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<p>Water availability in the reservoir is critically essential for the water resources managers and communities in the command and catchment areas, particularly in semi-arid regions where water is scarce. This is very important for the researchers and engineering departments to understand and establish a relationship between the catchment and the storm events so that inflow in the dam can be predicted during a storm event. This can be done using a suitable hydrological model, including inflow forecasting and predictions under various climate change scenarios. This review attempted to evaluate the use of popular traditional rainfall-runoff models and artificial intelligence (AI) tools or software for semi-arid basins. The study focused on the semi-arid basin, including compatibility, limitations and classification of the traditional and AI-based models. The study considered research done over the last twenty years, which revealed that the model selection differs according to the different catchment conditions, data types, and complexity of the events. However, soil and water analysis tool (SWAT) and hydrologic engineering center-hydrologic modeling system (HEC-HMS) have an edge over others. It is recommended that AI-based tools prove to be effective in rainfall-runoff modelling, and a hybrid approach is recommended. This review will help users or researchers select appropriate runoff prediction models depending on various conditions, especially for semi-arid basins.</p>

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Rainfall to runoff: a comprehensive review of AI and traditional models for inflow forecasting in semi-arid basin

  • 期刊论文

  • 发表日期：

    2025年6月15日

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  • Sanjay Kumar Agarwal,
  • Vineet Kumar Sharma,
  • Archana Sarkar,
  • Rohit Goyal

摘要

Water availability in the reservoir is critically essential for the water resources managers and communities in the command and catchment areas, particularly in semi-arid regions where water is scarce. This is very important for the researchers and engineering departments to understand and establish a relationship between the catchment and the storm events so that inflow in the dam can be predicted during a storm event. This can be done using a suitable hydrological model, including inflow forecasting and predictions under various climate change scenarios. This review attempted to evaluate the use of popular traditional rainfall-runoff models and artificial intelligence (AI) tools or software for semi-arid basins. The study focused on the semi-arid basin, including compatibility, limitations and classification of the traditional and AI-based models. The study considered research done over the last twenty years, which revealed that the model selection differs according to the different catchment conditions, data types, and complexity of the events. However, soil and water analysis tool (SWAT) and hydrologic engineering center-hydrologic modeling system (HEC-HMS) have an edge over others. It is recommended that AI-based tools prove to be effective in rainfall-runoff modelling, and a hybrid approach is recommended. This review will help users or researchers select appropriate runoff prediction models depending on various conditions, especially for semi-arid basins.