Pradhan, Pratistha

Dr. Pratistha Pradhan is a Professional Engineer with 20+ years of experience in water, wastewater, and stormwater sectors. As Managing Director of PNA Technical Services, she leads efforts in hydraulic modeling, planning, asset management, AI/ML applications, and other digital solutions. Previously, she spent over a decade at Houston Water Planning, playing a key role in Houston’s EPA Consent Decree, infrastructure modeling/ planning, and advanced analytics platform development. She continues to support the City as a consultant and is an active contributor to industry conferences.

Titles from this speaker

From Data to Action: Bridging EAM and SCADA Data on AWS for Next-Generation Proactive Pump Management in Lift Stations

Abstract

Introduction: Pump failures in wastewater collection systems disrupt operations, escalate maintenance costs, and pose environmental risks. Addressing these challenges requires a detailed understanding of failure patterns, identifying high-risk areas, and predicting future needs to enable proactive decision-making. This study presents an integrated framework designed to clean and enrich Enterprise Asset Management (EAM) work orders (WO), integrate Geographic Information System (GIS) data, and link them with Supervisory Control and Data Acquisition (SCADA) pump status data. The framework generates predictive insights to identify high-risk areas, analyze failure patterns, and support proactive decision-making. The framework, built on modern data architecture principles using Amazon Web Service (AWS) data lake and advanced business analytics techniques, enables scalable, efficient data processing and utilization. Objective: These are main goals of this study: 1. Create a comprehensive list of pump failures by cleaning EAM WO and integrating it with GIS, and SCADA data. 2. Define and calculate pump failure metrics including failure frequency, repair time, and risk. 3. Cluster failures to major and minor using repair time 4. Predict future budgets, labor, and maintenance needs based on historical trends. 5.Identify temporal and spatial failure patterns to find high-risk areas and periods. Data Preparation and Methodology: The EAM dataset filtered to focus on pump-related failures, using keywords such as 'breakdown' and categories like 'PMP (mechanical)' and 'ELEC (electrical)', 'NO-COM (no communication)', to name a few. SCADA data validated EAM failure events and improved the accuracy of repair time calculations by verifying the pump off duration during the EAM WO. For matching entries, SCADA timestamps indicating when the pump turned on were used instead of the less reliable EAM completion timestamps to calculate repair times. Metrics such as the repair period (time interval between EAM event creation and SCADA pump restoration) and failure frequency were derived to evaluate failure trends. Failures were classified as major (repair time > 1 day) or minor (â‰¤ 1 day). Pump risk was defined by counting its past failures that required repair and by analyzing failure frequency. Eventually, GIS data enriched the dataset with spatial identifiers for lift stations, enabling spatial analysis, and risk map was generated. The framework incorporated AWS data lake capabilities to unify and process large volumes of data. AWS ensures the solution is scalable and reliable, supporting both current analysis and future expansions. Using exploratory data analysis, we identified temporal and spatial failure patterns, along with failure costs, failure counts, and the type of expertise required for pump repairs based on predicted pump failures. Results and Key Findings: Temporal patterns were analyzed to identify the days of the week with higher failure risks, enabling crew scheduling optimization. Spatial analysis using GIS data revealed hotspots of failures over time, highlighting areas that experienced recurring failure issues. The risk map was generated, and high risk hotspots were tracked to understand how failure distributions shifted across the city overtime and to identify locations requiring more focused maintenance efforts in the future. The analysis provided the following insights: - System performance: Pump failures have decreased year by year, indicating improved reliability. - Failure types: Mechanical and electrical issues dominate, emphasizing the need for more specialized labor in these fields. - Temporal patterns: Certain day of the week consistently exhibit higher failure rates, highlighting the higher failure probability on that day and need for targeted scheduling. - Hotspots: Spatial mapping identified shifting high-risk areas over the past few years, necessitating dynamic maintenance strategies. Predictive insights based on forecast key metrics, including: 1. Annual budgets for repairs and replacements. 2. Labor requirements for mechanical and electrical failures. 3. Failure trends and repair versus replacement probabilities. 4. High-risk locations for future failures. 5.High-risk day of the week to optimize crew scheduling. Conclusion: This framework integrates EAM WO, GIS, and SCADA data with AWS capabilities to deliver a scalable and efficient solution for analyzing and predicting pump failures in wastewater systems. It provides actionable insights into risk management, budget planning, workforce management, failure prevention, and maintenance scheduling. Spatial and temporal analysis enhance system reliability and resilience and reducing costs. The framework's adaptability makes it applicable to broader infrastructure management, supporting long-term optimization and sustainability.

