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WEFTEC, the largest event of its kind in North America, offers water quality professionals the best in water quality education and training. With almost 200 technical sessions, workshops, mobile sessions, local facility tours and 1,000+ exhibitors, it is the premier water conference! The WEFTEC technical program is selected through a rigorous, peer-review process, ensuring that attendees experience the highest-quality education. WEFTEC speakers are experts and innovators, leading the way in water quality. The following paper was presented at WEFTEC 2019.

The prime objective of this work was to identify the major sources of internal heat generation and locations within the water recovery facility that provide the most potential for heat dispersion to the atmosphere. A facility-wide thermal energy model applied to each unit process was developed, calibrated and validated at two water recovery facilities. The model was verified by applying it to three separate months representing cold, intermediate and warm influent temperatures at the two facilities. Differences between model predicted and facility recorded effluent temperatures were within 0.06% to 2.3%, with an average of 1.13%. With these results, the thermal model was deemed to be calibrated. Subsequent detailed temperature monitoring at each unit process within the plants confirmed the validity of the model predicted results. The overall net heat gain, without hot water discharges to the effluent from cogeneration cooling water, ranges from 2.63×106 Joule/m3 influent flow at cold (15.7°C) influent temperatures to 3.73×106 Joule/m3 at warm (21.6°C) influent temperatures.The model showed that in critical colder months, covers over unit processes are largely ineffective in cooling the effluent. With heat recovery in the influent interceptor system, temperature impacts on biological processes were verified using the model.

The City of Carson, California sought an efficient way to conduct storm water inspections at permitted businesses within City limits on a compressed schedule as part of their MS4 permit requirements. Partnering with GHD, the City was able to gain efficiencies in the following areas: Progress reporting QA/QC of field data during collection Linking field photos with inspection forms Digital data entry of previous hand-written field forms Scheduling follow-up inspections Tabular data upload to regulatory database The efficiencies translate into faster progress of over 1100 inspections, transparency on progress and data quality for all team members, and easier upload to compliance databases. Coupling these tools with GHD's comprehensive training on what to ask and look for in the field during inspections and what might indicate an illicit discharge or connection provided a comprehensive package and robust support team for the City of Carson. Purpose: We will be presenting the successful workflow using Esri's ArcGIS Online and mobile apps to show how the City accelerated a team of new storm water inspectors and new adopters of Esri mobile apps into an efficient and seamless program of storm water compliance. Custom scripting within pop-ups allowed inspectors to benefit from the map aspect of Collector and ease of data entry with Survey123. Dashboards and web apps in the Online environment allowed for concurrent review of field data in real time, and at-a-glance progress for program managers. Benefits of Presentation: This project is an excellent example of a ground-up program roll-out of first-time inspectors using Esri's ArcGIS Online and mobile tools. It demonstrates how quickly and easily storm water inspections and reporting can be prepared, packaged and delivered in a way to get a team started and inspections completed with maximum transparency. Status of Completion/Conclusions: At the time of submittal, the training, data preparation, and tool development described above is complete. Inspections have started and are scheduled to be completed and data uploaded to the regulatory database in the first quarter of 2022. We expect the project will be entirely complete, with time for reflection, before the June conference date. Originality: Esri offers many online tools that can be designed to fit the need of any project. We took this one step further to apply custom scripting within the mobile apps to leverage the advantages of both Collector and Survey123. The way we have combined the building blocks provided by Esri created a custom fit to this project and would fit any organization who is starting from square-one with their storm water inspections required through their NPDES permit, or who is looking for a more streamlined and repeatable approach to their inspection data management.

