Rabbi, Fazle

Fazle Rabbi, PE with over 35 years of experience in water resources and municipal utilities infrastructure planning and management serves Houston...

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About Rabbi, Fazle

Fazle Rabbi, PE with over 35 years of experience in water resources and municipal utilities infrastructure planning and management serves Houston Water as Managing Engineer. His team is responsible for Wastewater Modeling, Long-range and CIP Planning, Operations support, Asset Management and Advanced predictive analytics. Fazle has been leading the Houston Water’s Digital Transformation Journey over a decade that includes Self-service Data Management and Predictive Analytics to handle big-data through a distributed workforce. Fazle Rabbi is a licensed professional engineer in the state of Texas.

Rabbi, Fazle

Titles from this speaker

Data Centric "Smart" Digital Platform for Wastewater Treatment Plant Proactive Optimization and Maintenance

Abstract

Managing utilities operations effectively in the era of big data involves application of emerging technologies, tools, and modern practices. Houston's in-house Wastewater Infrastructure Planning team incorporates technological advancement in its daily operations. 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 38 Wastewater Treatment Plants (WWTPs) with a combined permitted capacity of 564 MGD. The wastewater collection system includes approximately 6,000 miles of sewer pipes, 133,000 manholes, and approximately 380 lift stations spread out over 665 square miles area to serve about 2.3 million customers. Wastewater treatment plants (WWTPs) have long played a crucial role in safeguarding both the environment and public health. The conventional practices of operation and maintenance (O&M) within WWTPs have been predominantly reactive in nature. This reactive approach has created challenges for site staff operating in the field, as they grapple with noisy Supervisory Control and Data Acquisition (SCADA) systems and information overload. This often results in decisions being based on gut instinct rather than data-driven insights. In this context, plant operators typically become aware of issues only when equipment fails or performance reviews uncover underperformance, leading to a reliance on lagging performance indicators and a heavy focus on troubleshooting rather than proactive modeling and operations. To ensure the long-term sustainability and effectiveness of wastewater treatment plants, decision-makers require real-time visibility into plant operations with leading performance indicators that can enable them to optimize performance in a proactive manner. The harnessing of near real-time data from sensors, SCADA systems, IoT devices, and Work Order Management Systems presents an opportunity for WWTP operators to comprehensively understand their systems' performance using innovative digital tools that simplify rather than complicate their daily tasks. Over the past decade, COH has undergone a digital transformation of its wastewater management system. COH has developed an Advanced Infrastructure Analytics Platform (AIAP) which includes all data and analytics needed for wastewater infrastructure planning. This presentation discusses an early initiative by the planning team of COH aimed at developing a Data-Centric 'Smart' Operations and Maintenance platform. This approach begins with the essential step of integrating multiple databases or systems to gain a holistic understanding of the entire WWTP operation and maintenance landscape. In line with the City's Strategic Asset Management framework, this integration brings together the WWTP SCADA, HachWims, Work Order Management System, and Asset's Physical Attributes and Conditions into a unified system. This fundamental integration enables analysts to empower stakeholders to comprehend the status of WWTP operations and maintenance from multiple perspectives. The next critical step involves the development of comprehensive O&M Key Performance Indicators (KPIs) that serve as vital metrics for monitoring system performance. These KPIs, derived from the integrated databases, enable operators and decision-makers to assess the overall health of the WWTP system accurately. To transition towards a proactive mode of operation, advanced analytics and predictive machine learning algorithms are deployed to forecast influent characteristics. These forecasts, combined with real-time data from sensors and the SCADA system, allow for dynamic adjustments to energy consumption, chemical usage, and other operational KPIs. This dynamic approach ensures compliance with environmental regulations while enhancing operational efficiency. Moreover, a machine learning model developed based on the Work Order Management System enables predictive maintenance. It anticipates equipment failures, enabling maintenance teams to address issues before they escalate into costly breakdowns. By embracing proactive operation and maintenance, the integrated approach optimizes resource allocation across the board. This would eventually include personnel scheduling, chemical dosing, and various other aspects of WWTP operations, resulting in substantial cost savings. In conclusion, this presentation underscores the transformative potential of data-centric O&M for WWTPs. By adopting proactive strategies grounded in real-time data and predictive analytics, WWTPs can significantly enhance their efficiency, reduce operational costs, and bolster environmental stewardship. The transition towards data-centric O&M is a crucial step towards ensuring the long-term sustainability and effectiveness of wastewater treatment plants, positioning them as integral components of smart, forward-thinking cities. The tasks identified below are some examples of lesson learned:

*Collaborate with the WWTPs operation team to grasp the challenges and requirements for proactive optimization and maintenance.

*Understand, structure, and establish data systems to facilitate data modeling and integration.

*Utilize Business Intelligence tools such as Power BI and Python for exploratory data analysis and visualization.

*Develop data pipelines for seamless automatic data updates and document procedures to enhance digital transformation effectiveness.

Houston's wastewater planning adopts a data-driven approach, integrating various systems for a comprehensive operational view. Key Performance Indicators (KPIs) are utilized for precise performance evaluation. A machine learning model predicts influent characteristics, enabling proactive WWTP operations. This data-centric strategy optimizes resource use, enhances efficiency, and reduces costs.

SpeakerCao, Bo

Presentation time

14:30:00

15:00:00

Session time

13:30:00

15:00:00

SessionAutomation and Analysis: Data-Driven Strategies Improve Utility Processes

Session number610

Session locationRoom 349

TopicAsset Management, Business Organization and Technology Transformation, Intelligent Water, Intermediate Level

Author(s)

Cao, Bo, Paudel, Pratistha, Baldwin, Jennifer, Rabbi, Fazle

Author(s)B. Cao1 P.P. Paudel2, F. Rabbi3, J.D. Baldwin4, F. Rabbi3, P.P. Paudel2

Author affiliation(s)1STV, TX, 2Burns & McDonnell, TX, 3City of Houston, TX, 4Jacobs Solutions Inc., TN

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Oct 2024

DOI10.2175/193864718825159675

Volume / Issue

Content sourceWEFTEC

Word count14

Description: Houston Developed an Innovative Risk-based Strategic Asset Management Platform for...

Houston Developed an Innovative Risk-based Strategic Asset Management Platform for its 39 WWTP and 382 Lift Station Facilities for O&M and CIP Planning

Abstract

The City of Houston is the fourth largest city in the nation. Houston Water (HW) within the Houston Public Works (HPW) department operates and maintains a large water and wastewater system. Its wastewater system includes approx. 39 treatment plants (WWTP) with a combined permitted capacity of 564 MGD, 5900 miles of sewer pipes, 127,000 manholes, and 383 lift stations spread out over 650 square miles to serve about 2.3 million customers. Its water system includes approx. 7200 miles of waterlines, 3 surface water plants with a combined production capacity of 630 MGD and 56 groundwater plants with a combined production capacity of 270 MGD to serve about 4.5 million customers in the Houston region. The operations of these huge infrastructure facilities are monitored continuously, and data are used for operational decisions, regulatory reporting, as well as long-term planning. Houston Water Planning (HWP) team within Houston Water is responsible for long-range strategic planning, 1-10 year CIP planning and operational planning. HWP uses data including asset inventory, asset conditions, day-to-day O&M information, utility infrastructure improvements and regional planning data for different ranges of planning for both water and wastewater infrastructure. Houston Water has a long history of using state-of-the-art technologies for planning and management of its water and wastewater infrastructure. Since the mid-eighties, it has used flow-monitoring and hydraulic modeling for infrastructure capacity evaluation and planning of additional capacity needs. Since the mid-nineties, it has used enterprise GIS to document all infrastructure assets which provides the backbone for linear assets computerized work order management system. Since the mid-2000s, it has used Infor Enterprise Asset Management (EAM) to document facilities asset inventory and day-to-day work order management. For the last few years, HWP team has been supporting wastewater operations in developing and implementing Strategic Asset Management Plan. In August 2017, Hurricane Harvey brought in massive amount of rainfall up to approximately 50 inches in some area of Houston, which flooded many wastewater facilities including treatment plants and lift stations. Houston conducted the preliminary damage assessment just after the Hurricane. However, the damage to the system was extensive and Houston decided to do a detail condition assessment of the facilities to document the pre and post flooding condition. Houston Water Planning step in the process to help wastewater operations and create the foundation of the condition assessment and make it efficient and effective such that the massive data collected could also be seamlessly used for the City's goal of Strategic Asset Management. Figure 1 shows the conceptual framework of the wastewater facilities condition assessment which is a subset of the overall Strategic Asset Management Framework. Entire condition assessment project implementation for facilities involved 26 consulting teams. To manage the massive activities for this project and to ensure uniformity of the process followed by each of the 26 consultants, who will be utilizing various existing asset databases including InforEAM, the Houston Water Planning developed a unique and innovative approach. Utilizing the systemwide facilities asset condition data, HWP team recently completed a risk-based strategic need assessment for O&M and CIP Planning Platform. The Platform provides list of asset needs interactively based on the user-defined criteria set by the group of O&M and Planning staff. Houston's experience of developing such an innovative risk-based strategic asset management platform from inception to implementation to will be shared with the water utilities communities.

