In the world of water management, detecting and finding water leaks quickly and accurately is crucial for conserving resources and preventing infrastructure damage. 

While artificial intelligence (AI) has revolutionized many aspects of leak detection, the need for customization in these AI models is often overlooked. 

Let’s dive into why this customization is so important for finding water leaks.

 

Understanding the Physics Behind Finding Water Leaks

When a leak occurs in a water pipe, the pressure difference between the inside and outside of the pipe causes water molecules to move, creating turbulence and leading to water leaks.

This movement, shaped by the leak’s characteristics, creates irregular turbulence.

This turbulence, in turn, produces frictional vibration noise.

It’s worth noting that sound and vibration are essentially the same phenomena – we simply call vibrations transmitted through air “sound”. 

 

The vibration noise generated by a leak is determined by various factors, including:

•  Pressure

• Water molecule characteristics

• Pipe material

• Leak shape

 

Key Characteristics for Finding Water Leaks with AI

In most Southeast Asian countries, where the average water pressure is around 1-1.5 bar, the majority of leak sounds consist of frequencies below 1,000 Hz.

Initially, a leak produces sounds across a wide frequency range. 

 

However, as the sound travels along the pipeline:

• High-frequency components diminish quickly

• Low-frequency components travel further

 

Let’s consider an example of a leak sound from a valve packing in a cast iron pipe with 1.5 bar pressure. 

 

As we move away from the leak source:

• The frequency range of the leak sound narrows

• The amplitude of the sound decreases rapidly

This decrease in amplitude is even more pronounced in non-metallic pipes.

 

 

Sound Absorption Rate

The rate at which the vibration noise amplitude decreases is known as the sound absorption rate. This rate is most closely related to the density of the medium through which the sound is traveling.

* Source: Acoustical Design and Noise Control in Metro Stations: Case Studies of the Ankara Metro System (BUILDING ACOUSTICS · Volume 14 · Number 3 · 2007 Pages 231–249)

 

Locating Leaks Through Sound Analysis

By analyzing the characteristics of leak sounds collected at different points, we can estimate the location of a leak using advanced techniques like the underground water leak detector.

However, this process requires a crucial preliminary step: collecting and analyzing sound data from the target section to understand the typical characteristics of leak sounds in that area.

Professional leak detectors continually listen to leak sounds in the area they’re investigating, constantly refining their understanding of the local sound characteristics to improve their detection accuracy.

 

AI Customization for Finding Water Leaks in Different Environments

This is where the importance of customizing AI models for finding water leaks becomes apparent. 

 

To enhance the accuracy of AI-based leak detection, we need to:

• Collect leak sound data specific to the target area, which can be made easier with tools like a water leak detector specifically designed for precision.

• Use this data to customize our AI models

 

Just as human experts adapt their techniques to local conditions, our AI models must be tailored to the unique characteristics of each water network they monitor.

 

Conclusion: The Role of Custom AI Models in Finding Water Leaks Efficiently

Customizing AI models for finding water leaks isn’t just a technical nicety – it’s a necessity for achieving high accuracy in diverse environments.

By understanding the physics of leak sounds and adapting our models to local conditions, we can create more effective, efficient finding water leaks, ultimately contributing to better water management worldwide.

In the world of water management, leak detection is a critical challenge that has long plagued utility companies and municipalities. Traditional methods, while effective, often require significant time, resources, and expertise.

Enter Sonic GL, a cutting-edge leak detection system that’s transforming how we approach this vital task.

 

Understanding Sonic GL Leak Detection Equipment 

Sonic GL is not just another tool in the leak detection equipment arsenal.

It’s a high-sensitivity leak sensor with several key features:

– Uses piezoelectric elements and magnets

– Designed to detect the specific frequency band of water leak sounds

– Works in conjunction with the AI-powered NELOW system

– Aims to minimize dependence on specialists

 

The NELOW system, which Sonic GL is an integral part of, incorporates an artificial intelligence model designed to replace the need for expert judgment in leak detection.

This AI model analyzes the data collected by Sonic GL devices, providing accurate leak assessments without relying on human expertise.

 

The Challenge of Distribution Pipe Networks

Distribution pipe networks present unique challenges for leak detection equipment. Learn more about how underground water leak detectors are transforming leak management in our blog on Underground Water Leak Detectors.