Today we have the benefit of having digital data available at our fingertips. However, in the stormwater profession, are we ready to maximize the use of that data? Each project presents opportunities to maximize the application of this data and how we can best collect stormwater data today to further the digital world we live in. When we collect stormwater data are we thinking about how that data can be used in models to address watershed solutions? Are we thinking about how our modeling results can be used to engage stakeholders? We have the technical resources to keep up with the rapidly changing digital world; the challenge is applying those technical resource efficiently to maximize the use of the data. Ultimately, it is using data to create more sustainable and resilient solutions that reduce costs and increases public benefit while managing the risk associated with a changing climate. Our toolbox includes stormwater models. Stormwater models are used every day to simulate design and historical rainfall events. We use them to demonstrate the impacts and the benefit those stormwater solutions given our assumed inputs. We use models to map floodplains, to evaluate enclosed systems, and to predict future flooding based on land cover changes or increases in rainfall intensities. The potential uses are endless, but do we get the full benefit of the resources put into developing these models over time? Are we building an adaptable and transforming digital platform or are we just developing a list of static projects? For stormwater solutions to advance and be more adaptive, resilient, and sustainable we need to have a plan and a vision of how stormwater data should be collected, how that data is maintained, what that data is going to be used for, and, who is using the data. We can't keep doing stormwater modeling in a black box every 15-20 years. Stormwater data and the subsequent models need to be living and growing digital systems that are refined and updated regularly with an eye towards smarter stormwater solutions that provide greater long term public benefit. This presentation will address these issues using the City of Lawrence, Kansas as a case study. The City of Lawrence 20-years ago went through a typical stormwater master planning process that resulted in a list of projects. Many of those projects have been completed but stormwater issues remain and, in some cases, are worse. Instead of going through another typical stormwater master planning process, the City is fully understanding their stormwater system through comprehensive field evaluation of their assets; developing the processes and procedures for using digital data in comprehensive watershed models; using the results of the watershed models to define improvement projects and engage stakeholders; and to create a holistic, dynamic stormwater improvement plan. The field data was collected using the latest technology, organized within a stormwater asset management system, and delivered as geodatabases, shapefiles, and digital photos and videos to the modeling team. The modeling team then used that data with recent LiDAR to develop stormwater models of the enclosed system, the 2D overland flow, and the 2D open channels and streams. The stormwater models include PC-SWMM with 2D and HEC-RAS 6.0 2D. The model files were spatially referenced and fully integrated with the City's GIS. The model inputs and outputs were easily mapped and overlaid with other City GIS data. This resulted in digital stormwater data and model simulation results that formed the foundation for the development of an adaptive and dynamic stormwater improvement process. This process will allow the City to use the models to make decisions, update stormwater improvement plans periodically, and provide watershed visualization materials to educate and engage stakeholders on stormwater issues. This presentation will focus on stormwater modeling in the digital world and how the latest field data collection technology, asset management systems, and stormwater improvement projects can be done more effectively. Lessons learned in using digital data for model inputs, 2D modeling, and linking enclosed storm sewer models with open channel models will also be presented. And finally, a process will be presented of how this can be accomplished on a watershed scale to drive innovation, improve the resiliency of stormwater solutions, and reduce a city's overall stormwater improvement cost.