This paper was presented at the WEF/WEAT Collection Systems and Stormwater Conference, July 15-18, 2025.

Presentation time

14:00:00

14:30:00

Session time

13:30:00

16:45:00

SessionSmarter Sewer Systems: Innovations, Efficiency, and Safety

Session number18

Session locationGeorge R. Brown Convention Center, Houston, Texas, USA

TopicAsset Management, Historical Data Analytics, Reliability Centered Maintenance

Author(s)

Maymandi, Nahal, Rabbi, Fazle, Pradhan, Pratistha, CAO, BO

Author(s)N. Maymandi1, F. Rabbi2, P. Pradhan3, B. CAO4

Author affiliation(s)IMS Engineers, 1City of Houston, 2PNA Technical Services, 3STV Inc, 4 ,

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Jul 2025

DOI10.2175/193864718825159862

Volume / Issue

Content sourceCollection Systems and Stormwater Conference

Word count20

Indigenous Fine-Scale Modeling: Innovating Houston's Wastewater Management

Abstract

Background Metropolitan areas are prone to several challenges in the wake of rapid urbanization and climate change impacts. Aging wastewater infrastructures have been overburdened in many cities globally, causing increase in Sanitary Sewer Overflows (SSOs) under rainfall events mainly due to inflow and infiltration. To overcome this situation, a robust modelling approach is required, enabling exact determination of SSOs and identifying sewers with capacity limitations. Moreover, by simulating various scenarios, the model can suggest optimal configurations of the network, which may include resizing pipes, modifying slopes, or changing operational strategies to enhance system efficiency. Study Area Houston, one of the fastest-growing cities in the USA, is confronted with significant challenges in managing the increasing demands on its wastewater infrastructure. With a population of approximately 2.5 million and a daily wastewater generation of around 250 million gallons, the city relies on 38 wastewater treatment plants owned by the City of Houston (COH) to handle and treat this vast volume. Each plant is assigned to a specific service area, with associated pipe networks collecting the wastewater. However, the growing demands, fluctuating water consumption, variable weather patterns, and evolving land use exacerbate the difficulties in maintaining these collection systems. Methodology Initially, Houston's Wastewater Collection System model in Info-Works ICM software included 10-inches and larger pipes and associated infrastructures. However, overflows in areas served by smaller diameter pipes and redevelopment happening in already stressed areas required attention to more granular level. To address these challenges, COH started using in-house developed customized SWMM model for small diameters pipes in the basin level using City's discharge criteria and generalized derived RTK parameters. Recently, City focused on the development of high-resolution dynamic models capturing the flows at parcel level for sewer basins using Info-works ICM software. These models replicate the physical network infrastructure, including gravity mains, force mains, lift stations, manholes, weirs, etc., based on comprehensive data sources such as as-built drawings, field surveys, engineering estimates, and lidar data. These models employ advanced techniques to enhance wastewater management and system accuracy. They incorporate the Groundwater Infiltration (GWI) model in ICM to accurately assess and quantify infiltration into the sewer network. This methodology is vital for identifying vulnerable points where groundwater enters the system, allowing for targeted infrastructure improvements and maintenance scheduling. Additionally, the model replicates pump operations by leveraging extensive SCADA (Supervisory Control and Data Acquisition) data. This approach ensures that the simulation of pump behaviors closely mirrors real-world conditions, enabling more precise management of flow dynamics within the network. By integrating real-time operational data, our model optimizes the performance and reliability of pump stations, directly impacting overall system efficiency and reducing the risk of overflows and system failures. For sewer basins with potential capacity issues, additional flow monitors are added in the system, and models are updated and re-calibrated to ensure the models reflect the latest data from strategically placed flowmeters, capturing variations in flow under dry, wet, and extreme conditions. Presently, many high-priority sewer basina models are being updated with latest data. Dry weather calibrations focus on flow unaffected by rainfall, while wet weather calibrations are essential for evaluating network capacities by aligning with peak flows and groundwater inflows during rain events. The model results are evaluated against the tolerance limits of +20% and -10% for flow volume, +10% and -10% for baseflow and +25% and -15% for peak flow during rain event. Once calibrated, these models facilitate predictive analysis of future flow scenarios, thereby enhancing the COHs preparedness and resilience. Results The fine-scale ICM modelling offers a fine-grained approach to managing wastewater networks by providing detailed simulations at the individual parcel. This high-resolution modeling enables precise detection of SSOs, identification of surcharged pipes, and comprehensive assessment of system performance under various conditions. By assigning flow directly from each parcel based on specific land use and occupancy data, the model surpasses the capabilities of lower-resolution models, which often rely on aggregated or averaged data inputs. Consequently, these models deliver significant advantages in terms of reliability, predictive accuracy, and the granularity of data available for decision-making compared to more traditional, less detailed models, thereby, playing a pivotal role in proactive infrastructure management and strategic planning to address Houston's growing needs. Future Work For future, these models will be equipped to utilize real-time data, enabling dynamic and responsive management of wastewater systems. By continuously integrating data from sensors and SCADA systems, these models will provide up-to-the-minute simulations of network conditions, allowing for immediate adjustments in operations and emergency responses. Additionally, automated processes for data validation and error checking will be implemented to maintain the integrity and reliability of model outputs. This approach will not only optimize system efficiency but also enhance the predictive capabilities of the models, leading to more informed decision-making and improved system resilience in the future.

