Experiences from online and classroom education in hydroinformatics

Universities and other higher education institutions involved in water-related engineering education are facing new challenges in offering lifelong learning services and online educational support. Both the curricula and the form of delivery are changing, as contemporary water problems require interdisciplinary approaches involving diverse and up to date expertise maintained via continuous professional development. Hydroinformatics education faces similar challenges in developing relevant curricula and finding appropriate combinations of course delivery to its target group. This article presents experiences from delivering two hydroinformatics courses in the fields of flood modelling for management (FMM) and decision support systems (DSS) in river basin management that in recent years have been delivered both online and in classroom settings. Comparisons between the two modes of delivery are provided, with the conclusion that online education in this field, although still faced with many challenges, has a promising potential for meeting future educational needs

Enhancement of urban pluvial flood risk management and resilience through collaborative modelling: a UK case study

This paper presents the main findings and lessons learned from the development and implementation of a new methodology for collaborative modelling, social learning and social acceptance of flood risk management technologies. The proposed methodology entails three main phases: (1) stakeholder analysis and engagement; (2) improvement of urban pluvial flood modelling and forecasting tools; and (3) development and implementation of web-based tools for collaborative modelling in flood risk management and knowledge sharing. The developed methodology and tools were tested in the Cranbrook catchment (London Borough of Redbridge, UK), an area that has experienced severe pluvial (surface) flooding in the past. The developed methodologies proved to be useful for promoting interaction between stakeholders, developing collaborative modelling and achieving social acceptance of new technologies for flood risk management. Some limitations for stakeholder engagement were identified and are discussed in the present paper

Hydroinformatics as sociotechnology: Promoting individual stakeholder participation by using network distributed decision support systems

Civil Engineering and Geoscience

Impact analysis of satellite rainfall products on flow simulations in the Magdalena River Basin, Colombia

The Magdalena River is the most important river in Colombia in terms of economic activities and is home to about 77% of the country’s population. The river faces water resources allocation challenges, which require reliable hydrological assessments. However, hydrological analysis and model simulations are hampered by insufficient and uncertain knowledge of the actual rainfall fields. In this research the reliability of groundbased measurements, different satellite products of rainfall and their combinations are tested for their impact on the discharge simulations of the Magdalena River. Two different satellite rainfall products from the Tropical Rainfall Measuring Mission (TRMM), have been compared and merged with the ground-based measurements and their impact on the Magdalena river flows quantified using the Representative Elementary Watershed (REW) distributed hydrological model

A Classification-Based Machine Learning Approach to the Prediction of Cyanobacterial Blooms in Chilgok Weir, South Korea

Cyanobacterial blooms appear by complex causes such as water quality, climate, and hydrological factors. This study aims to present the machine learning models to predict occurrences of these complicated cyanobacterial blooms efficiently and effectively. The dataset was classified into groups consisting of two, three, or four classes based on cyanobacterial cell density after a week, which was used as the target variable. We developed 96 machine learning models for Chilgok weir using four classification algorithms: k-Nearest Neighbor, Decision Tree, Logistic Regression, and Support Vector Machine. In the modeling methodology, we first selected input features by applying ANOVA (Analysis of Variance) and solving a multi-collinearity problem as a process of feature selection, which is a method of removing irrelevant features to a target variable. Next, we adopted an oversampling method to resolve the problem of having an imbalanced dataset. Consequently, the best performance was achieved for models using datasets divided into two classes, with an accuracy of 80% or more. Comparatively, we confirmed low accuracy of approximately 60% for models using datasets divided into three classes. Moreover, while we produced models with overall high accuracy when using logCyano (logarithm of cyanobacterial cell density) as a feature, several models in combination with air temperature and NO3-N (nitrate nitrogen) using two classes also demonstrated more than 80% accuracy. It can be concluded that it is possible to develop very accurate classification-based machine learning models with two features related to cyanobacterial blooms. This proved that we could make efficient and effective models with a low number of inputs

Experiences from online and classroom education in hydroinformatics

Universities and other higher education institutions involved in water-related engineering education are facing new challenges in offering lifelong learning services and online educational support. Both the curricula and the form of delivery are changing, as contemporary water problems require interdisciplinary approaches involving diverse and up to date expertise maintained via continuous professional development. Hydroinformatics education faces similar challenges in developing relevant curricula and finding appropriate combinations of course delivery to its target group. This article presents experiences from delivering two hydroinformatics courses in the fields of flood modelling for management (FMM) and decision support systems (DSS) in river basin management that in recent years have been delivered both online and in classroom settings. Comparisons between the two modes of delivery are provided, with the conclusion that online education in this field, although still faced with many challenges, has a promising potential for meeting future educational needs

Model-aided design and optimization of artificial recharge-pumping systems

This paper presents a methodology for the design and optimization of artificial recharge-pumping systems (ARPS). The objective of ARPS is to provide a maximum abstraction rate through artificial recharge, while meeting two operational constraints: (a) the influences of the system operation on groundwater levels should be no more than 25 mm in the vicinity of the system; and (b) the travel time of the infiltrated water from the recharge pond to the pumping wells should be more than 60 days. The combined use of a 3-dimensional generic groundwater simulation model with particle tracking analyses has identified the two best ARPS systems: the circular pond system and the island system. By coupling the simulation model with linear and mixed integer programming optimization, the optimal pumping scheme (number, locations and rates of the pumping wells) has been determined. An unsteady state model has been used to simulate the response of the operation of the two systems under natural seasonal variations. The implementation aspects of the two systems are compared

Simulation-optimization approach for evaluating the feasibility of managed aquifer recharge in the Samail Lower Catchment, Oman

This article presents a simulation-optimization approach for evaluating the feasibility of managed aquifer recharge (MAR) in the Samail Lower Catchment, Oman. The objective is to provide a maximum recharge and extraction rate through MAR in an annual cycle of two successive injection and recovery periods, while meeting operational and system constraints such as water level, gradient, and travel time. Three groundwater management problems were solved by coupling a simulation model with successive linear programming (SLP) and the nondominated sorting genetic algorithm (NSGA-II) multiobjective genetic algorithm. Sensitivity analysis was also completed to examine the overall response of the simulation-optimization results to changes in hydraulic conductivities and maximum injection rates. Results using the SLP algorithm showed that the total volume of injected water for 4 months of injection without recovery is as high as 8 × 106 m3, and the total recovered volume of water for 4months injection and 8 months recovery is approximately 5.3 × 106 m3, giving a total recovery efficiency of approximately 66%. For the same setup the NSGA-II algorithm derived the entire nondominated front of solutions for two conflicting objectives: maximizing recovery rate and maximizing minimum groundwater head close to the sea (for preventing seawater intrusion). This algorithm includes travel time constraints directly in the optimization process. In conclusion, the proposed approach provides a cost-effective means to evaluate MAR in a coastal aquifer

Decision Support System for Daily and Long Term Operations of the System of Milan, Italy

This study introduces the development of a Decision Support System (DSS) for daily and long term operations of the Water Distribution Network of Milan operated by the utility Metropolitana Milanese S.p.A (MM), developed during the European project ICeWater. The DSS has been developed for the two main problems of the utility by applying multi-objective optimization for pump scheduling and sectorization of the system. The DSS was built based on open source software on the server and the client side, making its applicability to other utilities possible. This paper presents the architecture of the DSS components and shows the advantages in the application of such tool in the operation for MM. A test for validation of the DSS for pump scheduling has been applied in a subsector named Abbiategrasso. Some results are presented showing the benefits for the utility by using the DSS in their daily operations