SAILFLOW (Smart Automated and AI-powered Last-minute FLOod Forecasting and Warning System)

Background

Global warming is fueling a terrifying trend: more frequent and intense torrential rain, leading to a surge in flash floods. These sudden, violent floods devastate both rural and urban areas, including flood-free zones. Flash floods are the most common flood type globally, causing immense damage to infrastructure and property, and most importantly, the loss of life.

While developed nations can utilize advanced technologies like radar networks, high-resolution satellites, and supercomputers for accurate flood forecasting, this isn't the reality for developing countries and areas with fewer resources. These technologies are prohibitively expensive to establish and maintain due to infrastructure and energy costs.

Case study on Zimbabwe  

Zimbabwe currently has a few of those technologies due to the current economic situation. Zimbabwe currently has only three small, low-coverage radars, and uses a few international satellites to do its weather forecasting. This makes advanced weather forecasting difficult, and since weather forecasting plays a critical role in flood forecasting or monitoring, most Zimbabwe flood events are not forecasted, unless it is a massive inter-regional cyclone. Due to these infrastructural issues, there is little reliable data that is produced to make meaningful flood monitoring or forecasting models.  To worsen the problem The distribution of operational small weather stations is 30 provinces to 64 districts as of 2022 when we asked an expert, mostly located in urban and peri-urban areas. Now, this also leaves a gap between rural and urban dwellers as most basic weather forecasts are only relevant to urban communities. While this is the case in Zimbabwe, most developing nations such as Tanzania, Mozambique, Vietnam, etc 

The Bottom Line: Rural and Remote Communities at Risk of Chaotic Floods

The rising frequency of torrential rains due to global warming puts many communities at significant risk of flash flooding, impacting both ravines and previously flood-free zones. In essence, the core problems are:

• The high cost of modern weather systems for developing countries resulting in difficulty in weather forecasting, makes it challenging to predict local disasters like floods, whose frequency is increasing due to climate change.

• The inadequate distribution of existing weather systems, concentrated in major urban areas, hinders even basic weather forecasting for rural and remote areas, particularly for "last-mile communities", where most of the population is located.

This brings the question: How do you create a low-cost, solution that can be used to both make accurate flash flood forecasting/prediction and carry out alert and warning  protocol to the targeted community? 

Type1 Ai solution is SAILFLOW: A novel, low-cost, localized smart Automated, and AI-powered Last minute flood forecasting and Warning system. It’s a Stephenson screen-like device that is equipped with various weather sensors and microcontrollers, to create a portable automated weather station powered by solar energy, making it a high-tech sustainable low-cost device. SAILFlOW is based on two systems, the Flood Forecasting/prediction Systems- which was the problem of predicting flash floods, and the Flood Alert SysTem (FAST), which alerts the community of any potential threats. 

Flood Prediction system (FLOPS): we came up with a novel way of forecasting floods, which is based on using AI models to forecast precipitation hourly( precipitation Nowcasting) using real-time weather data and pictures of clouds, then use analytical techniques to forecast the water levels based on model the drainage or infiltration of an area. These models then work to predict different types of flooding depending on the Area.

Precipitation nowcasting: We developed a grounded precipitation nowcasting model that is used to run off-edge devices such as a Nvidia Jetson Nano or Raspberry Pi for the current prototype. The model uses data from sensors and pictures of clouds taken by a webcam(attached on the device) to make hourly forecasts from 0 to 10 hours in time. 

Analytical water level monitor: The device records data on current precipitation through a collection, there is a tap that simulates the drainage or infiltration, using that data as well as the precipitation data to simulate the water level of the area in the future in the ten-hour time frame.

Flood Alert System (FAST): 

It's a combination of both low-tech and high-tech. It consists of a civil alert system made up of lights and sirens that are triggered by the FLOPS.

