Forthcoming and Online First Articles

International Journal of Water

International Journal of Water (IJW)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

Forthcoming articles must be purchased for the purposes of research, teaching and private study only. These articles can be cited using the expression "in press". For example: Smith, J. (in press). Article Title. Journal Title.

Articles marked with this shopping trolley icon are available for purchase - click on the icon to send an email request to purchase.

Online First articles are published online here, before they appear in a journal issue. Online First articles are fully citeable, complete with a DOI. They can be cited, read, and downloaded. Online First articles are published as Open Access (OA) articles to make the latest research available as early as possible.

Open AccessArticles marked with this Open Access icon are Online First articles. They are freely available and openly accessible to all without any restriction except the ones stated in their respective CC licenses.

Register for our alerting service, which notifies you by email when new issues are published online.

We also offer which provide timely updates of tables of contents, newly published articles and calls for papers.

International Journal of Water (3 papers in press)

Regular Issues

  • Application of support vector machine for river flow estimation
    by Hasan Torabi, Reza Dehghani 
    Abstract: In recent years, the application of intelligent methods has been considered in forecasting hydrological processes. In this research, montlyh river discharge of the Kakareza, a river located in Lorestan province in the west of Iran, was forecast using support vector machine and as genetic programming inference system methods in Dehno stations. In this regard, some different combinations in the period 1979-2015 as input data for estimation of discharge in the monthly index were evaluated. Criteria of correlation coefficient, root mean square error and Nash Sutcliff coefficient to evaluate and compare the performance of methods were used. It showed that a combined structure using surveyed inelegant methods, resulted in an acceptable estimation of discharge to the Kakareza river. In addition, comparison between models shows that support vector machine has a better performance than other models in inflow estimation. In terms of accuracy, support vector machine with correlation coefficients (0.970) has more propriety than root mean square error (0.08m3/s) and Nash Sutcliff (0.94). To sum up, it is mentioned that support vector machine method has a better capability to estimate the minimum, maximum and other flow values.
    Keywords: gene expression programming; Kakareza river; support vector machine.

  • Perceptions and choice of payment behaviour of consumers for rural piped water services in an eastern Indian state   Order a copy of this article
    by Tapasi Mohanty, Himanshu Sekhar Rout 
    Abstract: Poor invoicing and collection practices of rural water supply have an implication for the institution's financial sustainability. This research aims to explore the perceptions and choice of payment behaviour of consumers for rural pipe water services. The sample unit is rural household consumer. Descriptive statistics and multinomial logistic regression technique are used to find out the result. This study found that colour of the water and the interruption of supply are the main sources of consumer dissatisfaction. The concept of free water service, why should we contribute when others do not, is the main reason for non-payment. The consumers stated that reasons for connecting to piped water were found to be the key variable for all payment choices with respect to non-payment.
    Keywords: rural water services; consumer perception; water quantity and quality; revenue collection.

  • Marine water quality index classification and prediction using machine learning framework   Order a copy of this article
    by Komathy Karuppanan 
    Abstract: Quality of marine water has a direct impact on the evolution of the ocean ecosystem, which aggravates the survival of marine organisms and animals. Additionally, the ballast water, carried in ships drawn from the open sea/ocean, may lead to bio-invasion if the quality of the water is unchecked and released into another sea/ocean. The ballast water treatment methods may sometime alter the physicochemical properties, which might be harmful to receiving water region. The objective of this paper is, therefore, to propose a new method for the estimation of marine water quality from the physiochemical and biological properties of the marine water and then a suitable computational architecture supporting machine learning techniques to assist in classifying and predicting the marine water quality. The proposed framework has evaluated various classification models to select the best-fit algorithm for this application through model training and optimizing. The finalized model called stacking classifier was then recommended for ballast water quality prediction with 100% accuracy, which could be deployed prior to the ballast water exchange.
    Keywords: data modelling; machine learning; water quality index; water quality; model optimizing; model training; prediction model.