Elaboración de una metodología de trabajo para el tratamiento y la predicción de series temporales de consumo de agua potable

[ES] El presente trabajo consiste en la elaboración de una metodología para el análisis de una serie temporal de caudal de agua potable en un sector hidráulico de tipo domiciliario de una ciudad de la provincia de Valencia (España). Esta metodología aborda la reconstrucción de la serie temporal mediante la imputación de valores faltantes, la corrección de valores anómalos y la predicción del consumo a corto plazo mediante el uso de técnicas de machine learning y deep learning. La investigación llevada a cabo propone una metodología novedosa, puesto que en la literatura científica relacionada con este ámbito no se ha abordado el problema del tratamiento de este tipo de series temporales de manera integral. La metodología desarrollada, por lo tanto, pretende ser la semilla de un sistema de ayuda para la toma de decisiones que permita decidir, para cada tipo de serie temporal de caudal de agua potable o similares, cuál es la estrategia idónea que debe seguir el analista para optimizar la predicción del consumo en un sector hidráulico, y por ende, la operación del propio sistema de distribución asociado.[EN] The following research consists in the elaboration of a methodology for the analysis of a time series of drinking water flow rate in a domestic hydraulic sector of a city in the province of Valencia (Spain). This methodology deals with the reconstruction of the time series through the imputation of missing values, the correction of outliers and the forecasting of short-term consumption using machine learning and deep learning techniques. The conducted research proposes a novel methodology since the treatment of this kind of time series has not been addressed in a comprehensive way in the scientific literature related to this field. The developed methodology, aims to be the seed of a decision support system that allows to decide, for each kind of time series of drinking water flow rate or similar, which is the ideal strategy to be followed by the analyst to optimize the forecast of the flow rate in a hydraulic sector, and therefore, the operation of the associated distribution system.Morer, FE. (2020). Elaboración de una metodología de trabajo para el tratamiento y la predicción de series temporales de consumo de agua potable. Universitat Politècnica de València. http://hdl.handle.net/10251/159161TFG

Assessment of the suitability of degradation models for the planning of CCTV inspections of sewer pipes

The degradation of sewer pipes poses significant economical, environmental and health concerns. The maintenance of such assets requires structured plans to perform inspections, which are more efficient when structural and environmental features are considered along with the results of previous inspection reports. The development of such plans requires degradation models that can be based on statistical and machine learning methods. This work proposes a methodology to assess their suitability to plan inspections considering three dimensions: accuracy metrics, ability to produce long-term degradation curves and explainability. Results suggest that although ensemble models yield the highest accuracy, they are unable to infer the long-term degradation of the pipes, whereas the Logistic Regression offers a slightly less accurate model that is able to produce consistent degradation curves with a high explainability. A use case is presented to demonstrate this methodology and the efficiency of model-based planning compared to the current inspection plan

Assessment of the suitability of degradation models for the planning of CCTV inspections of sewer pipes

The degradation of sewer pipes poses significant economical, environmental and health concerns. The maintenance of such assets requires structured plans to perform inspections, which are more efficient when structural and environmental features are considered along with the results of previous inspection reports. The development of such plans requires degradation models that can be based on statistical and machine learning methods. This work proposes a methodology to assess their suitability to plan inspections considering three dimensions: accuracy metrics, ability to produce long-term degradation curves and explainability. Results suggest that although ensemble models yield the highest accuracy, they are unable to infer the long-term degradation of the pipes, whereas the Logistic Regression offers a slightly less accurate model that is able to produce consistent degradation curves with a high explainability. A use case is presented to demonstrate this methodology and the efficiency of model-based planning compared to the current inspection plan