 It also uses IOT, and communication technology that will send SMS to most of the community members hours before a flood event occurs. Depending on the level of the threat( low, medius, high risk, existential)  SMS are sent to community leaders, households, and nearest rescue centers depending on the threat, as well as crucial precautions. These Alerts are given in all three languages commonly used in Zimbabwe. For the current prototype, the Light and sirens are on the device, but they can be placed throughout the village and activated via radio, such as already existing systems. 

The data collected and generated is not only vital for immediate flood warnings but also beneficial for agricultural forecasting. It can be stored locally on the device or uploaded to the cloud, where it can be accessed through smart mobile apps by farmers for enhanced agricultural planning. This system also holds potential for further research and data collection in flood-prone areas, which are often under-documented. As the system develops, it could also be integrated into the infrastructure of developing smart cities, connecting with existing weather stations and drainage systems to enhance urban flood management.

SAILFLOW will save lives against natural disasters, support agricultural activities, and contribute to vital environmental research.

Type1 Ai solution is SAILFLOW: A novel, low-cost, localized smart Automated, and AI-powered Last minute flood forecasting and Warning system. It’s a Stephenson screen-like device that is equipped with various weather sensors and microcontrollers, to create a portable automated weather station powered by solar energy, making it a high-tech sustainable low-cost device. SAILFlOW is based on two systems, the Flood Forecasting/prediction Systems- which was the problem of predicting flash floods, and the Flood Alert SysTem (FAST), which alerts the community of any potential threats. 

Flood Prediction system (FLOPS): we came up with a novel way of forecasting floods, which is based on using AI models to forecast precipitation hourly( precipitation Nowcasting) using real-time weather data and pictures of clouds, then use analytical techniques to forecast the water levels based on model the drainage or infiltration of an area. These models then work to predict different types of flooding depending on the Area.

Precipitation nowcasting: We developed a grounded precipitation nowcasting model that is used to run off-edge devices such as a Nvidia Jetson Nano or Raspberry Pi for the current prototype. The model uses data from sensors and pictures of clouds taken by a webcam(attached on the device) to make hourly forecasts from 0 to 10 hours in time. 

Analytical water level monitor: The device records data on current precipitation through a collection, there is a tap that simulates the drainage or infiltration, using that data as well as the precipitation data to simulate the water level of the area in the future in the ten-hour time frame.

Flood Alert System (FAST): 

It's a combination of both low-tech and high-tech. It consists of a civil alert system made up of lights and sirens that are triggered by the FLOPS.

 It also uses IOT, and communication technology that will send SMS to most of the community members hours before a flood event occurs. Depending on the level of the threat( low, medius, high risk, existential)  SMS are sent to community leaders, households, and nearest rescue centers depending on the threat, as well as crucial precautions. These Alerts are given in all three languages commonly used in Zimbabwe. For the current prototype, the Light and sirens are on the device, but they can be placed throughout the village and activated via radio, such as already existing systems. 

The data collected and generated is not only vital for immediate flood warnings but also beneficial for agricultural forecasting. It can be stored locally on the device or uploaded to the cloud, where it can be accessed through smart mobile apps by farmers for enhanced agricultural planning. This system also holds potential for further research and data collection in flood-prone areas, which are often under-documented. As the system develops, it could also be integrated into the infrastructure of developing smart cities, connecting with existing weather stations and drainage systems to enhance urban flood management.

SAILFLOW will save lives against natural disasters, support agricultural activities, and contribute to vital environmental research.

Type1 Ai solution is SAILFLOW: A novel, low-cost, localized smart Automated, and AI-powered Last minute flood forecasting and Warning system. It’s a Stephenson screen-like device that is equipped with various weather sensors and microcontrollers, to create a portable automated weather station powered by solar energy, making it a high-tech sustainable low-cost device. SAILFlOW is based on two systems, the Flood Forecasting/prediction Systems- which was the problem of predicting flash floods, and the Flood Alert SysTem (FAST), which alerts the community of any potential threats. 

Flood Prediction system (FLOPS): we came up with a novel way of forecasting floods, which is based on using AI models to forecast precipitation hourly( precipitation Nowcasting) using real-time weather data and pictures of clouds, then use analytical techniques to forecast the water levels based on model the drainage or infiltration of an area. These models then work to predict different types of flooding depending on the Area.