This paper was presented at the WEF/WEAT Collection Systems and Stormwater Conference, July 15-18, 2025.

Presentation time

09:30:00

10:00:00

Session time

08:30:00

10:00:00

SessionLeveraging Digital Tools for Stormwater Resilience

Session number10

Session locationGeorge R. Brown Convention Center, Houston, Texas, USA

TopicConsent Orders, Wastewater Management, Wet Weather

Author(s)

Ahmad, Junaid, Rabbi, Fazle, Mejia, Lesny, Pradhan, Pratistha, Borse, Pranjali, Rajbhandari, Deepa

Author(s)J. Ahmad1, F. Rabbi2, L. Mejia2, P. Pradhan3, P. Borse4, D. Rajbhandari2

Author affiliation(s)Arcadis, 1City of Houston, 2City of Houston, 2PNA Technical Services, 3Civitas, 4City of Houston Dept. of Public Works, 2,

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Jul 2025

DOI10.2175/193864718825159864

Volume / Issue

Content sourceCollection Systems and Stormwater Conference

Word count8

Over 10-years of Houston's Digital Transformation Journey led by Domain Specialists

Abstract

The City of Houston (COH) is the largest City in the state of Texas and the fourth largest city in the United States of America. Houston Water owns and operates 39 Wastewater Treatment Plants (WWTPs) with a combined permitted capacity of 564 MGD. The wastewater collection system includes approximately 5,900 miles of sewer pipes, 133,000 manholes, and 380 lift stations spread out over 650 square miles to serve about 2.3 million customers. Wastewater Infrastructure Planning (WWIP) Team within Houston Water (HW) is responsible for providing support to Wastewater Operations with long-range Strategic Plans, 1-10 year CIP plans, Strategic Asset Management Planning. The WWIP team consisting of a handful number of in-house hydraulic modelers, engineering planners and analysts along with augmented consulting staff have been utilizing enormous volume of data from various sources, both internal to the city and external. Over the past decade, the WWIP team has developed an array of tools accessible through their in-house platform, the "Advanced Infrastructure Analytics Platform (AIAP)." This platform provides operators and planners with access to the necessary datasets and associated insights for informed decision-making. Analytical results are presented in pre-defined dashboards accessible via web browsers. This capability enables infrastructure analytics, identification of needs, improvement planning, operational and development support, as well as strategic asset management (SAM). The internal data sources in AIAP include GIS database for wastewater infrastructure assets, Work Order Management systems for collection system and treatment facilities, Operational Performance and Regulatory databases, SCADA database that includes operations of the lift stations and wastewater treatment plants, CIP Project Management Database, Field Inspections, Flow meter, Level meter and Rainfall Databases, Hydraulic Capacity Modelling and Planning Databases, Infrastructure Condition Assessment Databases, and more. Additionally, external databases include existing land use and parcel profiles from County Appraisal Districts, population, employment and land use projections from HGAC, Rainfall from HCFCD, soil characteristics from NRCS and NASA, demographic, housing, social and economic characteristics from US Census, regulatory information for contract customers from TCEQ and EPA, flood hazard information from FEMA, stream gage data from USGS, among others.

Houston Water's digital transformation journey spanning over a decade is centered around automation, self-service data analytics, self-serve data management, and efficient data sharing. It involves developing an advanced infrastructure analytics platform by utilizing no-code, low-code tools like Power BI, Dataflows, Web GIS, AWS Data Lake, and scripting languages with SQL and Python through seamless integration of various databases maintained by the City of Houston and its partner organizations.

SpeakerRabbi, Fazle

Presentation time

14:00:00

14:30:00

Session time

13:30:00

15:00:00

SessionStories of Digital Transformation: It's Not About the Journey but the Destination

Session locationRoom S403b - Level 4

TopicIntermediate Level, Utility Management and Leadership

Author(s)

Rabbi, Fazle

Author(s)F. Rabbi 1; P. Pradhan 1 ; F. Rabbi 1;

Author affiliation(s)Houston Wate 1; Houston Water 1 ; Houston Water 1; Houston Water, Houston Public Works, City of Houston, Texas, USA ;

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Oct 2023

DOI10.2175/193864718825159106

Volume / Issue

Content sourceWEFTEC

Word count12

Prescriptive Stage Unlocked: Houston's Wastewater Digital Transformation in Action

