Code for optimising placement of Best Management Practices (BMPs) in the Greater Hyderabad Municipal Corporation (GHMC) area, Telangana, India. This repo contains:
- code for running NSGA-III and C-TAEA multiobjective optimisation algorithms (see folder
multiobjective) [1]. - code for running fuzzy optimisation with single objective genetic algorithms and three different membership functions (see folder
fuzzy) [2].
It is recommended to run all code within a Python virtual environment. To create an environment and install dependencies:
- Create a
python3environment using the bash commandsvirtualenv .venvor any similar command. - Activate the environment using
source .venv/bin/activate. - Run
pip install -r requirements.txtto install all the required libraries.
Your environment is now ready to run the code!
Our work uses data from the Greater Hyderabad Municipal Corporation (GHMC) area to perform this optimization. Data is formatted/stored as .shp files that can be opened using almost any GIS software or in Python using the geopandas library. The data directory contains sample .shp and other files as a representation of the data format. Please note that these files contain only the data format - not the actual complete dataset itself.
If you found this repository useful in your research, please consider citing:
[1] Rohit Dwivedula, R. Madhuri, K. Srinivasa Raju, A. Vasan; Multiobjective optimisation and cluster analysis in placement of best management practices in an urban flooding scenario. Water Sci Technol 15 August 2021; 84 (4): 966–984. doi: https://doi.org/10.2166/wst.2021.283
[2] Dwivedula, R., Madhuri, R., Srinivasa Raju, K., Vasan, A. (2023). Fuzzy Optimization Framework for Facilitating Best Management Practices in the Context of Urban Floods. In: Timbadiya, P.V., Patel, P.L., Singh, V.P., Mirajkar, A.B. (eds) Geospatial and Soft Computing Techniques. HYDRO 2021. Lecture Notes in Civil Engineering, vol 339. Springer, Singapore. https://doi.org/10.1007/978-981-99-1901-7_42