AIMC Topic: Water Supply

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A Probabilistic Digital Twin for Leak Localization in Water Distribution Networks Using Generative Deep Learning.

Sensors (Basel, Switzerland)
Localizing leakages in large water distribution systems is an important and ever-present problem. Due to the complexity originating from water pipeline networks, too few sensors, and noisy measurements, this is a highly challenging problem to solve. ...

Shall we always use hydraulic models? A graph neural network metamodel for water system calibration and uncertainty assessment.

Water research
Representing reality in a numerical model is complex. Conventionally, hydraulic models of water distribution networks are a tool for replicating water supply system behaviour through simulation by means of approximation of physical equations. A calib...

Measuring the crop water demand and satisfied degree using remote sensing data and machine learning method in monsoon climatic region, India.

Environmental science and pollution research international
Supply of water is one of the most significant determinants of regional crop production and human food security. To promote sustainable management of agricultural water, the crop water requirement assessment (CropWRA) model was introduced as a tool f...

Leak detection and localization in water distribution networks using conditional deep convolutional generative adversarial networks.

Water research
This paper explores the use of 'conditional convolutional generative adversarial networks' (CDCGAN) for image-based leak detection and localization (LD&L) in water distribution networks (WDNs). The method employs pressure measurements and is based on...

Fuzzy Control of Pressure in a Water Supply Network Based on Neural Network System Modeling and IoT Measurements.

Sensors (Basel, Switzerland)
As hydroenergetic losses are inherent to water supply systems, they are a frequent issue which water utilities deal with every day. The control of network pressure is essential to reducing these losses, providing a quality supply to consumers, saving...

Water demand in watershed forecasting using a hybrid model based on autoregressive moving average and deep neural networks.

Environmental science and pollution research international
Increasing water demand is exacerbating water shortages in water-scarce regions (such as India, China, and Iran). Effective water demand forecasting is essential for the sustainable management of water supply systems in watersheds. To alleviate the c...

A Novel Interannual Rainfall Runoff Equation Derived from Ol'Dekop's Model Using Artificial Neural Networks.

Sensors (Basel, Switzerland)
In water resources management, modeling water balance factors is necessary to control dams, agriculture, irrigation, and also to provide water supply for drinking and industries. Generally, conceptual and physical models present challenges to find mo...

Smart Sensorization Using Propositional Dynamic Logic.

Sensors (Basel, Switzerland)
The current high energy prices pose a serious challenge, especially in the domestic economy. In this respect, one of the main problems is obtaining domestic hot water. For this reason, this article develops a heating system applied to a conventional ...

Leak detection in real water distribution networks based on acoustic emission and machine learning.

Environmental technology
Water scarcity as well as social and economic damages caused by the increasing amounts of non-revenue water in the water distribution networks (WDNs) have been prompting innovative solutions. A great deal of potable water is wasted due to leakage in ...

Development of a Soft Sensor for Flow Estimation in Water Supply Systems Using Artificial Neural Networks.

Sensors (Basel, Switzerland)
A water supply system is considered an essential service to the population as it is about providing an essential good for life. This system typically consists of several sensors, transducers, pumps, etc., and some of these elements have high costs an...