Precipitation nowcasting: We developed a grounded precipitation nowcasting model that is used to run off-edge devices such as a Nvidia Jetson Nano or Raspberry Pi for the current prototype. The model uses data from sensors and pictures of clouds taken by a webcam(attached on the device) to make hourly forecasts from 0 to 10 hours in time. 

Analytical water level monitor: The device records data on current precipitation through a collection, there is a tap that simulates the drainage or infiltration, using that data as well as the precipitation data to simulate the water level of the area in the future in the ten-hour time frame.

Flood Alert System (FAST): 

It's a combination of both low-tech and high-tech. It consists of a civil alert system made up of lights and sirens that are triggered by the FLOPS.

 It also uses IOT, and communication technology that will send SMS to most of the community members hours before a flood event occurs. Depending on the level of the threat( low, medius, high risk, existential)  SMS are sent to community leaders, households, and nearest rescue centers depending on the threat, as well as crucial precautions. These Alerts are given in all three languages commonly used in Zimbabwe. For the current prototype, the Light and sirens are on the device, but they can be placed throughout the village and activated via radio, such as already existing systems. 

The data collected and generated is not only vital for immediate flood warnings but also beneficial for agricultural forecasting. It can be stored locally on the device or uploaded to the cloud, where it can be accessed through smart mobile apps by farmers for enhanced agricultural planning. This system also holds potential for further research and data collection in flood-prone areas, which are often under-documented. As the system develops, it could also be integrated into the infrastructure of developing smart cities, connecting with existing weather stations and drainage systems to enhance urban flood management.

SAILFLOW will save lives against natural disasters, support agricultural activities, and contribute to vital environmental research.

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Type1 Ai solution is SAILFLOW: A novel, low-cost, localized smart Automated, and AI-powered Last minute flood forecasting and Warning system. It’s a Stephenson screen-like device that is equipped with various weather sensors and microcontrollers, to create a portable automated weather station powered by solar energy, making it a high-tech sustainable low-cost device. SAILFlOW is based on two systems, the Flood Forecasting/prediction Systems- which was the problem of predicting flash floods, and the Flood Alert SysTem (FAST), which alerts the community of any potential threats. 

Flood Prediction system (FLOPS): we came up with a novel way of forecasting floods, which is based on using AI models to forecast precipitation hourly( precipitation Nowcasting) using real-time weather data and pictures of clouds, then use analytical techniques to forecast the water levels based on model the drainage or infiltration of an area. These models then work to predict different types of flooding depending on the Area.

Precipitation nowcasting: We developed a grounded precipitation nowcasting model that is used to run off-edge devices such as a Nvidia Jetson Nano or Raspberry Pi for the current prototype. The model uses data from sensors and pictures of clouds taken by a webcam(attached on the device) to make hourly forecasts from 0 to 10 hours in time. 

Analytical water level monitor: The device records data on current precipitation through a collection, there is a tap that simulates the drainage or infiltration, using that data as well as the precipitation data to simulate the water level of the area in the future in the ten-hour time frame.

Flood Alert System (FAST): 

It's a combination of both low-tech and high-tech. It consists of a civil alert system made up of lights and sirens that are triggered by the FLOPS.

 It also uses IOT, and communication technology that will send SMS to most of the community members hours before a flood event occurs. Depending on the level of the threat( low, medius, high risk, existential)  SMS are sent to community leaders, households, and nearest rescue centers depending on the threat, as well as crucial precautions. These Alerts are given in all three languages commonly used in Zimbabwe. For the current prototype, the Light and sirens are on the device, but they can be placed throughout the village and activated via radio, such as already existing systems. 

The data collected and generated is not only vital for immediate flood warnings but also beneficial for agricultural forecasting. It can be stored locally on the device or uploaded to the cloud, where it can be accessed through smart mobile apps by farmers for enhanced agricultural planning. This system also holds potential for further research and data collection in flood-prone areas, which are often under-documented. As the system develops, it could also be integrated into the infrastructure of developing smart cities, connecting with existing weather stations and drainage systems to enhance urban flood management.

