Model Library

Capabilities

• Offering extensive capabilities for modeling intricate river and channel networks, lakes, reservoirs, as well as river and drainage systems (Dang et al., 2024);
• Supporting parallel processing;
• Facilitating water quality assessments in river systems (Bansal et al., 2024):
• Supporting analysis of 2D surface water flow (Bansal et al., 2024);
• Enabling tailored modifications (Bansal et al., 2024);
• “User-centric design” (Bansal et al., 2024);
• The model is supported by the developers.

Limitations

• Not open-source;
• The model's accuracy depends on the quality and quantity of input data, with incomplete or biased datasets leading to suboptimal performance.
• It may inherit limitations of MIKE 11 and MIKE HYDRO River;
• While MIKE+ supports the import and conversion of existing models from MIKE 11 or MIKE HYDRO River, some model setups might need adjustments due to differences in how data is managed and validated in MIKE+ (DHI, 2025);
• It is not recommended to open SWMM and Water Distribution result files in MIKE+ without also opening the project database (DHI, 2025);
• The use of a PostGIS database requires a PostgreSQL installation (DHI, 2025).

Inputs

Topography data, Boundary conditions, LULC data, Soil data, Meteorological data, Hydrological data, Water quality data.

Outputs

• Reports on time-series hydrological and water quality simulation.
• Analysis of long-term impacts of LIDs and scenarios.
• Flood simulation.

References

Thanh Quang Dang, Ba Hoang Tran, Quyen Ngoc Le, Thanh Duc Dang, Ahad Hasan Tanim, Quoc Bao Pham, Van Hieu Bui, Son T. Mai, Phong Nguyen Thanh, & Duong Tran Anh. (2024). Application of machine learning-based surrogate models for urban flood depth modeling in Ho Chi Minh City, Vietnam Image 1. Applied Soft Computing, 150, 111031. https://doi.org/10.1016/j.asoc.2023.111031

Sharma, A., Kumar, A., Shankar, V., & Thakur, P. K. (2024). Hydrological modeling for rainfall–runoff assessment in a sub-Himalayan catchment using a distributed MIKE SHE/MIKE+ model. Water Practice and Technology, 19(7), 2929–2943. https://doi.org/10.2166/wpt.2024.158
4o mini

Objectives

The objectives of this study were: (1) to develop a coupled 1D–2D drainage model for simulating urban flood in HCMC using MIKE+ software; (2) to test a wide range of machine learning algorithms to evaluate their surrogate modelling capabilities to simulate surface flooding depth in coastal urban cities; 3) to compare the performance of surrogate ML models with high fidelity and computationally expensive 1D–2D drainage models.

The main objectives of the study were: (1) to develop a hydrological model for the study area using a distributed MIKE SHE/MIKE+ coupled approach; (2) calibration and validation of the developed model using observed discharge data for the study area; and (3) to perform sensitivity analysis on the developed model by using the One-at-a-Time sensitivity measures.