Abstract

Houston's wastewater system is a vast network that serves over 2.3 million residents through 38 wastewater treatment plants, over 375 lift stations, and 6,100 miles of gravity sewers. Managing such a complex system presents significant challenges-not just in day-to-day operations, but in meeting stringent regulatory requirements, including a federal Consent Decree. To overcome these hurdles and prepare for a future beyond compliance, Houston has embraced a revolutionary approach, transitioning its wastewater management into the prescriptive stage of digital transformation. At the heart of this transformation is Houston's Advanced Infrastructure Analytics Platform (AIAP), a 'Platform of Platforms' built on AWS Data Lake. Over the past decade, Houston's wastewater planning team has led this journey, now achieving the prescriptive stage. AIAP integrates diverse datasets, real-time monitoring, and advanced technologies such as artificial intelligence (AI) and machine learning (ML) to provide actionable, prescriptive solutions. This milestone marks a significant evolution in wastewater management, where challenges are not just identified or predicted but they are resolved through proactive, data-driven prescriptions that optimize operations and improve long-term sustainability. By integrating systems such as GIS asset inventories, hydraulic models, SCADA data, Hach-WIMS, work orders, and real-time API streams (including rainfall data, flow meters, and level sensors), Houston has developed a comprehensive view of its wastewater system within the AIAP platform. Now, through prescriptive analytics, the platform provides recommendations-whether it's optimizing pump operations, preventing sanitary sewer overflows (SSOs), or prioritizing maintenance activities by using traffic-light decision framework to provide actionable insights. These insights are transforming how Houston manages resources, meets regulatory goals, and ensures system resilience. This presentation will explore Houston's wastewater digital journey to the prescriptive stage, focusing on the tangible impact of AIAP on the city's operations. Attendees will see how prescriptive solutions are driving smarter decisions, reducing costs, and ensuring compliance, all while improving service delivery and sustainability. This presentation focuses on Houston's implementation of prescriptive analytics within the Advanced Infrastructure Analytics Platform (AIAP), showcasing its transformative impact on wastewater management. Key objectives of presentation: 1.Delivering Actionable Recommendations with Integrated Data: Exploring how AIAP unifies diverse datasets to generate prescriptive recommendations for system maintenance, asset management, and capacity planning. 2.Optimizing Wastewater Operations Through Prescriptive Analytics: Showcase how Houston leverages prescriptive analytics to optimize wastewater operation and prevent SSOs in real time. 3.Proactive Decision-Making with predictions using AI and ML: Showcasing how AI and ML drive proactive and automated solutions to address system inefficiencies while meeting regulatory compliance. 