Flood Judge: Difference between revisions
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{{ActionCluster | {{ActionCluster | ||
|image=FloodJudgeS.png | |||
| | |team=Civic Foundary | ||
|leader=Vijay Sammeta | |||
| team | |imagecaption=Ross Valley Flood | ||
|municipalities=Ross Valley CA | |||
|status=Ready for Public Announcement | |||
| leader | |description=Combining low cost / commodity sensors, enterprise IT skillsets, existing / familiar tools with the power of the cloud and artificial intelligence to proactively monitor and alert water levels in creeks during storm activity. | ||
|challenges=Communities around the world suffer from sudden flooding, sometimes with little to no warning. Our solution would use historical data to model potential water level rise at a hyperlocal level and predict associated flooding dates and time. The sensor was inspired by our own community tragedy. In Feb 2017 14,000 residents in the City of San Jose were displaced by a flood for which there was no warning. In the days, weeks and months that followed the lack of real-time data, data in the appropriate format (height as opposed to cubic feet per second) and a notification system all contributed to nearly 100 million dollars in damage to the community we live and work in. | |||
|solutions=TBD | |||
| imagecaption | |requirements=* Build the hardware using commodity / off-the-shelf components | ||
| municipalities | |||
| status | |||
| description | |||
Combining low cost / commodity sensors, enterprise IT skillsets, existing / familiar tools with the power of the cloud and artificial intelligence to proactively monitor and alert water levels in creeks during storm activity. | |||
| challenges | |||
Communities around the world suffer from sudden flooding, sometimes with little to no warning. Our solution would use historical data to model potential water level rise at a hyperlocal level and predict associated flooding dates and time. The sensor was inspired by our own community tragedy. In Feb 2017 14,000 residents in the City of San Jose were displaced by a flood for which there was no warning. In the days, weeks and months that followed the lack of real-time data, data in the appropriate format (height as opposed to cubic feet per second) and a notification system all contributed to nearly 100 million dollars in damage to the community we live and work in. | |||
| solutions | |||
| requirements | |||
* Build the hardware using commodity / off-the-shelf components | |||
* Write the application to read water levels every 5-15 mins and send the data to the cloud | * Write the application to read water levels every 5-15 mins and send the data to the cloud | ||
* Apply streaming analytics to determine if the current reading is at flood stage | * Apply streaming analytics to determine if the current reading is at flood stage | ||
* Apple machine learning to the predicted rainfall, current water levels and historical modeling for the current location of sensor deployment | * Apple machine learning to the predicted rainfall, current water levels and historical modeling for the current location of sensor deployment | ||
* Build a dashboard for administrators, a subscription page for notifications and real-time events to push mass notifications | * Build a dashboard for administrators, a subscription page for notifications and real-time events to push mass notifications | ||
|kpi=Provide automated notifications up to an hour prior to creek flooding (obviously we will try to model as much notice as possible). Due to the hyper local nature of each sensor the pilot will begin this fall to begin collecting data surrounding creek level and projected rainfall between every 5-15 mins. We will be working with the Ross Valley Fire Department determine alerting levels and finalize the KPI’s over the next two months | |||
| kpi | |measurement=The dashboard will include projected rainfall, associated hyperlocal creek rise and historical data. Using this combination we will be able to validate if the | ||
Provide automated notifications up to an hour prior to creek flooding (obviously we will try to model as much notice as possible). Due to the hyper local nature of each sensor the pilot will begin this fall to begin collecting data surrounding creek level and projected rainfall between every 5-15 mins. We will be working with the Ross Valley Fire Department determine alerting levels and finalize the KPI’s over the next two months | |||
| measurement | |||
The dashboard will include projected rainfall, associated hyperlocal creek rise and historical data. Using this combination we will be able to validate if the | |||
model is working properly as well as measure the accuracy the rainfall projections. The KPI’s will be a part of the dashboard and available to key stakeholders. | model is working properly as well as measure the accuracy the rainfall projections. The KPI’s will be a part of the dashboard and available to key stakeholders. | ||
|standards=* The current solution uses Wifi and is completely based around enterprise technology such as off-the-shelf hardware, web services and the data center. It can even be viewed in a simple spreadsheet. | |||
| standards | |||
* The current solution uses Wifi and is completely based around enterprise technology such as off-the-shelf hardware, web services and the data center. It can even be viewed in a simple spreadsheet. | |||
