Grey forecast can be used to predict behavior of non-linear time series. This is a non-statistical forecasting method that is particularly effective when the number of observations is insufficient.
This Project contains two approches for implement gray model.
1. Project with Angular frontend and flask(python) backend using gray 1.1 model
2. Full python project to predict flood using gray 1.1 model.
Here How to Run Angular Project
(You need only two folder to run grayModelAPI and floodprediction. (Other contents for further referance if needed)
Clone the repo.
git clone https://github.com/YohanKulasinghe/floodPrediction_grayModel.git
From root folder run followings (Root folder - floodForcasting)
cd floodForcasting
npm install
ng serve
From root folder run followings (Root folder - grayModel)
cd grayModel
pip install Flask
pip install -U flask-cors
pip install flask-restful
pip install flask-jsonpify
pip install matplotlib
pip install pandas
pip install xlrd
python run model.py
You will find a tab in nav bar to navigate prediction Precision checker.
https://colab.research.google.com/drive/1pLc1RD-NyBu4xamPgRY5u5A7W6whqpnv
Here How to Run Python Project
(You need only one folder to run completePython. (Other contents for further referance if needed)
Clone the repo.
git clone https://github.com/YohanKulasinghe/floodPrediction_grayModel.git
From root folder run following (root folder - completePython)
python fulPythonProject.py
Other Content
Corrilation folder contains the proof of rainfall and water level is having positive corrilation
cd in to correlation folder and execute following line
python fulPythonProject.py
Comparrision folder contains the graph shows waterlevel and rainfall fluctuation
cd in to Comparrision folder and execute following line
python comparison.py
2007-2012 folder contains monthwise analysis of data
cd in to 2007-2012 folder and execute following line
python 2007-2012.py