In this study a thorough analysis is conducted concerning the prediction of groundwater levels of Ljubljana polje aquifer. Machine learning methodologies are implemented using strongly correlated physical parameters as input variables. The results show that data-driven modelling approaches can perform sufficiently well in predicting groundwater level changes. Different evaluation metrics confirm and highlight the capability of these models to catch the trend of groundwater level fluctuations. Despite the overall adequate performance, further investigation is needed towards improving their accuracy in order to be comprised in decision making processes.
Groundwater management is important for all urban systems. Thus, data needs to be available on request for various decision-makers and stakeholders. This article presents conceptual and implemented framework for collecting, analyzing and sharing of groundwater data for various purposes. It allows controlled access to data that is continuously collected from different feeds and transformed into a common format. With this approach, the latest data as well as historical records are always available for real-time queries and further analysis. The proposed system can be extended to cover other areas of data collection in the future.
To enable effective decision-making at the entire city level, both surface water and groundwater should be viewed as part of the extended urban water ecosystem with its spatiotemporal availability, quantity, quality and competing uses being taken into account. The Water4Cities project aims to build an ICT solution for the monitoring, visualization and analysis of urban water at a holistic urban setting to provide added-value decision support services to multiple water stakeholders. This paper presents the main stakeholders identified, the overall approach and the target use cases, where Water4Cities platform will be tested and validated.
This paper presents an architecture and a platform for processing of water management data in real time. Stakeholders in the domain are faced with the challenge of handling large amounts of incoming sensor data from heterogeneous sources after the digitalization efforts within the sector. Our water management analytical platform (WMAP) is built upon the needs of domain experts (it provides capabilities for offline analysis) and is designed to solve real-world problems (it provides real-time data flow solutions and data-driven predictive analytics) for smart water management. WMAP is expected to contribute significantly to the water management domain, which has not yet acquired the competences to implement extensive data analysis and modeling capabilities in real-world scenarios. The proposed architecture extends existing big data architectures and presents an efficient way of dealing with data-driven modeling in the water management domain. The main improvement is in the speed (online analytics) layer of the architecture, where we introduce heterogeneous data fusion in a set of data streams that provide real-time data-driven modeling and prediction services. Using the proposed architecture, the results illustrate that models built with datasets with richer contextual information and multiple data sources are more accurate and thus more useful.
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