Efficient leak management is crucial for maintaining water distribution systems, and tools like Geographic Information Systems (GIS) and hydraulic simulation software such as EPANET can greatly improve this process. 

By analyzing different factors related to leakage likelihood, these technologies help prioritize areas at higher risk of leaks. This article examines how GIS and hydraulic modeling can be applied to a water distribution network in Indonesia. 

We will explore key factors like pipe condition, pressure variations, and flow velocity, and demonstrate how these insights can be used to create a decision matrix for effective leak detection and maintenance prioritization.

 

 

GIS in Water Distribution Network Analysis for Leak Detection

Geographic Information System (GIS) data plays a key role in identifying and prioritizing areas for leak detection. Incorporating insights from detecting water leaks can further enhance decision-making for water distribution systems. 

By analyzing factors such as the age, material, and condition of pipes, GIS helps target sections most susceptible to leaks.

 

a. Role of Pipe Material in Water Distribution Systems

Older, metal-based pipes (e.g., cast iron, galvanized steel, and steel) are particularly prone to corrosion and degradation, posing significant challenges in water distribution systems. 

The corrosive effects of water, soil conditions, and exposure to oxygen can cause these metals to weaken, develop rust, and lose their structural integrity. 

As the pipes corrode, they become more susceptible to cracks, punctures, and joint failures, which can lead to water leaks.

Figure 1: Pipe material repartition map and bar chart

In the example above, some of the main pipes are made of cast iron (CI) and galvanized steel (ST). Those pipes are more prone to corrosion compared to PVC, HDPE, and asbestos cement (AC) pipes.

However, asbestos cement (AC) pipes are more vulnerable to cracking and breaking over time, particularly due to their brittle nature as they age, rather than corrosion.

 

b. Pipe date of installation

The age of a pipe and the year it was installed are critical factors in assessing the likelihood of leaks. 

Over time, the materials used in plumbing systems naturally degrade due to a variety of environmental factors, such as temperature fluctuations, soil movement, and the constant wear from continuous use. 

As pipes age, they face growing stress from these external elements, which can lead to the deterioration of the pipes’ structure. This stress can cause cracks, weakened joints, and, eventually, leaks.

Figure 2: Pipe date of installation map

In the example above, the pipes marked in red are over 40 years old, making them significantly more vulnerable to failures and water leaks. 

The advanced age of these pipes means they have been exposed to decades of environmental changes and stress, which, combined with the aging of the materials, greatly increases the risk of cracks, bursts, and other forms of leakage.

 

c. Impact of Topography on Water Distribution Network Analysis

The topography of the area plays a crucial role in identifying regions that are more prone to leaks. 

Specifically, parts of the network situated at lower elevations, relative to the inlet point, are more likely to experience higher pressure, which can increase the risk of pipe bursts.

Figure 3: Digital Elevation Model (DEM) and junction elevation maps

In the example above, the southeastern part of the network is more prone to leaks due to its proximity to the inlet and its lower elevation. This combination of factors results in higher pressure in this area, making it more susceptible to pipe bursts and leaks.

 

d. Water demand repartition

The distribution of water demand across the network plays a significant role in identifying areas more vulnerable to leaks and failures. Regions with higher water demand often experience increased pressure fluctuations, which can strain the system and elevate the risk of pipe bursts.

In contrast, areas with lower demand may see reduced flow, leading to stagnation and potential blockages.

Figure 4: Base water demand repartition heatmap and diagram

In the example above, eastern and northern areas of the network with higher water demand are more susceptible to leaks due to the constant pressure exerted by the flow requirements. This heightened pressure, especially during peak demand periods, can weaken the pipes and increase the likelihood of bursts or other failures. 

 

 

Hydraulic Simulation in Water Distribution Systems for Leak Detection

Hydraulic modeling plays a vital role in identifying areas within a network that are more susceptible to leaks. Tools like water leak detection equipment enable accurate identification and prioritization of leak-prone areas. 

By simulating the behavior of fluid within the system under different conditions, hydraulic models can help prioritize locations where leaks are most likely to occur, or where they may have the most significant impact if left undetected. 

The following simulated results are essential in hydraulic modeling simulations to identify these priority areas.

 

a. Analyzing Junction Pressure in Water Distribution Systems

Junction pressure refers to the pressure at specific connection points in the pipeline network, where water or other fluids are delivered to various sections of the system. Monitoring junction pressures is crucial because fluctuations in pressure can indicate potential leak points. 

For example, unusually low pressure might suggest that there is a loss of flow due to a leak, while high pressure could stress the system, potentially leading to bursts or other failures. By identifying areas with abnormal junction pressures, utilities can prioritize those locations for further investigation.

