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Flood Prediction #887
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Thank you for creating this issue! We'll look into it as soon as possible. Your contributions are highly appreciated! 😊 |
What are the models you are planning to implement here for this problem statement? |
@abhisheks008 These K-Nearest Neighbors (KNN) |
Hi @thevijayshankersharma this project repository mainly focuses on deep learning methods instead of machine learning methods. You need to update your approach and get back again. |
Since no dataset provided, I want to use the dataset inspired from this published paper. |
Hi @SHREERAJ11 hope you are doing well. The dataset is good. Thanks for sharing it. |
Hello! @abhisheks008 I'm doing well, thanks. For this problem, I want to use ResNet. Based on the size, I think ResNet34 would be okay. I will also try ResNet50 if not constrained by my resources, also experiment with mobilenet architecture for comparison. |
Apart from ResNet what are the other models you are planning to implement here? As you know, you need to implement 3-4 models for each problem statement. |
I will be implementing ResNet, MobileNet, EfficientNet, and VGG19 models. |
Assigning this issue to you @SHREERAJ11 |
@abhisheks008 assign me this |
Deep Learning Simplified Repository (Proposing new issue)
🔴 Project Title : Flood Prediction Using Machine Learning
🔴 Aim : To develop machine learning models for accurate flood prediction by analyzing historical data, weather patterns, topographical information, and real-time sensor inputs. This will improve flood warnings, emergency response, and planning strategies.
🔴 Dataset : Historical flood data, weather data, topographical data, and real-time sensor inputs. (Specific dataset sources can be mentioned once identified.)
🔴 Approach : Implement 3-4 different machine learning algorithms to develop flood prediction models.
📍 Follow the Guidelines to Contribute in the Project :
requirements.txt
- This file will contain the required packages/libraries to run the project in other machines.Model
folder, theREADME.md
file must be filled up properly, with proper visualizations and conclusions.🔴🟡 Points to Note :
✅ To be Mentioned while taking the issue :
Happy Contributing 🚀
All the best. Enjoy your open source journey ahead. 😎
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