4.Providing a Scalable Model for Prescriptive Solutions: Sharing Houston's lessons learned to demonstrate how prescriptive analytics can be applied to wastewater systems in other cities. Real-World Applications of Prescriptive Analytics: Houston's AIAP platform demonstrates its prescriptive capabilities through advanced tools designed to resolve challenges before they escalate: 1.Dry Weather SSO Prevention: Leveraging real-time data to predict and prevent overflows through prescriptive adjustments in system operations. 2.Hydraulic Modeling for Capacity Management: Utilizing prescriptive analytics to identify capacity limitations and recommend targeted infrastructure upgrades to support growth. 3.Asset Management Optimization: Generating prescriptive strategies for prioritizing maintenance and repairs, ensuring asset reliability and preventing failures. 4.Operational Efficiency Enhancements: Guiding efficient operations at lift stations and WWTPs through prescriptive recommendations. This presentation highlights how Houston's prescriptive analytics capabilities are driving smarter, proactive decisions, optimizing system performance, and setting the stage for sustainable wastewater management. Attendees will gain practical insights into how prescriptive analytics can be harnessed to transform infrastructure management. In addition, the presentation will also showcase the application of Generative AI chatbot within city's AWS data lake. Conclusion: Houston's transition to prescriptive wastewater management has unlocked a new level of efficiency and innovation. By leveraging AIAP's prescriptive capabilities, the city can proactively address infrastructure challenges, prevent costly SSOs, and manage capacity with precision. This approach not only ensures regulatory compliance but also delivers better service to residents while fostering long-term sustainability. The prescriptive stage represents a pivotal milestone in Houston's digital transformation-a stage where data-driven solutions are no longer just insights, but actionable steps that drive measurable impact. This presentation will inspire attendees with Houston's story, offering practical takeaways and a roadmap for small and larger municipalities and utilities looking to unlock their own prescriptive potential. It will also showcase the application of Generative AI chatbot within the city's AWS data lake.

This paper was presented at the WEF/WEAT Collection Systems and Stormwater Conference, July 15-18, 2025.

SpeakerPradhan, Pratistha

Presentation time

08:30:00

09:00:00

Session time

08:30:00

10:00:00

SessionDigital Innovation for Smarter Wastewater Management

Session number12

Session locationGeorge R. Brown Convention Center, Houston, Texas, USA

TopicInnovative Technology, Machine Learning, Optimization

Author(s)

Pradhan, Pratistha, Rabbi, Fazle

Author(s)P. Pradhan1, F. Rabbi2

Author affiliation(s)PNA Technical Services, 1City of Houston, 2

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Jul 2025

DOI10.2175/193864718825159895

Volume / Issue

Content sourceCollection Systems and Stormwater Conference

Word count10

Upgrading Lift Station Maintenance: Predictive Insights Through SCADA and AWS Data Architecture for Advanced Infrastructure Management