Figure 5: Junction pressure repartition map and diagrams

 

b. Pipe velocity

Pipe velocity refers to the speed at which fluid flows through the pipes. A high velocity can increase the likelihood of wear and tear on the pipes, which in turn raises the risk of leaks or bursts, especially in older infrastructure. 

This highlights the importance of how to find underground water leaks for effective leak prevention and maintenance. 

Hydraulic modeling can help simulate varying flow conditions and predict areas where velocity-related problems may lead to pipe damage. Prioritizing these areas based on velocity data allows for more targeted leak detection and maintenance activities.

Figure 6: Pipe velocity repartition map, diagrams and bar chart

 

c. Understanding Pipe Unit Head Loss in Network Analysis

Pipe head loss refers to the reduction in pressure (or head) as fluid moves through a pipe, caused by friction and other resistive factors like bends, fittings, and the roughness of the pipe’s internal surface. Pipe unit head loss is the head loss per unit length of pipe. 

Hydraulic modeling helps identify sections with excessive unit head loss, which can indicate potential leak sites. Areas with significant unit head loss become high-priority zones for maintenance and inspection to prevent further damage or system failure.

Figure 7: Pipe unit head loss repartition map

 

Decision Matrix for Water Distribution Network Analysis

Based on the analysis of GIS data and hydraulic simulation results, we can develop a decision matrix to identify and prioritize pipes that are most critical for leak detection. This matrix evaluates all key factors related to leak potential, on a score of 0 to 5, each then weighted by a coefficient that reflects its importance.

Table 1: Leak investigation matrix decision coefficient table

Each pipe is ultimately assigned a composite score between 0 and 100, with higher scores indicating greater urgency for leak investigation.

Table 2: Pipe leakage investigation score table

We can then map this data, allowing the local water authority to visually identify and prioritize the pipes and areas that require immediate attention for leak investigation.

Figure 8: Pipe leakage investigation score map

In our example, the analysis shows that approximately 25% of pipes score above 40, while about 6% score above 50. These high-score pipes should be prioritized first for leak investigation.

Figure 9: Pipe leakage investigation score repartition diagram

 

Conclusion

GIS and hydraulic simulation models are powerful tools for effectively enhancing the leakage management process in water distribution systems, offering valuable insights through water distribution network analysis. 

GIS enables visualization and prioritization of areas prone to leaks by analyzing data such as pipe material, installation date, topography, and water demand distribution. 

On the other hand, hydraulic simulation provides detailed insights into operational characteristics, such as junction pressure, flow velocity, and unit head loss, allowing for precise targeting of maintenance and leakage prevention efforts.

This integrated approach significantly improves the efficiency of water management systems and enables optimal results even with limited resources. In regions like Indonesia, it is crucial to tailor the analysis to the unique characteristics and environmental conditions of the network, ensuring long-term stability and sustainability.

By leveraging GIS and hydraulic modeling, leakage management can minimize water loss, maximize network efficiency, and ensure a reliable water supply for communities, establishing itself as a vital strategy for modern water resource management.

Effective water leak detection requires careful planning and consideration of various factors. 

In this blog post, we’ll explore the key elements that contribute to successfully detecting water leaks and how to plan for them.

 

Key Factors in Detecting Water Leaks with Acoustic Methods

 

When planning for an acoustic water leak detection system, three crucial factors come into play:

1. Water pressure

2. Pipe material

3. Distribution of listening points (water meters and valves)

 

Water Pressure

Higher water pressure creates larger amplitude friction noise from leaks, allowing the sound to travel further. 

Therefore, it’s advantageous to collect and analyze leak sounds during periods of high pressure for more effective water leak detection equipment.

 

Pipe Material

The material of the pipes significantly affects how far leak sounds can travel. 

Underground water leak detectors are especially useful when working with non-metallic pipes that limit sound transmission.

 

 

Distribution of Listening Points

The availability and distribution of points to detect water leaks (such as valves and water meters) are crucial. 

In urban areas with metal pipes and numerous valves, successful leak detection might be possible by checking valves alone, without the need to inspect water meters.

 

 

The Importance of Water Network Configuration in Detecting Water Leaks

The configuration of the water pipe network largely determines the cost, time, and success rate of detecting leaks. 

Areas with few listening points and non-metallic pipes pose significant challenges for acoustic leak detection.

 

Strategies for Challenging Scenarios

– Long Pipelines Without Listening Points

For long stretches of buried pipes without accessible listening points, two main strategies can be considered to help in detecting water leaks:

• Surface Listening: Checking the ground surface every 50 centimeters for leak sounds.
  However, this method is time-consuming and nearly impossible in noisy urban environments.