SAILFLOW will save lives against natural disasters, support agricultural activities, and contribute to vital environmental research.

Type1 Ai solution is SAILFLOW: A novel, low-cost, localized smart Automated, and AI-powered Last minute flood forecasting and Warning system. It’s a Stephenson screen-like device that is equipped with various weather sensors and microcontrollers, to create a portable automated weather station powered by solar energy, making it a high-tech sustainable low-cost device. SAILFlOW is based on two systems, the Flood Forecasting/prediction Systems- which was the problem of predicting flash floods, and the Flood Alert SysTem (FAST), which alerts the community of any potential threats. 

Flood Prediction system (FLOPS): we came up with a novel way of forecasting floods, which is based on using AI models to forecast precipitation hourly( precipitation Nowcasting) using real-time weather data and pictures of clouds, then use analytical techniques to forecast the water levels based on model the drainage or infiltration of an area. These models then work to predict different types of flooding depending on the Area.

Precipitation nowcasting: We developed a grounded precipitation nowcasting model that is used to run off-edge devices such as a Nvidia Jetson Nano or Raspberry Pi for the current prototype. The model uses data from sensors and pictures of clouds taken by a webcam(attached on the device) to make hourly forecasts from 0 to 10 hours in time. 

Analytical water level monitor: The device records data on current precipitation through a collection, there is a tap that simulates the drainage or infiltration, using that data as well as the precipitation data to simulate the water level of the area in the future in the ten-hour time frame.

Flood Alert System (FAST): 

It's a combination of both low-tech and high-tech. It consists of a civil alert system made up of lights and sirens that are triggered by the FLOPS.

 It also uses IOT, and communication technology that will send SMS to most of the community members hours before a flood event occurs. Depending on the level of the threat( low, medius, high risk, existential)  SMS are sent to community leaders, households, and nearest rescue centers depending on the threat, as well as crucial precautions. These Alerts are given in all three languages commonly used in Zimbabwe. For the current prototype, the Light and sirens are on the device, but they can be placed throughout the village and activated via radio, such as already existing systems. 

The data collected and generated is not only vital for immediate flood warnings but also beneficial for agricultural forecasting. It can be stored locally on the device or uploaded to the cloud, where it can be accessed through smart mobile apps by farmers for enhanced agricultural planning. This system also holds potential for further research and data collection in flood-prone areas, which are often under-documented. As the system develops, it could also be integrated into the infrastructure of developing smart cities, connecting with existing weather stations and drainage systems to enhance urban flood management.

SAILFLOW will save lives against natural disasters, support agricultural activities, and contribute to vital environmental research.

Type1 Ai solution is SAILFLOW: A novel, low-cost, localized smart Automated, and AI-powered Last minute flood forecasting and Warning system. It’s a Stephenson screen-like device that is equipped with various weather sensors and microcontrollers, to create a portable automated weather station powered by solar energy, making it a high-tech sustainable low-cost device. SAILFlOW is based on two systems, the Flood Forecasting/prediction Systems- which was the problem of predicting flash floods, and the Flood Alert SysTem (FAST), which alerts the community of any potential threats. 

Flood Prediction system (FLOPS): we came up with a novel way of forecasting floods, which is based on using AI models to forecast precipitation hourly( precipitation Nowcasting) using real-time weather data and pictures of clouds, then use analytical techniques to forecast the water levels based on model the drainage or infiltration of an area. These models then work to predict different types of flooding depending on the Area.

Precipitation nowcasting: We developed a grounded precipitation nowcasting model that is used to run off-edge devices such as a Nvidia Jetson Nano or Raspberry Pi for the current prototype. The model uses data from sensors and pictures of clouds taken by a webcam(attached on the device) to make hourly forecasts from 0 to 10 hours in time. 