Municipalities often face difficult choices about how to allocate resources, where to build infrastructure, and with whom to partner to address complex stormwater management challenges. The City of Aiken in South Carolina is no exception as city leaders seek to restore predevelopment hydrology in the Sand River. The Sand River collects runoff from 1,109 acres of urban watershed in the City of Aiken and flows primarily through Hitchcock Woods, the largest privately-owned urban forest in the country. Hitchcock Woods and the Sand River are a haven for hikers and equestrians, and a driver of tourism in the City. What was once small enough for a person to step across, the Sand River has become a 70-ft deep canyon as a result of frequent and intense stormwater flows coming from downtown Aiken over the past 40 years [1,2]. In 2017, the newly-formed Sand River Stormwater Task Force recommended 20 capital improvement projects, sized to capture 58 ac-ft of stormwater, and costing approximately $22 million (2017 dollars) as a way to restore the river's predevelopment hydrology [3]. While this plan was based on the best available information at the time, recent developments in real-time control technology led the Task Force to reevaluate the recommended project list. In 2020, the Task Force recommended the use of forecast-based real-time control technology, also called continuous monitoring and adaptive control (CMAC), on the largest proposed stormwater project: a 24 ac-ft set of underground vaults (Figure 1). These vaults are positioned at the downstream end of Aiken's Sand River urban watershed, just upstream major erosion in Hitchcock Woods (Figure 2). CMAC was estimated to increase the effectiveness of the vaults by 2x to 3x by retaining water during and after storms, increasing infiltration, and only discharging in preparation for forecast storm events exceeding the capacity of the vaults [4]. To further quantify how the adaptively-controlled Sand River vaults perform compared to traditional passive underground storage, a modeling effort was undertaken for the period of calendar years 2001 through 2006, during which time Aiken received 295 inches of rainfall (average of 49 inches per year). The CMAC system was found to retain 71% of stormwater flow entering the site during wet weather periods, compared to only 26% wet weather capture achieved by passive storage. Modeling also showed the adaptive control vaults to exfiltrate 66% of all stormwater volume entering the system compared to 25% exfiltration by passive storage. This increase in performance will set a new baseline for stormwater flow in the Sand River, thereby mitigating the need for upstream stormwater projects and saving the City of Aiken millions of dollars in capital expenditures. This presentation will examine the decision to include CMAC on this project, discuss the design process, and present modeling results showing the performance improvement of the vaults with adaptive controls. Future work will include comparing the modeled performance to observed performance after the system is commissioned in 2022.

Variations in Green Infrastructure (GI) Low Impact Development (LID) Best Management Practices (BMPs) within the right-of-way have resulted in problems with construction, maintenance, and durability. GI implementation guidance documents, standard details and specifications provide uniformity in planning, design, construction, and maintenance. By standardizing how LID is placed in the Right-of-Way agencies can reclaim valuable space for people to enjoy, turn a burden into a resource, reduce irrigation demand and stormwater runoff, and provide for a sustainable and resilient transportation corridor. When green stormwater infrastructure is integrated with complete street concepts numerous co-benefits strengthen the three pillars of sustainability; the environment, social equity, and the economy. San Diego County is one agency that implemented GI guidelines and standards. Green Infrastructure Guidelines are an outreach tool and are also intended to serve as a companion to the rest of the Green Infrastructure publications. They include an introduction, strategies, procedures and design examples for implementing Green Infrastructure projects. The strategies include tree wells, rain gardens, rock gardens, and permeable pavement. Green Infrastructure Design Criteria lay out general definitions, general policy, and landscape design criteria for Green Infrastructure strategies. Green Infrastructure Design Standard Drawings includes construction details which may be referenced in improvement plans. Green Streets Maintenance Schedules include a list with maintenance tasks, frequency, and time of year for initial, routine, and as-needed maintenance of each strategy. Green Streets Specifications include detailed construction material and installation requirements for strategies including aggregates, geosynthetics, underdrains, permeable pavement, engineered soil media, mulch, overflow risers, check dams, and tree grates. Building on the GI guidelines and standards developed within the Southern California region, these concepts were integrated into a Climate Resilient Infrastructure Guidebook for Riverside and San Bernardino Counties in southern California including climate resiliency strategies related to stormwater. The Guidebook complements city-level, climate-related transportation hazards and evacuation maps and other products developed as part of a larger resilient infrastructure initiative. The Guidebook serves as a guide to help educate local practitioners in making the transportation system more resilient to these changes. It is intended as a practical resource for jurisdictions and includes strategies, best practices, and methods for overcoming challenges and using climate resiliency tools to address changing patterns of extreme temperatures, heavy precipitation and flooding events, and drought. It was tailored to address the diverse climate, terrain, and needs of the region to ensure the long-term viability of the transportation system. Stakeholder groups and agency staff are instrumental in identifying implementation priorities and correcting procedural barriers to implementation. There is typically limited surface and underground area available within the right-of-way to accommodate often competing needs for travel lanes, parking, bike lanes, pedestrian sidewalks/paths, and utilities. In constrained right-of-way areas, this often leads to limited availability for implementation of green/climate resilient infrastructure strategies. Another challenge to implementing strategies within the public right-of-way includes often conflicting jurisdiction regulations, codes, ordinances, and standards from various levels (local through Federal) due to differing priorities of various project elements. Standardizing the implementation, design, construction, and maintenance of green stormwater infrastructure provide multiple benefits for the public and stakeholders. Integration of green stormwater infrastructure elements into transportation planning, resiliency studies and guidelines provides effective tools for planners and engineers to address changing patterns of extreme temperatures, heavy precipitation and flooding events, and drought.