1. Often use non-metallic pipes, limiting sound transmission
2.  Numerous branch lines can distort leak sounds
3.  It is required to check every water meter and valve for successful leak detection

These factors combine to make leak detection in distribution networks a complex and time-consuming process. 

However, Sonic GL offers a solution that addresses these challenges head-on.

 

The Sonic GL Leak Detection Equipment Process

Step 1: Initial Leak Check

The process begins with a preliminary sweep that can be performed by non-expert staff. For a detailed guide on choosing the best water leak detector, explore our Best Water Leak Detectors.

1. Staff equipped with Sonic GL leak detection equipment and smartphones collect 3-second sound samples at water meters and valves.
2. An AI model on a central server analyzes these samples.
3. The system generates a map highlighting suspected leak areas.

This initial step provides a solid starting point for more focused investigations, significantly reducing the time and resources needed for comprehensive leak detection.

 

Key advantages of this approach include:

– Efficiency:

A single leak inspector can check 100 to 150 locations per day.

– Job Creation:

In some countries, local women are employed to perform these initial checks, contributing to job creation in the community.

– Accessibility:

The simplicity of the process allows for the employment of non-expert staff, further reducing costs.

 

Step 2: Secondary Leak Check

For areas flagged in the initial check, a more detailed inspection is performed:

1. 2-3 Sonic GL leak detection equipment is installed near suspected leak points.
2. Data is collected at regular intervals (typically every 10 minutes).
3. The system’s correlation feature analyzes the collected data.
4. Continuous leak signals confirm the presence of a leak.
5. Correlation analysis estimates the exact leak location.

 

This step narrows down the search area significantly, allowing for more efficient use of resources in pinpointing leaks.

 

Step 3: Final Pinpointing

Even with advanced technology, a final surface check is necessary:

1. Technicians perform a surface leak check in quiet conditions.
2. Leak sounds can typically be heard within 1-2 meters of the actual leak point.
3. This step ensures pinpoint accuracy in leak location.

 

 

Advantages of Sonic GL as Leak Detection Equipment

Sonic GL offers several key advantages over traditional leak detection equipment:

1. Versatility: One device for mobile checks, fixed correlation checks, and surface leak detection
2. Cost-effective: Reduces the need for specialist technicians
3. Time-efficient: Streamlines the leak detection process
4. Accuracy: Combines AI analysis with correlation techniques for precise leak location

Sonic GL Variants and Recommended Setup

Sonic GL comes in two variants:

– Sonic GL-F: Used for mobile checks and fixed correlation checks

– Sonic GL-G: Specialized for surface leak detection

 

For a typical city, a recommended setup might include:

– 5 units for initial leak checks by non-expert staff

– 25 units for expert use in correlation analysis and surface detection

 This setup provides a comprehensive toolkit for tackling leaks across a wide urban area, balancing the need for broad coverage with detailed analysis capabilities.

By integrating Sonic GL into your leak detection strategy, you’re not just adopting a new tool; you’re embracing a new philosophy in water network maintenance. 

This approach allows for a more systematic, less expertise-dependent method of keeping our water systems running smoothly. 

It’s a step towards more sustainable water management, where leaks are caught early, water loss is minimized, and resources are used more efficiently.

 

Conclusion

Sonic GL represents a significant advancement in leak detection technology. 

By combining high-sensitivity sensors with intelligent analysis and a user-friendly approach, it addresses many of the longstanding challenges in water network maintenance. 

As we move towards smarter, more sustainable cities, tools like Sonic GL will undoubtedly play a crucial role in ensuring our most precious resource – water – is managed with the care and efficiency it deserves.

In South Korea, the production and supply of tap water come with a hefty cost—around 800 billion won per year in electricity bills. Interestingly, about 95% of this cost is driven by pump operations, which are essential to AI water management. 

With over 3,200 water reservoirs nationwide, South Korea also experiences significant variations in industrial electricity rates, with daytime electricity being more than twice as expensive as nighttime rates. 

Moreover, the energy required to produce a ton of water varies depending on the combination of pumps used. This creates a unique opportunity to leverage ai water management to predict water usage, optimize pump and valve operations, and significantly reduce energy consumption and costs.

