Clear Brook: A Mobile App that Crowd Sources Water‑Related

Problems from Around a Community and Display Them on a Man

S. Manikandan

, M. Rehaana Hafrin

, V. Bhagyalakshmi

, Sudaroli

M. Ramakrishnan

and M. P. Thiruvenkatasuresh

Department of Information Technology, E.G.S. Pillay Engineering College, Nagapattinam, Tamil Nadu, India

Department of Information Technology, Erode Senguthar Engineering College, Erode, Tamil Nadu, India

Keywords: Geolocation Services, Community Reporting, Real‑Time Monitoring, Water Issue Tracking, Data

Visualization, GIS Mapping.

Abstract: Water-Related challenges, including scarcity, contamination, leakage, and flooding, significantly impact

communities worldwide. This paper presents a mobile application that leverages crowdsourcing and

geolocation technologies to enable users to report and track water issues in real-time. Through an intuitive

interface, citizens can submit location-based reports enriched with descriptions, photos, and severity levels.

These reports are aggregated, validated, and displayed on an interactive map, providing authorities, NGOs,

and policymakers with valuable insights into critical water-related problems. The platform fosters community

participation and data-driven decision-making, facilitating proactive interventions and sustainable water

resource management. By bridging the gap between citizens and stakeholders, the application enhances

response efficiency and contributes to long-term water sustainability efforts.

1 INTRODUCTION

Water-related challenges such as scarcity,

contamination, leakage, and flooding pose significant

threats to communities worldwide, impacting public

health, agriculture, and infrastructure. Addressing

these issues requires efficient monitoring, timely

reporting, and coordinated intervention. Traditional

water management systems often rely on manual

reporting and bureaucratic processes, leading to

delays in identifying and resolving issues.

Additionally, the lack of centralized data and real-

time monitoring hinders effective decision-making

and resource allocation (

J. M. Shepherd, 2022).

This paper introduces a mobile application that

leverages crowdsourcing and geolocation

technologies to enable community-driven reporting

of water-related problems (

M. P. Gomez and L. J.

Brown, 2022)

. The platform allows users to submit

location-based reports enriched with descriptions,

images, and severity levels. These reports are

aggregated and visualized on an interactive map,

offering real-time insights into problem hotspots. By

integrating computer vision and machine learning

techniques, the system enhances data validation and

categorization, ensuring reliable information for

decision-makers (

P. Rajalakshmi., 2022).

Unlike traditional water management approaches,

this application bridges the gap between citizens and

authorities by fostering active participation,

accountability, and collaboration (

N. Al-Ghamdi and K.

S. Al-Hassan., 2021)

. The system incorporates an alert

mechanism to notify relevant stakeholders about

critical issues, enabling a faster response to

emergencies such as pipeline bursts or flood risks.

Furthermore, predictive analytics can be integrated to

analyze historical data and anticipate potential water-

related issues before they escalate.

The proposed solution is designed to be scalable

and adaptable (

R. K. Mishra et al., 2021), making it

suitable for both urban and rural environments (

Wang et al., 2021)

. It can be customized to

accommodate region-specific water challenges and

integrate with existing governmental or non-

governmental databases (

S.Manikandan et al., 2024). By

providing a decentralized yet structured approach to

water issue reporting, the application contributes to

long-term sustainability, disaster preparedness, and

efficient resource management (

Saradhi Thommandru

V et al., 2024)

This paper explores the system’s architecture, key

Manikandan, S., Hafrin, M. R., Bhagyalakshmi, V., Sudaroli, , Ramakrishnan, M. and Thiruvenkatasuresh, M. P.

Clear Brook: A Mobile App that Crowd Sources Water-Related Problems from Around a Community and Display Them on a Man.

DOI: 10.5220/0013929900004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st Inter national Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 5, pages

357-361

ISBN: 978-989-758-777-1

357

functionalities, user engagement strategies, and the

broader impact of crowdsourced water issue tracking.

Additionally, we discuss potential challenges such as

data validation, user participation, and system

scalability, along with future enhancements to

improve its effectiveness (

Mujawar, M et al., 2024)

2 METHODOLOGY

The proposed system integrates Firebase services and

machine learning techniques to create a secure,

efficient, and real-time platform for water issue

reporting and management. The methodology

consists of the following key components:

2.1 User Authentication & Security

The system ensures secure user authentication using

Firebase Authentication, which employs scrypt for

password hashing, HMAC-SHA256 for JWT signing,

and OAuth 2.0 for Google Sign-In. This prevents

unauthorized access and protects user data.

2.2 Real-Time Database & Data

Management

Firestore is used as the primary database, offering B-

tree indexing for fast retrieval, WebSockets for real-

time updates, and Firestore Security Rules to enforce

access control. This enables seamless storage and

retrieval of issue reports.

