that the hybrid support vector regression model performed the best. The singular spectrum analysis model also exhibited considerable accuracy in rainfall forecasting. Also, two methods to filter reconstructed components of singular spectrum analysis, supervised and unsupervised approaches, were compared. The unsupervised method appeared more effective where nonlinear dependence between model inputs and output can be considered. read more read less

Topics:

Singular spectrum analysis (58%)58% related to the paper, Artificial neural network (54%)54% related to the paper, Support vector machine (52%)52% related to the paper
View PDF
273 Citations
open accessOpen access Journal Article DOI: 10.2166/HYDRO.2013.134
Improved annual rainfall-runoff forecasting using PSO-SVM model based on EEMD
Wen chuan Wang1, Dong mei Xu2, Dong mei Xu1, Kwok Wing Chau3, Shou-yu Chen2

Abstract:

Rainfall-runoff simulation and prediction in watersheds is one of the most important tasks in water resources management. In this research, an adaptive data analysis methodology, ensemble empirical mode decomposition (EEMD), is presented for decomposing annual rainfall series in a rainfall-runoff model based on a support vect... Rainfall-runoff simulation and prediction in watersheds is one of the most important tasks in water resources management. In this research, an adaptive data analysis methodology, ensemble empirical mode decomposition (EEMD), is presented for decomposing annual rainfall series in a rainfall-runoff model based on a support vector machine (SVM). In addition, the particle swarm optimization (PSO) is used to determine free parameters of SVM. The study data from a large size catchment of the Yellow River in China are used to illustrate the performance of the proposed model. In order to measure the forecasting capability of the model, an ordinary least-squares (OLS) regression and a typical three-layer feed-forward artificial neural network (ANN) are employed as the benchmark model. The performance of the models was tested using the root mean squared error (RMSE), the average absolute relative error (AARE), the coefficient of correlation ( R ) and Nash–Sutcliffe efficiency (NSE). The PSO–SVM–EEMD model improved ANN model forecasting (65.99%) and OLS regression (64.40%), and reduced RMSE (67.7%) and AARE (65.38%) values. Improvements of the forecasting results regarding the R and NSE are 8.43%, 18.89% and 182.7%, 164.2%, respectively. Consequently, the presented methodology in this research can enhance significantly rainfall-runoff forecasting at the studied station. read more read less
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209 Citations
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Journal of Hydroinformatics format uses plainnat citation style.

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Frequently asked questions

1. Can I write Journal of Hydroinformatics in LaTeX?

Absolutely not! Our tool has been designed to help you focus on writing. You can write your entire paper as per the Journal of Hydroinformatics guidelines and auto format it.

2. Do you follow the Journal of Hydroinformatics guidelines?

Yes, the template is compliant with the Journal of Hydroinformatics guidelines. Our experts at SciSpace ensure that. If there are any changes to the journal's guidelines, we'll change our algorithm accordingly.

3. Can I cite my article in multiple styles in Journal of Hydroinformatics?

Of course! We support all the top citation styles, such as APA style, MLA style, Vancouver style, Harvard style, and Chicago style. For example, when you write your paper and hit autoformat, our system will automatically update your article as per the Journal of Hydroinformatics citation style.

4. Can I use the Journal of Hydroinformatics templates for free?

Sign up for our free trial, and you'll be able to use all our features for seven days. You'll see how helpful they are and how inexpensive they are compared to other options, Especially for Journal of Hydroinformatics.

5. Can I use a manuscript in Journal of Hydroinformatics that I have written in MS Word?

Yes. You can choose the right template, copy-paste the contents from the word document, and click on auto-format. Once you're done, you'll have a publish-ready paper Journal of Hydroinformatics that you can download at the end.

6. How long does it usually take you to format my papers in Journal of Hydroinformatics?

It only takes a matter of seconds to edit your manuscript. Besides that, our intuitive editor saves you from writing and formatting it in Journal of Hydroinformatics.

7. Where can I find the template for the Journal of Hydroinformatics?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Journal of Hydroinformatics's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

8. Can I reformat my paper to fit the Journal of Hydroinformatics's guidelines?

Of course! You can do this using our intuitive editor. It's very easy. If you need help, our support team is always ready to assist you.

9. Journal of Hydroinformatics an online tool or is there a desktop version?

SciSpace's Journal of Hydroinformatics is currently available as an online tool. We're developing a desktop version, too. You can request (or upvote) any features that you think would be helpful for you and other researchers in the "feature request" section of your account once you've signed up with us.

10. I cannot find my template in your gallery. Can you create it for me like Journal of Hydroinformatics?

Sure. You can request any template and we'll have it setup within a few days. You can find the request box in Journal Gallery on the right side bar under the heading, "Couldn't find the format you were looking for like Journal of Hydroinformatics?”

11. What is the output that I would get after using Journal of Hydroinformatics?

After writing your paper autoformatting in Journal of Hydroinformatics, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is Journal of Hydroinformatics's impact factor high enough that I should try publishing my article there?

To be honest, the answer is no. The impact factor is one of the many elements that determine the quality of a journal. Few of these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for Journal of Hydroinformatics?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for Journal of Hydroinformatics. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In Journal of Hydroinformatics?

The 5 most common citation types in order of usage for Journal of Hydroinformatics are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

15. How do I submit my article to the Journal of Hydroinformatics?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Journal of Hydroinformatics's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download Journal of Hydroinformatics in Endnote format?

Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in Journal of Hydroinformatics Endnote style according to Elsevier guidelines.

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