• Gas Injection: Injecting a mixture of 5% hydrogen and 95% nitrogen and air into the pipes.
  However, due to regulations, many countries restrict this method for drinking water systems.

 

– Installing Permanent Listening Equipment 

For areas where traditional methods are challenging, installing permanent listening equipment like Sonic T1 can create a sustainable infrastructure for ongoing leak detection. 

While this approach has higher initial costs, it enables long-term, sustainable leak monitoring.

 

– Developing a Comprehensive Leak Detection Plan for Detecting Water Leaks

 

To create an effective leak detection system plan:

1. Analyze the entire water pipe network based on GIS information.
2. Collect and analyze pressure data at key points.
3. Determine appropriate leak detection methods for different areas.
4. Plan the timing and personnel deployment for leak detection activities.
5. Identify blind spots where traditional leak detection is challenging.
6. Consider installing permanent listening equipment or gas injection methods for these difficult areas.

 

 

Conclusion

Successful leak detection requires thorough planning and a good understanding of your water network’s characteristics. 

By considering factors like water pressure, pipe materials, and the distribution of listening points, you can develop a comprehensive strategy that maximizes the effectiveness of your leak detection efforts. 

Remember, investing in infrastructure improvements like permanent listening equipment can pay off in the long run by enabling sustainable, ongoing leak monitoring.

 

Looking to enhance your water leak detection system? 

Contact WI.Plat today to learn how our advanced solutions can help you achieve more effective and sustainable water management.

 

The Importance of Underground Water Leak Detection in Modern Infrastructure

Around the world, over $50 billion is wasted annually due to water pipe leaks. If we can reduce water loss, it could supply clean water to an additional 900 million people. One of the most challenging water issues is identifying leaks in long underground water leak detection systems, which are often invisible.

Currently, leak detection experts use water leak detectors to listen to leak sounds from the road surface or pipes and identify potential problem areas.

This method relies heavily on the expert’s experience, making it difficult to train enough professionals. Moreover, the results may vary based on the skill level of the expert.

To solve this, we need to digitalize leak sounds and teach AI to detect leaks, reducing reliance on human expertise.

 

How to Detect Water Leaks Underground?

When a pipe leaks, a sound wave known as “leak noise” is generated. This wave energy travels through mediums such as pipes and the ground. The distance the sound can travel depends on the pipe material and the characteristics of the ground.

For example, metal pipes, which are less elastic, allow leak sounds to travel farther, while non-metallic pipes, which are more elastic, transmit the sound over shorter distances.

This is closely related to the density of the medium. The denser the material, the less sound absorption occurs, meaning the leak sound can travel farther.

The characteristics of leak noise also depend on the pipe material, ground composition, and pipe pressure. The frequency of leak noise in water pipes typically ranges between 200 Hz and 2,000 Hz.

The specific frequency depends on factors such as the material of the pipe and the water pressure. Lower-frequency sounds tend to travel farther than higher-frequency sounds.

 

How Do Humans Perceive Leak Sounds?

Humans can hear sounds up to 22 kHz. When sound waves move air molecules and these waves reach the ear, they cause the cochlea in the ear to move, sending tiny electrical signals to the brain. This is how we perceive sound.

Advanced Technologies in Underground Water Leak Detection

Leak sensors, used in leak detection, work similarly to the cochlea in the human ear. They typically use piezoelectric elements, which generate electrical energy when they detect mechanical energy. The frequency a piezoelectric element detects depends on its diameter and length.

It’s essential to design piezoelectric sensors according to the specific characteristics of leak noise in water pipes to ensure optimal detection.

How Does AI Work with Underground Water Detectors?

The way AI detects leaks using underground water detection equipment is similar to how humans do. When humans hear a leak sound, their brain processes the data and learns to recognize it. AI works the same way by learning from data.

First, the signals detected by underground water detectors are converted into WAV files. These WAV files are then transformed into spectrogram images, which show the frequency spectrum of the sound over time. AI is trained using these spectrogram images.

To develop an AI model for leak detection, we commonly use Convolutional Neural Networks (CNNs), which are also used for distinguishing between different objects, like cats and dogs. With training, AI can recognize leak sounds just like a human expert.

The most important part of developing a water leak AI model is to secure a large amount of water leak noise data from different environments using specialized underground water leak detection equipment.

Conclusion

AI  has the potential to revolutionize water leak detection, reducing reliance on human expertise and allowing for more consistent and efficient leak identification.

By harnessing the power of sound data and advanced neural networks, we can tackle the global water loss problem and ensure more people have access to clean water.

Take control of your underground water management and safeguard your infrastructure with WI.Plat’s cutting-edge leak detection technology. Contact us today to learn more!