**4. Conclusions**

This Special Issue provided a number of modern-day examples of decision support capabilities directed toward improving environmental water quality. The collection of papers in this Special Issue suggest a wide range of issues and regulatory means that can make good use of current state-of-the-art achievements in DSTs and DSSs to improve the policy regulation of deteriorated water quality.

These examples embrace the use of state-of-the-art computer-aided technologies, in particular the use of remote sensing and machine learning, both of which dominate research applications in this field of endeavor. The DSTs derived from the applications of these technologies provide direct and actionable links between scientific research and practice, and this Special Issue highlights how such linkages can be achieved in programmatic, thematic and operational ways. However, despite the excitement generated from these activities, we should not lose sight of the important need to support and carry out basic data

collection and data quality assurance activities, as well as the need to strive toward greater dissemination and our ability to share these data. Assessment and interpretation techniques count for nothing if they are based on flawed and inadequate background data. These activities need to march forward in lockstep with advances in decision support capabilities.

**Author Contributions:** Conceptualization, writing—review and editing, N.W.T.Q., A.D. and V.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Acknowledgments:** The co-guest editors would like to thank all of the authors, reviewers, and editors for their contributions. We are also grateful to the *Water* journal staff for their help and guidance in this Special Issue on "Decision Support Tools for Water Quality Management".

**Conflicts of Interest:** The authors declare no conflict of interest.