Abstract

Introduction: Houston's wastewater infrastructure is vast and complex, serving over 2.3 million residents through 38 wastewater treatment plants, approximately 380 lift stations, 5,800 miles of gravity sewers, and 134,000 manholes. In addition to the operational challenges inherent in maintaining such a large system, Houston must also comply with strict regulatory requirements under a federal consent decree, which mandates specific actions to prevent sewer overflows and improve aged infrastructure for system reliability. While addressing the compliance of consent decree, the city is also preparing for the period beyond the consent decree, it is adopting a forward-thinking approach to infrastructure management that emphasizes operational efficiency, regulatory compliance, and resilience. To address these demands, Houston is applying advanced technologies such as data integration, real-time monitoring, and predictive analytics through the Advanced Infrastructure Analytics Platform (AIAP), a 'Platform of Platforms' built on AWS Data Lake. AIAP integrates multiple operational and planning systems to enhance data-driven decision-making and optimize the management of the city's wastewater infrastructure. This paper explores how Generative AI, with a specific focus on Retrieval-Augmented Generation using AWS Bedrock and Amazon Q, is transforming Houston's wastewater management, providing critical insights into operational performance, predictive maintenance, and long-term infrastructure planning. LS plays a vital role in areas where gravity alone cannot transfer wastewater effectively. LS pumps act as the heart of the system, maintaining flow and preventing backups. Any disruption can ripple through the system, impacting public health and safety. By applying predictive failure methods, we can better protect these essential components and the communities they serve. Objective: The primary goal of this study is to: - Determine the status of LS operations at Risk using SCADA data - Develop pump downtime risk profiles for each LS to serve as a baseline - Track and evaluate the risk profiles against the baselines - Suggest a predictive maintenance plan This study presents a data-driven method using SCADA pump status data to monitor and evaluate the performance and risk of LS. By detecting the early signs of potential failures, it enables proactive maintenance and necessary actions. The approach provides a structure risk analysis, helping to prioritize maintenance for inefficient LS, effectively focused on the most critical issues to prevent high-risk incidents. Data preparation/methodology: We conducted EDA on historical pump performance data and developed a risk analysis approach that categorizes lift station conditions into three levels (Normal, Medium, and High Risk) based on SCADA data, considering the number of pumps in each station and their downtime, with traffic light colors representing each level. This method pinpoints high-risk stations, forecasts future risks based on current conditions, and culminates in a risk matrix to evaluate the station's condition based on pump functionality. To streamline the approach, a modern data architecture was implemented, with AWS data lake features being used to collect, store, and process large datasets. Scalability and reliability in analytical workflows were ensured by this setup, which efficiently managed extensive data volumes. Results/Key Findings: By analyzing SCADA data on pump operational hours, downtime frequency and the total number of operating pumps at each LS, the system identifies how many pumps are functioning well and how many are not. To assess LS risk based on pump performance, a new method is established, using traffic light indicators to classify LS conditions into three categories: normal operation (Green), medium risk (Yellow), and high risk (Red). Historic data revealed that stations transitioning from Green to Yellow often progress to Red without prompt intervention, highlighting the potential for early risk forecasting. Our method was validated using Sanitary Sewer Overflow (SSO) data, which commonly occurred near lift stations with higher pumps downtime risks. Discussion/Implications: The findings of this study contribute significantly to the field of wastewater infrastructure management by offering a proactive, data-driven approach to LS health assessment. The predictive risk model facilitates timely interventions, reducing the likelihood of pump failures and system overflows. This approach also enables better resource allocation by prioritizing high-risk LS for further assessment or renewal. Conclusion: In conclusion, this study introduced a novel approach to assess the health of LS and predict early warning signs of risk using SCADA pump status data, enabling intervention before the stations reach the danger zone. To achieve this goal, EDA techniques were applied alongside AWS data lake capabilities. The study enables proactive maintenance strategies by identifying underperforming LS, while systematically maintaining and upgrading assets to promote long-term sustainability.

This paper was presented at the WEF/WEAT Collection Systems and Stormwater Conference, July 15-18, 2025.

Presentation time

11:15:00

11:45:00

Session time

08:30:00

11:45:00

SessionInnovations in Texas Water Infrastructure

Session number21

Session locationGeorge R. Brown Convention Center, Houston, Texas, USA

TopicOperations And Maintenance, Risk Management, SCADA

Author(s)