The English Coulee meanders through Grand Forks, North Dakota and has many important uses to the City not only drainage and interior flood protection storage but also pedestrian walking / biking corridor and active recreation opportunities such as ice skating and paddling. Given that the English Coulee serves a watershed over 50 square miles that is dominated by agricultural land use, the coulee has been plagued by issues related to E coli and phosphorous. Further, as Grand Forks continues to grow, urban runoff quality is also providing significant loading. Finally, a significant hydrologic / hydraulic alteration to the English Coulee was made about 20 years following the catastrophic 1997 Red River flood that destroyed much of the City - the English Coulee Diversion was constructed as part of the interior flood protection system that aids in protecting the City from Red River floods. This diversion cuts off much of the agricultural watershed but also reduces the potential for high springtime flows to flush sediment and nutrients that have accumulated in the coulee within the City. The stagnant water conditions throughout much of the summer combined with the likely high nutrient concentrations create severe aesthetic and odor issues related to algal blooms and anoxic conditions. While the coulee is listed as an impaired 303(d) waterway, the City decided to begin their own multi-phase initiative to renew the coulee given its benefit to the community as a recreational resource. The first phase of the project focused on answering the question 'what could be done?'. To answer this question, the first phase involved three key components: 1) public outreach and engagement, 2) water quality monitoring and modeling, and 3) hydrologic / hydraulic monitoring and modeling. A unique approach to public engagement was used with signs along the coulee with QR codes that pedestrians could scan and bring the online survey up on their phones. The public engagement survey received over 500 responses with an overwhelming response that the community wanted to see the Coulee's water quality improved. Water quality monitoring was also completed for numerous parameters that confirmed the community's observations nutrients (namely phosphorous), suspended sediment, and E coli were all very high throughout the year. Water level data was also collected at several locations that indicated that FEMA's hydrologic and hydraulic analysis did not reflect the complexity of the coulee hydraulics, illustrating the need to create a more robust hydrologic / hydraulic model of the coulee system to evaluate potential watershed and channel / dam modifications. The second, ongoing phase of the English Coulee Renewal Plan aims to answer the question 'What should be done?' with subsequent phases aimed at implementing projects to improve water quality in the coulee.

The NYC Department of Environmental Protection (NYCDEP) developed a Catskill/Delaware watershed protection program that met the requirements for unfiltered water supply systems as determined by the EPA and NYS Department of Health. Under the resulting 2007 Filtration Avoidance Determination (FAD) program DEP installed Stormwater BMPs within the Kensico Reservoir to reduce pollutant loading and improve water quality. Implementing BMPs impacted existing wetland buffers, open water, and submerged aquatic vegetation habitats. A comprehensive review of mitigation opportunities was completed and the former Armonk Bowling Alley in Armonk, NY was selected for the creation of off-site mitigation. The Bowling Alley was privately owned, but it provided an opportunity for permit compliance that supported larger goals of water quality for the Reservoir, including retention of the first flush and reduction of sediment loads. The site doubled the adjoining acreage of two previously completed wetland enhancement projects and will cumulatively improve wetland functions by creating a contiguous headwater complex that filters and treats water prior to entering the reservoir. The Bowling Alley, constructed in the 1960s, ditched a tributary of the reservoir - Bear Gutter Creek. The mitigation design restored the Creek, meandering it through created wetland and forested floodplain. The site is within a system prone to flash flooding and was designed to provide flood mitigation and storage during rain events. Retention of water buffers the downstream stream corridor from erosion, delays the peak flow, and allows suspended sediments to drop outside of the reservoir. Permitting of the 3.13-acre mitigation was challenging as aquatic submerged vegetation impacts were compensated for by wetland and open water creation. A functional assessment validated that the mitigation design compensated for the impacted habitat functions. Modeling confirmed the new mitigation would not alter the hydrology of the adjacent wetland area and no adverse upstream or downstream flooding would occur. Lessons from implementation of previous mitigations were incorporated into the design to improve site success, while construction presented its own lessons on soil management, installation of instream structures, post-construction monitoring and associated adaptive management. The presentation will provide attendees with knowledge of NYCDEP's Filtration Avoidance Determination and how watershed management results in wins for not only water quality, but also preservation of open space, creation of highly functioning habitat complexes, and flood risk reduction. We will discuss many of the lessons learned during the design, permitting, and construction phases; including how regulatory hurdles translated in to design decisions.