The following conference paper was presented at WEFTEC 2021, October 16-20, 2021. To read the full abstract, see "Abstract" tab below.

SpeakerRabbi, Fazle

Presentation time

14:30:00

15:00:00

Session time

13:30:00

15:00:00

SessionSetting the Groundwork for Success in Your Asset Management Program

Session number104

TopicAsset Management, Intelligent Water, Resilience, Disaster Planning and Recovery

Author(s)

Fazle Rabbi

Author(s)F. Rabbi1;Dr. P. Pradhan2;S. Dunne3;A. Shokouhi4;

Author affiliation(s)Houston Water, Houston Public Works, City of Houston, TX1,2,3,4

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Oct 2021

DOI10.2175/193864718825158177

Volume / Issue

Content sourceWEFTEC

Word count24

Description: Houston Develops and Implements An Automated Process For Collection System Risk...

Houston Develops and Implements An Automated Process For Collection System Risk Assessment and Mitigation Planning Using An Integrated Analytics Platform, InfoAsset Planner, and Artificial Intelligence

Abstract

Background
City of Houston is the fourth largest city in the United States of America. Houston Water (HW), within Houston Public Works (HPW) department of the City of Houston, facilitates wastewater services to approximately 2.3 million customers. HW maintains wastewater collection system that includes approximately 31 million LF of sewer pipes, 127,000 manholes, 385 lift stations, and 39 treatment plants. Houston Water Planning (HWP) team within Houston Water develops strategies and plans for O&M and CIP projects.
Houston Water applies cutting edge technologies for forecasting future scenarios utilizing advanced models including Geographical Information System (GIS), Info-Works ICM, Info-Asset Planner, Business Intelligence tools and techniques, and others. HPW recognizes the importance of asset and data centric planning using both capacity and condition for operational and capital decisions to optimize cost and increase level of service and comply with regulatory requirements.
This paper illustrates the process of information integration and system implementation for risk-based asset management of wastewater collection system for the City of Houston. Conventional approach to infrastructure asset management relies on time-based responsive and preventive maintenance, and reactive decisions that involve high rates of inaccuracy and inefficiency. The ever-increasing volume of data from multifaceted sources and technologies and availability of AI/ML based predictive models enhance the opportunities for City of Houston Wastewater Operations to venture into predictive and prescriptive approaches with the objective of lowering cost, reducing system downtime, and improving level of service.
Commonly used approach to detect wastewater asset failures and consequent rehabilitation decisions are based on age-dependent models of pipe attributes, location, repair, and failure history. With the availability of CCTV condition data along with numerous physical and asset performance information, and analytical tools like GIS, AI, ML, BI, InfoAsset Planner, and cloud-based computing environment, Houston Water envisions a data-centric predictive planning framework to optimize comprehensive long-term social, environmental, and economic perspective of planning decisions. The current planning process for operation and maintenance activities are designed at basin-based aggregated level. The proposed planning process will enable micro to macro level planning in roll-up and drill-down fashion capable of addressing the condition of individual asset to the system level from a single platform. Analytical Framework Data-centric predictive planning is built on analysis of asset capacity and condition assessment for sewer system with physical characteristics of the assets, spatial attributes, CCTV data, and other performance data. Spatial and asset related attributes of the wastewater system are stored in GIS system that is regularly updated. HPW plans to conduct CCTV condition assessment of 4 to 5 million LF of sewer pipes every year that is transcribed into digital form using AI based automated process and integrated in the planning module.
