Data for designing discharge monitors in Surma River

Data (attached) focuses on a case study of designing and evaluating the discharge monitoring station of the Surma River using the entropy-based method. In the first phage, a 1-D model has been developed for the Surma River to extract the time series of discharge data. Afterward, two entropy contents (Joint entropy and total Correlation) were used to design and evaluate the optimal number and placement of the monitoring stations in the Surma River. Non-dominated sorting genetic algorithm II (NSGA-II by Dev et al., 2002) and Greedy algorithm (Alfonso et al., 2013; Banik et al., 2017a) have optimized the monitoring network.

1. MIKE-II model data: Data containing this folder had been used to build the 1-D hydrodynamic model. The model was built for two datasets (2015-2019 and 2021-2022)
2. Calibration data (.csv file )
3. Extracted time series discharge data (2015-2019 and 2021-22) for valuating the optimal number and placement of the monitoring stations in the Surma River (.csv file)