The Allegheny County Sanitary Authority (ALCOSAN) serves an area that spans approximately 310 square miles across 83 municipalities, including the City of Pittsburgh, Pennsylvania. ALCOSAN recently adopted a Clean Water Plan (CWP) to improve and protect the water quality of the region's streams and rivers by controlling combined sewer overflows (CSOs) and eliminating sanitary sewer overflows (SSOs). The CWP is part of a Federal Consent Decree (CD) to comply with the United States Environmental Protection Agency's CSO Control Policy. The CWP is made up of several components including preventing excess water from entering the sewer systems. As part of its green stormwater infrastructure (GSI) and source control (SC) program, ALCOSAN released Controlling the Source that lays out a consistent framework for evaluating GSI/SC in the Pittsburgh region. A key component involves identifying cost€effective and impactful GSI/SC projects capable of reducing sewer overflows to improve water quality. Overflow reduction efficiencies (OREs) were developed throughout the service area using automated modeling techniques to represent different GSI/SC techniques, varying implementation levels, and different system conditions. OREs provide a hydraulically-informed estimate of the relative overflow impacts of projects in different areas, so that effort and attention can be focused in those areas with the greatest potential overflow benefits. To complement the OREs, a GIS-based constraints analysis was performed to provide a geospatially-informed estimate of areas where GSI potential may be limited and/or costlier based on mapped physical and environmental constraints. Individual constraints such as shallow bedrock and slopes were assigned a range of constraint scores based the relative impact to GSI and then overlaid and summed. By targeting areas/sites with high OREs and lower levels of constraints, practitioners have powerful planning-level tools to identify projects most likely to be feasible, cost-effective, and impactful. Using these tools, ALCOSAN identified and evaluated almost 200 potential GSI opportunities. The opportunities were ranked and prioritized based on capture potential and implementation feasibility. Detailed concept plans for 60 of the higher ranked sites were developed and incorporated in discussions with municipalities as a tool to incentivize GSI project implementation in response to public comments received during the development of the CWP. This presentation will describe the development and application of the ORE modeling and the constraint scores as well as the results of the opportunity analysis and will discuss lessons learned for applicability in helping to address wet weather issues in other communities.