 

AI’s Role in Optimizing Water Energy Management

 AI water management can help operators predict water demand optimize pump and valve operations, and integrate the best water leak detector systems for early detection and prevention. Here’s a process that WI.Plat has explored how to make this a reality:

 

Step-by-Step AI Model for Efficient Water Management

To develop an AI water management model that optimizes energy use in water systems, several critical components need to be in place. One of the most important is a SCADA (Supervisory Control and Data Acquisition) system, which collects real-time operational data. The general process for developing AI-based water energy-saving models includes the following steps:

 

1. Water Supply System Analysis: 

Begin by mapping out the water management system, including pump stations, valves, reservoirs, and various sensors. This provides a clear understanding of how water flows through the system.

2. Data Mapping: 

Next, map out the sensor tags (Tag_ID) based on the system diagram to link data points to the physical infrastructure.

3. Data Collection and Preliminary Analysis: 

Collect SCADA data and perform basic analysis to establish preprocessing standards, preparing the data for AI model training.

 

4. Realtime Data Processing Module (RDPM) Design: 

Define the data that will be used for the AI water management model and design preprocessing workflows.

5. Water Usage Forecasting Model Design: 

Use historical data to analyze patterns in water usage, creating a time-based prediction model for future water demand.

 

6. Water Level Forecasting Model Design: 

Build a model that predicts future water levels in reservoirs based on predicted inflows, outflows, and current water levels.

 

7. Q-Table Design: 

Create a Q-Table that correlates operational conditions (like water levels, pump operation, and valve status) with inflows, pressure, and power consumption. The table will be used to simulate the hydraulic behavior of the system under different conditions.

 

8. Optimized Decision-Making Model (ODMM) Design: 

Use reinforcement learning to create an optimized decision-making model that selects the pump and valve control conditions yielding the best Q-Value for the current system state.

 

9. HMI Control Model (HCM) Design: 

Design the Human-Machine Interface (HMI) control model to implement AI water management recommendations on pump and valve operations, considering factors like control change thresholds and model update frequency.

 

10. AI Model and Integration System Development: 

Develop individual AI models and integrate them into a system that can run cohesively in real time.

 

11. Simulation Testing: 

Before full implementation, test the AI water management system with historical data to validate its accuracy and efficiency.

 

12. Non-stop Test Operation: 

Finally, run a continuous, uninterrupted test for at least seven days, allowing the AI to control the system independently and without operator intervention.

 

Case Study: AI Water Management in Gumi’s Water Supply System

One of the most notable applications of this AI-driven approach has been at K-water’s Gumi Regional Water Supply System in South Korea.

This system manages 464,000 tons of water per day and includes 10 pump stations and 14 reservoirs. AI development began in May 2023 and is expected to be fully operational by April 2024. 

Currently, the AI system is undergoing test operations, with human operators stepping in only when abnormal situations arise.

Early results are promising—testing has shown that AI optimization can reduce electricity costs by around 5%. However, full-scale implementation is expected to yield even more substantial savings.

 

Lessons Learned: Challenges and Opportunities in AI-Driven Water Management

There have been some challenges along the way. In many cases, the capacity of the pumps was found to be unsuitable, or the valve controls were inadequate.

For optimal AI-based control, simultaneous facility upgrades are often required. Additionally, some reservoirs lack inflow and outflow meters, which are crucial data points for AI systems to operate effectively.

The project team also found that the integration of AI systems should ideally follow after improving the water supply infrastructure. This way, the maximum potential of energy-saving measures can be realized.

Furthermore, operators may be hesitant to trust AI systems initially, so overcoming this resistance is another important factor in the success of these projects.

 

Conclusion

AI holds immense potential for revolutionizing water management by making it more energy-efficient. South Korea’s early experiments in this area, such as the Gumi Multi-regional Water Supply System, demonstrate that even modest implementations can lead to significant cost reductions. 

However, for the full benefits to be realized, upgrades in infrastructure and a shift in operator mindsets are necessary. As AI technology continues to evolve, it could play a critical role in making water supply systems more sustainable and cost-effective worldwide.

Moreover, AI models that replace human operators can be particularly beneficial in developing countries where there is often a shortage of skilled operators. 

By providing decision-making support and facilitating operator training, AI could help ensure safer, more efficient operation of water supply systems. In this way, AI not only saves energy and costs but when combined with the best water leak detector technologies, ensures more reliable water management in regions where it is most needed.

Optimize your water management with WI.Plat’s advanced solutions. Save costs, boost efficiency, and protect your infrastructure. Contact us for a consultation today!