Analytical water level monitor: The device records data on current precipitation through a collection, there is a tap that simulates the drainage or infiltration, using that data as well as the precipitation data to simulate the water level of the area in the future in the ten-hour time frame.

Flood Alert System (FAST): 

It's a combination of both low-tech and high-tech. It consists of a civil alert system made up of lights and sirens that are triggered by the FLOPS.

 It also uses IOT, and communication technology that will send SMS to most of the community members hours before a flood event occurs. Depending on the level of the threat( low, medius, high risk, existential)  SMS are sent to community leaders, households, and nearest rescue centers depending on the threat, as well as crucial precautions. These Alerts are given in all three languages commonly used in Zimbabwe. For the current prototype, the Light and sirens are on the device, but they can be placed throughout the village and activated via radio, such as already existing systems. 

The data collected and generated is not only vital for immediate flood warnings but also beneficial for agricultural forecasting. It can be stored locally on the device or uploaded to the cloud, where it can be accessed through smart mobile apps by farmers for enhanced agricultural planning. This system also holds potential for further research and data collection in flood-prone areas, which are often under-documented. As the system develops, it could also be integrated into the infrastructure of developing smart cities, connecting with existing weather stations and drainage systems to enhance urban flood management.

SAILFLOW will save lives against natural disasters, support agricultural activities, and contribute to vital environmental research.

Type1 Ai solution is SAILFLOW: A novel, low-cost, localized smart Automated, and AI-powered Last minute flood forecasting and Warning system. It’s a Stephenson screen-like device that is equipped with various weather sensors and microcontrollers, to create a portable automated weather station powered by solar energy, making it a high-tech sustainable low-cost device. SAILFlOW is based on two systems, the Flood Forecasting/prediction Systems- which was the problem of predicting flash floods, and the Flood Alert SysTem (FAST), which alerts the community of any potential threats. 

Flood Prediction system (FLOPS): we came up with a novel way of forecasting floods, which is based on using AI models to forecast precipitation hourly( precipitation Nowcasting) using real-time weather data and pictures of clouds, then use analytical techniques to forecast the water levels based on model the drainage or infiltration of an area. These models then work to predict different types of flooding depending on the Area.

Precipitation nowcasting: We developed a grounded precipitation nowcasting model that is used to run off-edge devices such as a Nvidia Jetson Nano or Raspberry Pi for the current prototype. The model uses data from sensors and pictures of clouds taken by a webcam(attached on the device) to make hourly forecasts from 0 to 10 hours in time. 

Analytical water level monitor: The device records data on current precipitation through a collection, there is a tap that simulates the drainage or infiltration, using that data as well as the precipitation data to simulate the water level of the area in the future in the ten-hour time frame.

Flood Alert System (FAST): 

It's a combination of both low-tech and high-tech. It consists of a civil alert system made up of lights and sirens that are triggered by the FLOPS.

 It also uses IOT, and communication technology that will send SMS to most of the community members hours before a flood event occurs. Depending on the level of the threat( low, medius, high risk, existential)  SMS are sent to community leaders, households, and nearest rescue centers depending on the threat, as well as crucial precautions. These Alerts are given in all three languages commonly used in Zimbabwe. For the current prototype, the Light and sirens are on the device, but they can be placed throughout the village and activated via radio, such as already existing systems. 

The data collected and generated is not only vital for immediate flood warnings but also beneficial for agricultural forecasting. It can be stored locally on the device or uploaded to the cloud, where it can be accessed through smart mobile apps by farmers for enhanced agricultural planning. This system also holds potential for further research and data collection in flood-prone areas, which are often under-documented. As the system develops, it could also be integrated into the infrastructure of developing smart cities, connecting with existing weather stations and drainage systems to enhance urban flood management.

SAILFLOW will save lives against natural disasters, support agricultural activities, and contribute to vital environmental research.

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2 years

when I start hiring, I will hire people of various genders and ages as long as they have the relevant skills necessary for the job. As the company grows, I will also do internships for students from disadvantaged communities to learn to code and 

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through donations