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)

本附带数据集围绕基于熵方法的苏尔马河（Surma River）流量监测站设计与评估展开案例研究。第一阶段，研究者针对苏尔马河构建一维水动力模型，用于提取流量时序数据。后续，采用两类熵指标——联合熵（Joint entropy）与总相关（total Correlation），设计并评估苏尔马河监测站的最优布设数量与位置。研究通过非支配排序遗传算法II（Non-dominated sorting genetic algorithm II，NSGA-II；Dev等，2002）与贪心算法（Greedy algorithm；Alfonso等，2013；Banik等，2017a）对监测网络进行优化。

1. MIKE-II模型数据：该文件夹内的数据用于构建一维水动力模型，模型基于2015-2019年与2021-2022年两套数据集搭建。
2. 校准数据（.csv格式文件）
3. 用于评估苏尔马河监测站最优布设数量与位置的提取流量时序数据（覆盖2015-2019年及2021-2022年），存储为.csv格式文件。