The solution for this challenge is a machine learning model as required in the challenge. So, we decided to make a website and developed it by some programming languages like HTML and others. The websites is mainly depended on the satellite sensor date and blog it in the website to make the people know about the expected disasters in the nearby time, the details, causes and location to avoid the bad sites as possible as we can. so, the website will divide into three parts. First, what are the expected disaster. Second, previous ones. Third is how to deal with. All of that will help our world to avoid that and protect our community from being damaged.
The main reason that inspired us to work in his challenge is the floods in Sudan that caused a high number of dead people and many losses. Also, damage some places. According to that we realize the main value for this machine learning model to help the communities avoid all this bad effect. So, we approached our website to make the people know about the disaster expectations, previous stories with same disaster to know about the causes and the results, and finally how to deal with by taking the full safety precautions. Using programming languages such as HTML and CSS and adobe dream weaver app tO complete our software project. There are some problems that faced us in achieving the project like the programming section but, we did not give up and use an app to help in achieving the software of the website to get our idea out to the light for real help for people.
We have used NASA data to understand the challenge more and help us to know examples of problems that may occur with scientists in space, and there was a prior solution to this problem, and this solution is that they use dramatic images of tropical cyclones to understand the size and intensity of the storm.
NASA data
Hindawi
Arduino
Adobe Dreamweaver