Condition based planning system is designed using open and flexible architecture to facilitate seamless modification and expansion. In the initial stage, the system is constructed on ArcGIS platform along with InfoAsset Planner and integrated with business analytics platform (Power BI) and custom modules built on Python. The process is in line with ISO 55000, International Infrastructure Management Manual (IIMM), EPA SIMPLE and NASCCO standards for CCTV condition assessment. The analytical procedure is based on asset risk calculation for individual pipe segments using relevant parameters to estimate Consequence of Failure (COF) and Likelihood of Failure (LOF). The process involved developing a linear model to estimate relative weights for LOF parameters based on the relationship between PACP condition scores and estimated risk for the assets for which PACP data was available. The estimated weights were used for citywide pipe segments risk calculation. The decision trees were developed by City's wastewater operations and planning staff knowledgeable about the system. Decision trees were used in InfoAsset Planner for determining appropriate rehabilitation actions through a consistent and unified process. The individual high-risk pipe segments are grouped for project scoping using rule-based integration and prioritization.
Rehabilitation actions can be agglomerated into integrated projects satisfying constraint that include budget allocation, proximity, action type etc. Furthermore, the projects can be scheduled, automated workorders can be framed, and work-process can be monitored for resource allocation through smart work manager. Also, project costs are estimated and fed into Life-Cycle Cost Analysis (LCCA) module to reach at optimum long-term investment decisions. The process also involves asset deterioration model to capture the effects of timing of rehabilitation decisions. The integrated planning process is depicted in Figure 1 Application of the Condition-Based Planning Process
The designed asset condition-based planning process is being implemented for the Houston's sewer pipe network. Based on the preliminary results, approximately 1.63 and 3.08 percent of the total sewer network is in Extreme and High-risk level respectively. The proposed planning process enable authorities to identify segments that need inspection and take preemptive action to avoid failure.
Conclusion and Plans for the Future Development
The paper presents the preliminary design and implementation for condition-based asset management framework for the wastewater collection system in the City of Houston. The developed system will enable HPW to enforce more efficient predictive actions to reduce pipe failures. The risk-based asset management will be further expanded to include the followings in the next stages of development. - Optimum infrastructure rehab strategy formulation under different scenarios through simulation process. Integrated simulation may be executed through use of scripting language. - Extended analytics for decision making can be implemented through integration of Business Analytics tools (Power BI or similar) and InfoAsset Planner. Automated data-exchange between backend asset condition simulation under different budget scenario and front-end analytics will reduce the gap between analysts and stakeholders and enable judicious and optimal decision making. - Integration of cloud-based data collection and repository can be integrated into real-time monitoring and rehab action activation.

SpeakerRabbi, Fazle

Presentation time

14:00:00

14:25:00

Session time

13:30:00

15:00:00

TopicAdvanced Level, Asset Management, Utility Management and Leadership

Author(s)

Rabbi, Fazle

Author(s)Jobair Alam1; Rabbi Fazle2; Pratistha Pradhan3; Jack Canfield4

Author affiliation(s)IMS Engineers, Houston, TX1; City of Houston, Houston, TX2; City of Houston, Houston, TX3; City of Houston, Houston, TX4

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Oct 2022

DOI10.2175/193864718825158639

Volume / Issue

Content sourceWEFTEC

Word count26

Description: Houston's Effective Approach in Tracking and Preventing SSOs using Web-based Dynamic...

