Green AI

Our problem is the lack of vegetation, In this project we tried to find the best way to plant trees as efficiently as possible to reduce the process of global warming using artificial intelligence based on factors including environmental pollutants in the air, temperatures, population density, the ROI standard that measures fine particles with temperatures so that the artificial intelligence model finds the number of trees that should be planted in the region, neighborhoods, and any A spot in the kingdom was big or small based on geographical locations . We are creating a deep-learning model with the help of Long Short-Term Memory (LSTM) networks a modified version of recurrent neural networks (RNN), to predict the number of trees that should be planted in each city based on population density, air pollution, and temperature. air pollution data collected during covid - 19 was used in the regions and geographic area data. as the start of the project, the model was applied to the Dammam region to identify the neighborhoods in it and determine the percentages of environmental pollution present in it and temperatures and we found the ROI standard so that the darker the area or neighborhood, the more trees it needs to be planted. :And of course our project Achieving one of the goals of sustainable development by ensuring a healthy life and promoting luxury. - Accelerate and facilitate the process of achieving one of the goals of Vision 2023 by using artificial intelligence mechanisms. - Increasing the country's economy. - Preserving the environment. We used Python for programming and interfaces HTML , CSS , and Javascript For the tools we used Flask as a framework. As well as Colab for Python and Model Finally, we used visual studio to apply all the details of the project. As a future direction, we aspire to add more features to the system that will improve our environment like : integrate more features into our prediction model.