Predictive modeling framework accelerated by GPU computing for smart water grid data-driven analysis in near real-time

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The M4 Competition: 100,000 time series and 61 forecasting methods

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Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada

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Efficient progressive sampling

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Short‐term municipal water demand forecasting

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Criteria for classifying forecasting methods

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Automated Detection of Pipe Bursts and Other Events in Water Distribution Systems

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Novelty detection for time series data analysis in water distribution systems using support vector machines

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Handling missing data in near real-time environmental monitoring: A system and a review of selected methods

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Short-Term Water Demand Forecast Based on Deep Learning Method

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Forecasting operational demand for an urban water supply zone

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Comparison of stochastic and machine learning methods for multi-step ahead forecasting of hydrological processes

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Water demand forecasting: review of soft computing methods

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Statistical modeling of daily urban water consumption in Hong Kong: Trend, changing patterns, and forecast

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Water Distribution System Burst Detection Using a Nonlinear Kalman Filter

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Burst Detection in Water Networks Using Principal Component Analysis

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Burst Detection in District Metering Areas Using Deep Learning Method

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A neural network approach to burst detection

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Predicting water demand: a review of the methods employed and future possibilities

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Applying human mobility and water consumption data for short-term water demand forecasting using classical and machine learning models

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Leakage Detection and Estimation Algorithm for Loss Reduction in Water Piping Networks

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Improving the rapidity of responses to pipe burst in water distribution systems: a comparison of statistical process control methods

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Leakage fault detection in district metered areas of water distribution systems

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Detecting anomalies in water distribution networks using EPR modelling paradigm

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Geostatistical techniques for approximate location of pipe burst events in water distribution systems

Romano, M., Kapelan, Z. S., & Savic, D. A. (2013) · Journal of Hydroinformatics · doi:10.2166/hydro.2013.094 · Cited by 57

STL Decomposition of Time Series Can Benefit Forecasting Done by Statistical Methods but Not by Machine Learning Ones

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Disturbance Extraction for Burst Detection in Water Distribution Networks Using Pressure Measurements

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Real-Time Burst Detection in District Metering Areas in Water Distribution System Based on Patterns of Water Demand with Supervised Learning

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Burst Detection Using an Artificial Immune Network in Water-Distribution Systems

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Pipe network leak detection: comparison between statistical and machine learning techniques

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Forecasting Hourly Water Demands With Seasonal Autoregressive Models for Real‐Time Application

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Monitoring Support for Water Distribution Systems based on Pressure Sensor Data

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Evolutionary Deep Learning with Extended Kalman Filter for Effective Prediction Modeling and Efficient Data Assimilation

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Forecasting Residential Water Consumption in California: Rethinking Model Selection

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Energetic optimization and evaluation of a drinking water pumping system: application at the Rassauta station

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GPU-Accelerated Deep Learning for Heatwave Prediction and Urban Heat Risk Assessment

Alihodžić, A. (2026) · arXiv · doi:10.48550/arxiv.2605.16435