Houston's Effective Approach in Tracking and Preventing SSOs using Web-based Dynamic GIS and Power BI.

Abstract

The City of Houston is the fourth largest city in the nation. Houston Water within the Houston Public Works department operates and maintains a large water and wastewater system. Its wastewater system includes approx. 39 treatment plants (WWTP) with a combined permitted capacity of 564 MGD, 5900 miles of sewer pipes, 127,000 manholes, and 385 lift stations spread out over 650 square miles to serve about 2.3 million customers. City of Houston's 311 Call Center receives an average of 1800 wastewater customer complaints per month. Issues may range from odor complaints, cave-ins to sewer backups in bathtubs. Houston Water is responsible for addressing these issues by mitigating the impact on public health and environment. Houston Water has a long history of using state-of-the-art technologies for managing its huge water and wastewater infrastructure. For Wastewater, it relies on systems such as; Infor IPS to manage work orders, service requests and tracking SSO records, HachWIMS to manage regulatory reporting, Infor EAM for facilities work order management and Enterprise GIS to support asset management for collection system. Recently, Houston Water introduced ArcGIS Online and Business Intelligence Tool, Power BI in every aspect of full-cycle process- starting from receiving customers call to satisfactory resolution of the problems.

THE PROCESS
Initial Call It all starts when a possible SSO is reported to the City of Houston's 311 Call Center. The 311 operator generates a service request (SR) which is logged into the Infor IPS. Once a service request is initiated, the clock starts ticking for the first responders, wastewater inspections team to respond to the request. Depending on the type of request -- a priority level is assigned to each Service Request. As an example- 'High Priority' is assigned to an active SSO that is threatening to enter a storm drain. A 'Medium-Priority' may be assigned to an odor complaint. First Responders Wastewater inspectors are equipped with a tablet to notify them of newly created SR through Infor IPS dashboard and also to enter field data into the Infor system. Inspectors are responsible to identify the type of SSO- Public or Private, where it occurred, the volume, flow rate, status of manholes upstream and downstream of the incident location and the possible cause. Inspectors create an SSO incident in the Infor platform for the stoppage team to take further action. The start times and flow rate data are recorded in real-time and cannot be edited to maintain the integrity of data. Stoppage team- Further Investigation Stoppage team are equipped with jetting and vacuum trucks to clear a blockage, stop the SSO, clean and disinfect the area and report the confirmed cause of the SSO. Stoppage crew is also equipped with tablets to enter details of SSO such as confirmed cause, actions taken to mitigate the SSO, and request for work orders. Depending on the needs work order could be issued for televising the line or installing bypass pumps. Quality Checking the Data for Regulatory Purposes Data is validated and confirmed in a weekly SSO meeting before weekly reports are sent to State Regulatory Agency- TCEQ. The meeting is attended by various stakeholders that includes regulatory team, inspection and stoppage team, customer service and Houston Health Department (HHD). ArcGIS Online and Power BI Dashboards provide the real-time and historical reference information. Recurrence Prevention through Analysis Greater than 70% of all public SSOs are preventable SSOs as they are caused by the accumulation of grease and rags/non flushable wipes in the sewer pipelines. Repeat SSOs are plotted on a dynamic ArcGIS map to identify SSO hot spots. This makes it easier for the wastewater analysis group to target high priority areas for Preventive Maintenance (PM) cleaning. A sample hotspot snapshot is shown in Exhibit 1, which illustrates the effectiveness of the PM cleaning program by reducing the preventable SSOs from 63 SSOs in 2018 to 29 SSOs in 2019.

