High-Level Project Summary
Various researches and information have been published in many countries about the landslides that have occurred in recent years. In the statistics, it has been understood that the digital maps showing the data used in the geographical systems provide precautions for today's landslides. With these maps, the probability ratios of landslide areas can be determined. Studies are in progress for the parameters used on the maps but not yet proven. Landslide; It is graded in direct proportion to the regional, climatic, soil structure, soil slope, vegetation, the amount of precipitation the region receives, and the landslide history of the region.
Link to Project "Demo"
Link to Final Project
Detailed Project Description
Various researches and information have been published in many countries about the landslides that have occurred in recent years. In the statistics, it has been understood that the digital maps showing the data used in the geographical systems provide precautions for today's landslides. With these maps, the probability ratios of landslide areas can be determined. Studies are in progress for the parameters used on the maps but not yet proven. Landslide; It is graded in direct proportion to the regional, climatic, soil structure, soil slope, vegetation, the amount of precipitation the region receives, and the landslide history of the region.
Using this information, we designed a machine called DRONE, which has a predictive feature of landslides, and STEP 63 to compact the stones and soil in the region in order to prevent the deadly consequences of severe landslides, especially in some regions of the world. We used an underground power for STEP 63.
In addition, we 3D modeled a map showing the regions of the country with high landslide risk. In our software work, we aimed to transfer data between AFAD and COOLR. We used an ARDINUO circuit inside the data center we made.
We have modeled a DRONE that will detect possible landslide signals by reconnaissance in landslide areas, notify the authorities, take precautions and prevent major losses. We modeled 3D maps to indicate risks and give people a chance to think more clearly. This DRONE memorizes the existing landslide statistics map and in line with this information, it has the equipment to go on a reconnaissance tour in areas with high landslide risk in the region and emit signals in case of danger. The DRONE will transmit the signals of the risky areas it detects to the Crowdsource Reporter. However, data from COOLR authorities is collected in the Crowdsource Reprter. Therefore, the data is collected under a single roof.
COOLR officials share their data with AFAD in order to protect the landslide area and protect it from great losses. A system model is created to implement the application for this data transfer between COOLR and AFAD officials. The AFAD team flies their DRONEs to determine the location and coordination. Then, in order to stop the landslide, a machine called STEP 63 was modeled to compress the soil in the direction of the landslide. During a landslide, STEP 63 senses the accelerating and moving soil and takes the action of compaction and stops the natural disaster. Then, the compacted soil is removed with the scoop. In this way, loss of life and property is prevented.
Space Agency Data
https://gpm.nasa.gov/landslides/resources.html
https://gpm.nasa.gov/applications/landslides#modelingandreportinglandslides
https://gpm.nasa.gov/data/imerg
https://earthdata.nasa.gov/
Hackathon Journey
It was exciting to be involved in scientific research. We enjoyed being a part of NASA. It was a good experience for us to access scientific resources. Now we can all be good researchers.
References
tinkercad
visual studio
excel
actiivinspire
google academic
ArcGIS
Crawd source reporter
Tags
#landslide#nasa#tinkercad#visualstudio
Global Judging
This project has been submitted for consideration during the Judging process.