Introduction: An open channel is a concrete waterway that is widely used in many regions. Like other concrete structures, open channels suffer from many deficiencies, such as scour, cracking, spalling, and tilting. Inspections need to be performed on a regular basis aimed at identifying and rating deficiencies. Ratings represent the severities of a deficiency, which are often associated with descriptions and suggested actions. Table 1 shows an example of spall rating classification. Manually classifying deficiency ratings is often difficult, since it requires both expertise and extensive physical inspection. Therefore, automating deficiency rating work has been an industry challenge. Machine learning (ML) is a type of artificial intelligence (AI) that can learn from various data, identify patterns, and make decisions. In this study, we are interested in evaluating the power of ML on providing accurate rating classifications of channel deficiencies without human intervention. The developed ML model is described in Methodology and followed by preliminary Model Results and Conclusion. Methodology: Since the goal is to rate defects or deficiencies, a supervised ML model is selected. Specially, we train a convolutional neural network (CNN) on 80% of the total available data, then validate & test model results on the remaining 20%. For each deficiency, the total available data consists of thousands of pre-labeled deficiency photos. Table 2 lists the total available photos for each test deficiency. Figure 1 provides a proposed modelling flowchart. Convolutional neural network (CNN) is one of the most popular neural networks in computer vision. Compared with traditional neural networks, CNN consists of not only fully connected layers, but also convolutional layers and pooling layers. This structure allows CNN to utilize spatial pixel interaction information as well as reduce model complexity, which makes CNN especially suitable for image classification. The convolutional layer is used for parsing the input photo. Three brightness intensity values (red, green, and blue, or 'RGB' channels) are assigned to each pixel on a color photo. Figure 2 gives an illustrative example using a tree photo. After parsing a photo into its RGB channels, the convolutional layer employs several 'filters' that contain trainable weights to apply convolution operations on these channels. These filters move over the input layer until all pixels have been covered. The dimensions of input layer will be reduced after the convolution. A pooling layer typically follows a convolutional layer, which further decreases the input layer dimension. The pooling layer also summarizes the regional pixels information so that the model becomes robust against objective position variations. Figure 3 demonstrates a CNN example that is used to predict if a photo is a tree, streetlamp, or a stop sign. In this study, we further adopt a 'transfer learning' technique: A pre-trained CNN, ResNet-50, is used and followed by a trainable fully connected layer to yield deficiency rating predictions. Our model is developed based on open-source software libraries: TensorFlow and Keras frameworks. Model Results: Results are briefly discussed in this abstract. Figure 4 show both the loss and accuracy curves against 70 epochs for cracking as an example, with class weight considered in training. As the training proceeds, in general, the validation loss is decreasing and the validation accuracy is increasing before reaching a relative stable condition, which indicates the convergence of the model. Figure 5 plots the confusion matrix of cracking based on the cracking test data. There are three ratings for cracking (R1, R2, and R3). Each row represents the model prediction while each column is the true label. For example, there are 333 photos with the true R2 labels that are also predicted as R2. Based on the confusion matrix, the model accuracy, precision, and recall are calculated. In this study, we select accuracy and macro-averaged F1 score as two evaluation metrics. Accuracy is defined as the number of correct predictions divided by the total number of predictions, and it assumes that all the classes are equally important. Macro-averaged F1 score is a harmonic mean (tradeoff) between precision and recall, which is useful when the false negatives and false positives are essential. For some deficiencies, rating classes can be imbalanced, where one rating class only has several photos while another rating class has thousands of photos. In this case, F1 score is a preferred over accuracy since it accounts for the model ability to predict the minority class. One way to increase F1 score is to incorporate class weight into loss function, where class weight is the inverse data ratio among different rating classes. Table 3 shows the class weight information for each deficiency. Based on the class weights, Table 4 shows the test results for all four deficiencies, where 60%-70% accuracy can be generally achieved. The accuracy is generally lowered by 2% in exchange for 0.02 increase of F1 score, when comparing the no-class-weight model to the class-weight model. Due to the limited data (Table 2), tilting has the least accuracy performance. Also, because of the relatively balanced classes (Table 3), F1 score does not increase for tilting when transiting from the no-class-weight to the class-weight model. Conclusion: This abstract presents the method used to develop a machine learning model based on CNN, with the objective of assigning open-channel deficiency ratings. Four deficiencies were selected (cracking, spalling, tilting, and vegetation), and the preliminary results indicate a general 60%-70% accuracy and 0.4-0.5 F1 score. In general, the achieved accuracy performance is positively related to the number of training data. Adding class weights into the model can increase F1 score, if the deficiency rating classes are imbalanced. More discussions will be provided in the following paper.