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 Articles will not be published until the final proofs are validated by their authors.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.
Articles 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.
International Journal of Water (5 papers in press)
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.
The current situation of water resources and future feasible plans in Taiwan by YuFen Chen, Ching-Hua Mao Abstract: Taiwan has been experiencing a shortage of water, and in 2021, Taiwan encountered the most severe drought in the past 56 years. In this study, the researcher mainly explored the current situation, problems, and importance of water resources in Taiwan by citing statistics in the present databases. Through focusing on the unbalanced distribution and shortage of water resources, the researcher probed into the main causes of the water shortage to propose solutions. The researcher attempted to find solutions through the accessibility of water resources, the provision of effective water resources, renewable water resources per capita, and comparisons of water rates in Taiwan with those of other countries. Keywords: water resources; water shortage; renewable water resources per capita; water rates; Taiwan.
Assessing the impact of meteorological parameters for forecasting floods in the northern districts of Bihar using machine learning by Vikas Mittal, T.V. Vijay Kumar, Aayush Goel Abstract: India is the second largest flood-affected country in the world. Every year, floods have a deleterious effect on people, agriculture and infrastructure. Owing to its high population density and poor infrastructure, the damage caused by floods in India is exacerbated thus forcing millions of people to migrate from one place to other. Therefore, there is a need to device flood mitigation strategies that would forecast future floods in real time. In this paper, machine learning techniques have been used for forecasting floods in the northern districts of Bihar. Experimental results showed that, in addition to traditional meteorological parameters rainfall and temperature, certain parameters such as vapour pressure, cloud cover, wet day frequency, crop evapotranspiration and surface evapotranspiration have a severe impact on the performance of a flood-forecasting model. Keywords: natural hazards; floods; forecasting; artificial intelligence; machine learning; supervised learning; classification. DOI: 10.1504/IJW.2022.10047522
Influence of climate type on the predictive capabilities of stochastic models applied to monthly dam inflows by Leila Benchaiba, Larbi Houichi, Hocine Amarchi Abstract: This contribution assesses the predictive capacity of monthly inflow of two stochastic models called ARIMA and TBATS and highlights the influence of climate type on their performances. These are the inflows to three dams in three distinct climates: semi-arid, subhumid and humid. The actual inflows are deducted from the water balance equation for 132-month period. The first ten corresponding years of each series are used for training of the two models and the last one is then used for test. Model performances are evaluated using three commonly used metrics: the square root of the mean square error (RMSE), the mean of the absolute errors (MAE), and the mean absolute error in percentage (MAPE). The results show that the TBATS model performs better than the ARIMA model and its predictive capabilities decrease depending on whether the climate is semi-arid, sub-humid and humid (MAPE = 50.47%, 34.79% and 29.99%, respectively). Keywords: ARIMA; TBATS; climate type; forecast; monthly dam inflows; stochastic models; time series; predictive capabilities.
Water management technologies using Industry 4.0 tools by Chuks Medoh, Arnesh Telukdarie Abstract: The fourth industrial revolution facilitates the realization of integrated ICT technologies as tools for water management. The key challenge is the design of the multilayer network, including enterprise (and in some cases inter-enterprise), operational level (known as the Manufacturing Execution Systems), and the sensor network (known as the control network). The secondary considerations are the testing and integration of a variety of technologies into this network. This paper propositions a global best practice, hierarchical network of systems for data optimisation and total digitalisation of the water sector. This provides for technology integration by testing and integrating three control network systems integrated into the multilayer network. The system impacts, data impacts, and overall business automation as a result of the integration of automated data is illustrated as results. The digital impacts of the technologies are modelled and illustrated as outputs. Keywords: digitalisation; fourth industrial revolution; water resources planning; water management.