* The entire goal of the project is to highlight Replicability, Scalability, and Sustainability by extending municipal IT skill sets incrementally as opposed | * The entire goal of the project is to highlight Replicability, Scalability, and Sustainability by extending municipal IT skill sets incrementally as opposed | ||
to introducing new ones that often times are ultimately unsustainable due to costs, attraction & retention of talent or skillsets. Our goal is to move smart cities from proof-of-concept to as common as email in cities by lowering barriers to entry and sustainability. | to introducing new ones that often times are ultimately unsustainable due to costs, attraction & retention of talent or skillsets. Our goal is to move smart cities from proof-of-concept to as common as email in cities by lowering barriers to entry and sustainability. | ||
|cybersecurity=TBD | |||
|impacts=This is a life, health, safety and property protection project. | |||
| cybersecurity | |demonstration=We will have our prototype sensors, dashboard, community notification page and alerting solution available for demo. We are prepared to demonstrate various water | ||
levels including flood stage and alerting by pour water into a cylinder. | |||
| impacts | |chapter=Digital Twins | ||
This is a life, health, safety and property protection project. | |supercluster=Wireless | ||
|year=2017 | |||
|title=Flood Judge | |||
| demonstration | |email=vijay@civicfoundry.org | ||
We will have our prototype sensors, dashboard, community notification page and alerting solution available for demo. We are prepared to demonstrate various water | |||
levels including flood stage and alerting by pour water into a cylinder. | |||
| supercluster | |||
| year | |||
}} | }} |
Latest revision as of 23:09, January 24, 2023
Flood Judge | |
---|---|
Ross Valley Flood | |
Team Organizations | Civic Foundary |
Team Leaders | Vijay Sammeta |
Participating Municipalities | Ross Valley CA |
Status | Ready for Public Announcement |
Document | None |
Description
Combining low cost / commodity sensors, enterprise IT skillsets, existing / familiar tools with the power of the cloud and artificial intelligence to proactively monitor and alert water levels in creeks during storm activity.
Challenges
Communities around the world suffer from sudden flooding, sometimes with little to no warning. Our solution would use historical data to model potential water level rise at a hyperlocal level and predict associated flooding dates and time. The sensor was inspired by our own community tragedy. In Feb 2017 14,000 residents in the City of San Jose were displaced by a flood for which there was no warning. In the days, weeks and months that followed the lack of real-time data, data in the appropriate format (height as opposed to cubic feet per second) and a notification system all contributed to nearly 100 million dollars in damage to the community we live and work in.
Solutions
TBD
Major Requirements
- Build the hardware using commodity / off-the-shelf components
- Write the application to read water levels every 5-15 mins and send the data to the cloud
- Apply streaming analytics to determine if the current reading is at flood stage
- Apple machine learning to the predicted rainfall, current water levels and historical modeling for the current location of sensor deployment
- Build a dashboard for administrators, a subscription page for notifications and real-time events to push mass notifications
Performance Targets
Key Performance Indicators (KPIs) | Measurement Methods |
---|---|
Provide automated notifications up to an hour prior to creek flooding (obviously we will try to model as much notice as possible). Due to the hyper local nature of each sensor the pilot will begin this fall to begin collecting data surrounding creek level and projected rainfall between every 5-15 mins. We will be working with the Ross Valley Fire Department determine alerting levels and finalize the KPI’s over the next two months |
The dashboard will include projected rainfall, associated hyperlocal creek rise and historical data. Using this combination we will be able to validate if the model is working properly as well as measure the accuracy the rainfall projections. The KPI’s will be a part of the dashboard and available to key stakeholders. |
Standards, Replicability, Scalability, and Sustainability
- The current solution uses Wifi and is completely based around enterprise technology such as off-the-shelf hardware, web services and the data center. It can even be viewed in a simple spreadsheet.
- The entire goal of the project is to highlight Replicability, Scalability, and Sustainability by extending municipal IT skill sets incrementally as opposed
to introducing new ones that often times are ultimately unsustainable due to costs, attraction & retention of talent or skillsets. Our goal is to move smart cities from proof-of-concept to as common as email in cities by lowering barriers to entry and sustainability.
Cybersecurity and Privacy
TBD
Impacts
This is a life, health, safety and property protection project.
Demonstration/Deployment
We will have our prototype sensors, dashboard, community notification page and alerting solution available for demo. We are prepared to demonstrate various water levels including flood stage and alerting by pour water into a cylinder.