Enhancing Rainfall-Runoff Simulation via Meteorological Variables and a Deep-Conceptual Learning-Based Framework

Accurate streamflow simulation is crucial for many applications, such as optimal reservoir operation and irrigation. Conceptual techniques employ physical ideas are suitable representing the physics of hydrologic model, but they might fail in competition with their more advanced counterparts. In contrast, deep learning (DL) approaches provide a great computational capability simulation, rely on data characteristics issue cannot be fully understood. To overcome these limitations, current study provided novel framework based combination conceptual DL enhancing accuracy snow-covered basin. this regard, simulated daily Kalixälven river basin northern Sweden by integrating snow-based hydrological model (MISD) model. Daily precipitation, air temperature (average, minimum, maximum), dew point temperature, evapotranspiration, relative humidity, sunshine duration, global solar radiation, atmospheric pressure were used inputs to examine effect each meteorological variable simulation. Results proved that adding variables underframe parallel settings can improve simulating The MISD had an MAE = 8.33 (cms), r 0.88, NSE 0.77 validation phase. proposed deep-conceptual learning-based also performed better than standalone model; method 7.89 0.90, 0.80 phase when results combined integrated rainfall-runoff research new concept modeling which accurate simulations.