2.3 Media Handling & Storage

Users can upload images as proof of water-related

issues. These images are stored securely in Firebase

Storage, which utilizes AES-256 encryption, HMAC-

SHA256 for signed URLs, and resumable uploads to

ensure efficient and secure file handling.

2.4 Geolocation & Issue Mapping

The system captures location data using GPS and

network-based location services. It employs the

Haversine formula to calculate distances and cluster

reports, ensuring accurate issue mapping and

identifying high-risk zones.

2.5 Image Processing & Optimization

Images are processed using JPEG compression and

bilinear interpolation to optimize storage and ensure

faster loading times without compromising quality.

This enhances user experience and system

performance.

This methodology ensures a secure, scalable, and

real-time solution for water issue reporting,

leveraging modern cloud-based technologies to

enhance user experience, optimize system

performance, and facilitate data-driven decision-

making. Methodology for proposed work Shown in

Figure 1.

Figure 1: Methodology for Proposed Work.

3 MODULES

3.1 User Management Module

The User Management Module ensures secure and

role-based access to the system. It allows citizens,

government authorities, and NGOs to register, log in,

and manage their profiles. Users can edit their profile

details such as name, email, and location preferences.

The system implements authentication methods like

email verification or OTP-based login to ensure

security. Different user roles define functionalities,

where citizens can submit reports, and authorities can

ICRDICCT‘25 2025 - INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION,

COMMUNICATION, AND COMPUTING TECHNOLOGIES

358

view, validate, and update issue statuses for proper

resolution.

3.2 Water Issue Reporting Module

The Water Issue Reporting Module enables users to

report water-related problems like leakage,

contamination, flooding, and scarcity. Reports

include text descriptions, severity levels, geolocation,

and images. The system automatically captures GPS

location or allows users to manually enter it. Users

can also attach images for better verification of the

reported problem. The collected data is stored in a

centralized database, where it undergoes validation

and processing before further action is taken.

3.3 Data Validation & Filtering

Module

The Data Validation & Filtering Module ensures that

only authentic and relevant reports are processed.

Natural Language Processing (NLP) techniques such

as TF-IDF and BERT analyze text descriptions and

detect spam or irrelevant content. Image verification

using Convolutional Neural Networks (CNNs) like

YOLO and OpenCV helps confirm whether uploaded

images match the reported issue. Additionally,

geolocation-based clustering (DBSCAN) detects

duplicate reports within the same area, preventing

unnecessary redundancy and improving accuracy.

3.4 Interactive Map & Visualization

Module

The Interactive Map & Visualization Module

provides a real-time view of reported water issues

using GIS-based mapping. The system displays

reports on an interactive map with color-coded

severity markers. Users can filter reports based on

issue type, severity, or location to analyze problem

patterns. Heatmaps help authorities and NGOs

identify high-risk zones, allowing them to allocate

resources efficiently. This module improves

transparency and helps visualize problem hotspots in

real time.

3.5 Notification & Alert Module

The Notification & Alert Module ensures that critical

water issues are addressed promptly by alerting

authorities, users, and other stakeholders. The system

uses a priority queue algorithm to classify reports

based on urgency and send push notifications, emails,

or SMS alerts. Users receive real-time updates on

their reported issues, while government agencies and

NGOs are notified to take necessary action. The

module also includes an emergency alert system for

major problems like flooding or severe

contamination.

3.6 Issue Resolution & Tracking

Module

The Issue Resolution & Tracking Module allows

authorities to update the status of reported problems

and track their resolution. Reports can be marked as

“Pending,” “In Progress,” or “Resolved”, ensuring

transparency. Users can track progress and provide

feedback once the issue is resolved. The module also

maintains a record of resolved issues, helping

stakeholders analyze response time, resolution

effectiveness, and service quality to improve future

interventions.

4 ARCHITECTURE

Figure 2: System Architecture.

This Figure 2 provides a structured framework for

efficiently handling water-related issues, ensuring

seamless communication between users, authorities,

and other stakeholders. It enables real-time data

collection, processing, analysis, and dissemination,

allowing for quick issue identification and resolution.

The architecture is designed with multiple layers,

each responsible for specific tasks such as data

collection, transmission, aggregation, analytics, and

Clear Brook: A Mobile App that Crowd Sources Water-Related Problems from Around a Community and Display Them on a Man

359

dissemination. These components work together to

streamline the reporting process and enhance

decision-making in water management.

4.1 Data Collection

This phase focuses on gathering information from

users regarding water-related issues such as leakage,

contamination, or inadequate supply.

 Residents/Users: Individuals in a community

identify and report water-related issues using

mobile devices (smartphones or basic feature

phones).

 Mobile Device Interaction: Users submit

reports through a dedicated mobile

application, SMS, or a web portal.

