and by the number of stakeholders concerned to utilise and preserve the environment as a sustainable resource. Any hydroinformatics system will reflect something of the complexity of the processes and demands of the stakeholders. An important function of a hydroinformatics system is its ability to support its users

This research proposes system models for assessing effects of social changes on hydrological cycle in river basins, as well as water movement and balance especially among Asian countries which are expected to be still dynamically developing with population and economical growths, industrial

Open Source, in which the source code to software is freely shared and improved upon, has recently risen to prominence as an alternative to the more usual closed approach to software development. A number of high profile projects, such as the Linux operating system kernel and the Apache web server

Hydroinformatics is a sociotechnical endeavour, which is to say that it deals with social processes that cannot proceed without the provision of appropriate technologies and technologies that cannot succeed without the introduction of appropriate social arrangements. In particular, the introduction

The developing insights of hydroinformatics have much to offer the water industry, and particularly urban storm and wastewater drainage. This paper reviews aspects of data mining and knowledge discovery of large asset databases, the complementary nature of both physically based and data

Prototype hydroinformatics-based system for supporting decision making in culvert design and monitoring

decision support systems for impact prediction and for communicating information to multiple stakeholders having quite different interests and concerns—all in an effort to obtain some consensus or shared visions of what should be done, and why—this project provides one.

Hydrology is developing its next generation of software for the conduct of the National Weather

processes and ecological responses in river systems over time. Increasing deployment of affordable sensors, however, is beginning to provide information to fill such gaps, but their utility remains constrained by human interpretation. We developed “hydrolapse videography ” – a digital, time

and ordinary least-squares regression have under-performed because of the system’s non-linear dynamics. This paper presents an alternative approach that employs artificial neural network (ANN) models to perform multivariate, non-linear interpolation between gauging stations. Using a combination of static