Tracking Wet Weather SSOs
SSOs are also caused due to hydraulic capacity constraints in the collection system. Power BI is used to track the high volume (over 100,000 gallons) and number of repeat SSOs (3 or more) occurred during wet weather in previous three years in one manhole, as shown in Exhibit 2. This information is used to develop a capacity remedial measures plan. Fats, Oil and Grease (FOG) Program Houston Water's FOG outreach group uses ArcGIS Online based tool for targeted campaigns. HHD administers FOG Generator Interceptor Inspection program. HHD maintains a database for the Grease Trap inspections that are used by the FOG Outreach group to identify target areas that includes apartment complex and Food Service Establishments. Both, HHD and FOG outreach group use similar density maps as seen in Exhibit 1 for their respective needs. Intelligent Manhole Covers Houston Water uses intelligent manhole covers to provide real-time water levels to pro-actively detect a potential SSO that helps to mobilize maintenance crews to prevent SSOs from occurring. The continuously monitored manhole covers provide information to determine the rate of accumulation of debris in the pipes that eventually helps to develop PM cleaning program. The data from the manhole covers along with the lift station SCADA data are also used for verification of the collection system hydraulic models. Business Intelligence software such as Power BI and ArcGIS Online Dynamic mapping tools are used to efficiently process, visualize, analyze and share the outcome to present in a meaning way to effectively use the resources. The experience and effectiveness of using these emerging techniques and technologies, some of which are free, would be beneficial for water and wastewater utilities of all sizes.

The following conference paper was presented at Collection Systems 2021: A Virtual Event, March 23-25, 2021.

SpeakerRabbi, Fazle

Presentation time

11:00:00

11:20:00

Session time

11:00:00

12:00:00

SessionInnovation

Session number1

Session locationLive

TopicCombined Sewer Overflow, Historical Data Analytics, Optimized CSO Control Strategies, Regulatory Compliance - Collection Systems, Smart Water Infrastructure, SSO Reduction, Utility Management

Author(s)

F. RabbiA. Varghese

Author(s)F. Rabbi1; A. Varghese2

Author affiliation(s)City of Houston, PWE1; City of Houston, PWE2

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Mar 2021

DOI10.2175/193864718825157709

Volume / Issue

Content sourceCollection Systems Conference

Word count16

Description: Houston's Intelligent Planning Tool for Prioritizing and Optimizing Rehabilitation...

Houston's Intelligent Planning Tool for Prioritizing and Optimizing Rehabilitation of Gravity Pipe

Abstract

Wastewater collection and treatment system plays a critical role in facilitating sustainable urban environment. Maintaining complex wastewater infrastructure, with varied range of assets and functionalities, is extremely challenging. For the City of Houston (COH), providing quality service in the wastewater sector is even more demanding due to rapidly increasing population and diverse industrial and commercial growth. To confront the challenge with modern technology, COH envisioned an intelligent planning tool to prioritize and optimize rehabilitation of its gravity pipe network by integrating asset inventory, field inspections and machine-learning models into a data-driven predictive analytics platform. This paper illustrates the underlying modules of the tool with their analytical framework. It also explains application of the tool to analyze future risk levels in different scenarios and optimize rehabilitation strategies.

The paper presents Houston Water's visionary initiative for design and implementation of an intelligent planning tool for optimum management of its wastewater collection system. The tool incorporates advanced analytics and visualization techniques to enable predictive planning for the rehabilitation of its extensive gravity pipe network. It leverages CCTV condition data, physical and asset performance information, and analytical tools like GIS, AI, ML, BI, InfoAsset Planner and cloud-computing.

SpeakerRabbi, Fazle

Presentation time

14:30:00

14:50:00

Session time

13:30:00

15:00:00

SessionCollection System Predictive Analysis

Session locationRoom S402b - Level 4

TopicAsset Management, Collection Systems, Intermediate Level, Utility Management and Leadership

Author(s)

Rabbi, Fazle

Author(s)F. Rabbi 1; J. Alam 2 ; F. Rabbi 1;

Author affiliation(s)City of Houston 1; IMS Engineers, 5821 Southwest Fwy #130, Houston, TX 77057 2 ; City of Houston 1;

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Oct 2023

DOI10.2175/193864718825159212

Volume / Issue

Content sourceWEFTEC

Word count13

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

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