 Telecom Network: The data is

transmitted through a telecom network

to ensure real-time reporting to the

server.

This stage ensures efficient and immediate issue

reporting, reducing delays in addressing water-related

problems.

4.2 Data Aggregation & Analytics

Once the data is collected, it is processed and

analysed to extract meaningful insights.

 Server: The collected reports are sent to a

centralized server, which manages, stores, and

processes the data.

 Data Processing: AI-based classification and

prioritization algorithms categorize reports

based on severity, location, and type of issue.

 Data Analytics: Advanced analytics

techniques are applied to detect patterns,

trends, and high-priority areas for immediate

action.

This stage ensures efficient data organization,

making it accessible to decision-makers for effective

planning.

4.3 Data Dissemination

In this phase, the processed data is shared with

stakeholders who can take corrective actions.

 Utility/Government Agencies: The

responsible authorities receive alerts and

notifications about reported water issues. They

deploy teams for repair and maintenance.

 NGOs: Non-governmental organizations

working in water conservation and

management receive data to assist in

community projects.

 Dashboard & Mobile Access: The analyzed

data is displayed on dashboards accessible via

mobile phones and computers. Users can track

reported issues, monitor resolution progress,

and stay informed about actions taken.

This phase ensures transparency, accountability,

and efficient problem-solving by involving multiple

stakeholders.

5 EXPERIMENTAL RESULTS

Figures 3 through 6 illustrate various aspects of the

system's performance and scope. Specifically, Figure

3 presents the Monthly Reports, Figure 4 displays the

Reports State, Figure 5 highlights the Range of Areas

Covered, and Figure 6 outlines the Future

Improvements envisioned for further development.

Figure 3: Monthly Reports.

Figure 4: Reports State.

ICRDICCT‘25 2025 - INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION,

COMMUNICATION, AND COMPUTING TECHNOLOGIES

360

Figure 5: Range of Areas Covered.

Figure 6: Future Improvements.

6 CONCLUSIONS

In conclusion, this system provides a structured and

efficient approach to addressing water-related

challenges through real-time issue reporting,

validation, and resolution. By leveraging geolocation

services, AI-powered analytics, and seamless

communication channels, it enhances public

participation and ensures that authorities can take

timely action. The integration of automated alerts and

GIS-based mapping further improves transparency

and accountability, allowing for better resource

management and faster response times. Ultimately,

this system empowers communities and decision-

makers to work together in ensuring sustainable and

proactive water management.

REFERENCES

A. Kumar and S. Gupta, "Smart water management using

IoT and cloud computing," IEEE Internet of Things

Journal, vol. 8, no. 4, pp. 3120-3132, 2021.

J. M. Shepherd, "Flooding: Causes, consequences, and

solutions," IEEE Earthzine, vol. 14, no. 2, pp. 1-6,

2022.

M. P. Gomez and L. J. Brown, "GIS-based mapping and

visualization of water issues in urban areas,"

International Journal of Geoinformatics, vol. 18, no. 3,

pp. 55-68, 2022.

Mujawar, M., Manikandan, S., Kalbande, M. et al.

Optimizing connectivity: a novel AI approach to assess

transmission levels in optical networks. J Supercomput

(2024).]

N. Al-Ghamdi and K. S. Al-Hassan, "AI-driven early

warning systems for water leak detection," IEEE

Sensors Journal, vol. 19, no. 10, pp. 2015-2027, 2021.

P. Rajalakshmi, A. Shankar, and R. Balaji, "A

crowdsourced approach for monitoring urban water

crises using mobile applications," Proceedings of the

IEEE International Conference on Smart Cities and

Sustainable Solutions, pp. 112-117, 2022.

R. K. Mishra and B. N. Patil, "Geospatial analytics for

water crisis prediction using AI-based decision support

systems," IEEE Access, vol. 9, pp. 145678-145691,

2021.

S. Wang, Y. Li, and X. Chen, "Real-time water quality

monitoring using machine learning and IoT sensors,"

IEEE Transactions on Industrial Informatics, vol. 17,

no. 6, pp. 4201-4212, 2021.

S.Manikandan, E.Elakiya, K.C.Rajheshwari, &

K.Sivakumar, "Efficient energy consumption in hybrid

cloud environment using adaptive backtracking virtual

machine consolidation", Scientific Reports, (2024)

14:22869

Saradhi Thommandru V, Suma T, Odilya Teena AM,

Muthukrishnan A, Thamaraikannan P, Manikandan S.

Intelligent Optimization Framework for Future

Communication Networks using Machine Learning.

Data and Metadata [Internet]. 2024 Apr. 18 [cited 2024

Apr. 21]; 3:277.

Clear Brook: A Mobile App that Crowd Sources Water-Related Problems from Around a Community and Display Them on a Man

361