Development of a GIS-Based Decision Support System for Diagnosis of River System Health and Restoration

An Observational Process Ontology-Based Modeling Approach for Water Quality Monitoring

The increasing deterioration of aquatic environments has attracted more attention to water quality monitoring techniques, with most researchers focusing on the acquisition and assessment of water quality data, but seldom on the discovery and tracing of pollution sources. In this study, a semantic-enhanced modeling method for ontology modeling and rules building is proposed, which can be used for river water quality monitoring and relevant data observation processing. The observational process ontology (OPO) method can describe the semantic properties of water resources and observation data. In addition, it can provide the semantic relevance among the different concepts involved in the observational process of water quality monitoring. A pollution alert can be achieved using the reasoning rules for the water quality monitoring stations. In this study, a case is made for the usability testing of the OPO models and reasoning rules by utilizing a water quality monitoring system. The system contributes to the water quality observational monitoring process and traces the source of pollutants using sensors, observation data, process models, and observation products that users can access in a timely manner.

Three-Phase-Based Approach to Develop a River Health Prediction and Early Warning System to Guide River Management

To effectively manage a river system, systematic tracking and diagnosing the change and risks of a river system are essentially required to efficiently conserve or restore its conditions. Hence, this study focuses on how to integrate current status assessment, trend prediction, and cause diagnosis in river health to guide early warning decision-making in river protection and management. This study has presented a three-phase approach by coupling spatial with nonspatial information in a highly systematic and reliable way, and an early warning system has been designed. In phase I, the current health status is assessed and nowcasted by using the order degree of each indicator. In phase II, health predictors, including the single perspective-based health index (HI) (e.g., water quality index (WQI) and index of biotic integrity (IBI)) and multi-perspective-based health index, have been forecasted under normal conditions or emerging conditions using predictive models. In phase III, key causal factors threatening the river health have been identified to enable early notification and to address unexpected events before occurrence. Although different modeling methods can be used in each phase to demonstrate this concept, we tested the model of partial least square regression (PLSR) associated with time series. Additionally, the three-phase approach has been integrated with geographic information system (GIS) and a decision support system (DSS) to develop a river health prediction and early warning system (RHP-EWS), an automatic prediction and decision-making tool. This tool was implemented to deal with the landing of typhoon “Maria” in 2018 into the Shanxi River watershed in China. Because of the timely responses and decisions, the drinking water supply was not influenced. However, the models should be extended to other river systems for testing and improvement at different temporal or spatial scales.