An intelligent analytics system for real-time catchment regulation and water management

Regulation procedures and water management that incorporate projected hydrological changes with related uncertainties become extremely important in order to prevent degradation of water ecosystems. Ensuring real time water management and optimization becomes mandatory for resolving the constraints of water supply/demand and to comply with biodiversity requirements

Enhancing Operation of a Sewage Pumping Station for Inter Catchment Wastewater Transfer by Using Deep Learning and Hydraulic Model

This paper presents a novel Inter Catchment Wastewater Transfer (ICWT) method for mitigating sewer overflow. The ICWT aims at balancing the spatial mismatch of sewer flow and treatment capacity of Wastewater Treatment Plant (WWTP), through collaborative operation of sewer system facilities. Using a hydraulic model, the effectiveness of ICWT is investigated in a sewer system in Drammen, Norway. Concerning the whole system performance, we found that the S{\o}ren Lemmich pump station plays a vital role in the ICWT framework. To enhance the operation of this pump station, it is imperative to construct a multi-step ahead water level prediction model. Hence, one of the most promising artificial intelligence techniques, Long Short Term Memory (LSTM), is employed to undertake this task. Experiments demonstrated that LSTM is superior to Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), Feed-forward Neural Network (FFNN) and Support Vector Regression (SVR)

Integrating building and urban semantics to empower smart water solutions

Current urban water research involves intelligent sensing, systems integration, proactive users and data-driven management through advanced analytics. The convergence of building information modeling with the smart water field provides an opportunity to transcend existing operational barriers. Such research would pave the way for demand-side management, active consumers, and demand-optimized networks, through interoperability and a system of systems approach. This paper presents a semantic knowledge management service and domain ontology which support a novel cloud-edge solution, by unifying domestic socio-technical water systems with clean and waste networks at an urban scale, to deliver value-added services for consumers and network operators. The web service integrates state of the art sensing, data analytics and middleware components. We propose an ontology for the domain which describes smart homes, smart metering, telemetry, and geographic information systems, alongside social concepts. This integrates previously isolated systems as well as supply and demand-side interventions, to improve system performance. A use case of demand-optimized management is introduced, and smart home application interoperability is demonstrated, before the performance of the semantic web service is presented and compared to alternatives. Our findings suggest that semantic web technologies and IoT can merge to bring together large data models with dynamic data streams, to support powerful applications in the operational phase of built environment systems

A Strategic Digital Transformation for the Water Industry

This book is a compilation of the knowledge shared and generated so far in the IWA Digital Water Programme. It is an insightful collection of white papers covering best practices, linking academic and industrial studies/insights with applications to give real-world examples of digital transformation. These White Papers are designed to help utilities, water professionals and all those interested in water management and stewardship issues to better understand the opportunities of digital technologies. This book covers a plethora of topics including: Instrumentation and data generation Artificial intelligence and digital twins The digital transformation and public health Mapping the digital transformation journey into the future With these topics, the aim is to present an all-encompassing reference for practitioners to use in their day-to-day activities. Through the Digital Water Programme, the IWA leverages its worldwide member expertise to guide a new generation of water and wastewater utilities on their digital journey towards the uptake of digital technologies and their integration into water services

A Strategic Digital Transformation for the Water Industry

This book is a compilation of the knowledge shared and generated so far in the IWA Digital Water Programme. It is an insightful collection of white papers covering best practices, linking academic and industrial studies/insights with applications to give real-world examples of digital transformation. These White Papers are designed to help utilities, water professionals and all those interested in water management and stewardship issues to better understand the opportunities of digital technologies. This book covers a plethora of topics including: Instrumentation and data generation Artificial intelligence and digital twins The digital transformation and public health Mapping the digital transformation journey into the future With these topics, the aim is to present an all-encompassing reference for practitioners to use in their day-to-day activities. Through the Digital Water Programme, the IWA leverages its worldwide member expertise to guide a new generation of water and wastewater utilities on their digital journey towards the uptake of digital technologies and their integration into water services

An assessment of the effectiveness of using data analytics to predict death claim seasonality and protection policy review lapses in a life insurance company

Data analytics tools are becoming increasingly common in the life insurance industry. This research considers two use cases for predictive analytics in a life insurance company based in Ireland. The first case study relates to the use of time series models to forecast the seasonality of death claim notifications. The baseline model predicted no seasonal variation in death claim notifications over a calendar year. This reflects the life insurance company’s current approach, whereby it is assumed that claims are notified linearly over a calendar year. More accurate forecasting of death claims seasonality would enhance the life insurance company’s cashflow planning and analysis of financial results. The performance of five time series models was compared against the baseline model. The time series models included a simple historical average model, a classical SARIMA model, the Random Forest Regressor and Prophet machine learning models and the LSTM deep learning model. The models were trained on both the life insurance company’s historical death claims data and on Irish population deaths data for the 25-74 age cohort over the same observation periods. The results demonstrated that machine learning time series models were generally more effective than the baseline model in forecasting death claim seasonality. It was also demonstrated that models trained on both Irish population deaths and the life insurance company’s historical death claims could outperform the baseline model. The best forecaster was Facebook’s Prophet model, trained on the life insurance company’s claims data. Each of the models trained on Irish population deaths data outperformed the baseline model. The SARIMA and LSTM consistently underperformed the baseline model when both were trained on death claims data. All models performed better when claims directly related to Covid-19 were removed from the testing data. The second case study relates to the use of classification models to predict protection policy lapse behaviour following a policy review. The life insurance company currently has no method of predicting individual policy lapses, hence the baseline model assumed that all policies had an equal probability of lapsing. More accurate prediction of policy review lapse outcomes would enhance the life insurance company’s profit forecasting ability. It would also provide the company with the opportunity to potentially reduce lapse rates at policy review by tailoring alternative options for certain groups of policyholders. The performance of 12 classification models was assessed against the baseline model - KNN, Naïve Bayes, Support Vector Machine, Decision Tree, Random Forest, Extra Trees, XGBoost, LightGBM, AdaBoost and Multi-Layer Perceptron (MLP). To address class imbalance in the data, 11 rebalancing techniques were assessed. These included cost-sensitive algorithms (Class Weight Balancing), oversampling (Random Oversampling, ADASYN, SMOTE, Borderline SMOTE), undersampling (Random Undersampling, and Near Miss versions 1 to 3) as well as a combination of oversampling and undersampling (SMOTETomek and SMOTEENN). When combined with rebalancing methods, the predictive capacity of the classification models outperformed the baseline model in almost every case. However, results varied by train/test split and by evaluation metric. Oversampling models performed best on F1 Score and ROC-AUC while SMOTEENN and the undersampling models generated the highest levels of Recall. The top F1 Score was generated by the Naïve Bayes model when combined with SMOTE. The MLP model generated the highest ROC-AUC when combined with BorderlineSMOTE. The results of both case studies demonstrate that data analytics techniques can enhance a life insurance company’s predictive toolkit. It is recommended that further opportunities to enhance the predictive ability of the time series and classification models be explored

Applying Machine Learning to Biological Status (QValues) from Physio-chemical Conditions of Irish Rivers

This thesis evaluates and optimises a variety of predictive models for assessing biological classification status, with an emphasis on water quality monitoring. Grounded in previous pertinent studies, it builds on the findings of (Arrighi and Castelli, 2023) concerning Tuscany’s river catchments, highlighting a solid correlation between river ecological status and parameters like summer climate and land use. They achieved an 80% prediction precision using the Random Forest algorithm, particularly adept at identifying good ecological conditions, leveraging a dataset devoid of chemical data