Estimating rainfall–runoff modeling using the rainfall prognostic model-based artificial framework with a well-ordered selective genetic algorithm. Issue 5 (5th September 2022) - Kumar, Shailesh; Pandey, K. K.; Kumar, Sunil; Supriya, Sunidhi
Abstract: Rainfall–runoff modeling is one of the most well-known applications of hydrology. The goal of rainfall–runoff modeling is to simulate the peak river flow caused by an actual or hypothetical rainfall force. In existing methods, the rainfall–runoff relationships are quantified to predict the daily streamflow of each catchment from its landscape attributes to measure the daily rainfall. However, the structural model error, infiltration rate, and the steep slopes of the hill affect the prediction process. To tackle these issues, this paper proposed a novel rainfall prognostic model-based artificial framework, which predicts day-to-day rainfall to prevent environmental disasters. The day-to-day predictions minimize the risks to life and property and also manage the agricultural farms in a better way because the possibility of rainfall has been estimated earlier. Furthermore, the posterior fire-breathing network is utilized to estimate model errors in the computational runoff by using time-dependent and random noise to the model's internal storage to solve the uncertainty problem. Since the model errors are estimated, there are limits to the infiltration rate and thus a prophetic multilayer network is utilized which relies on the soil runoff levels. Moreover, the network measures the dynamics of soil moisture to regulate the infiltration rate according to the rural or urban section. Moreover, to measure the surface water from the steep slopes, the system offered a well-ordered selective genetic algorithm to calculate the velocity of runoff in different bend areas to overcome the numerical problem. Thus, the model results showed that the work effectively predicts the rainfall from the investigation of model errors, infiltration rates, and velocity to achieve a better prediction range in the rainfall. HIGHLIGHTS: Despite the progress made in recent years, modeling hydrological reactions to rainfall prediction remains a complex task in runoff modeling. Thus the presented paper effectively introduced a rainfall prognostic artificial model framework for the prediction of rainfall. The framework applied a posterior fire breathing network to estimate model errors with random noise to reduce the uncertainty. Further to regulating the infiltration rate, the system suggested a prophetic multilayer network which analyses the runoff levels with the soil moisture in urban and rural areas. In addition, to evaluate the velocity at low water depths on steep slopes, the model incorporates a well-ordered selective genetic algorithm to forecast different bend areas to overcome the numerical problem. Thus, from the rainfall model, the daily rainfall is efficiently predicted to prevent the environmental glitches. Experimental results show that the framework exhibits the highest prediction range of 12-20 mm with the subjective results, and outperforms the existing runoff models with a better infiltration rate of 1.5 cm/hr, runoff level 0.05 cm, and the obtained velocity of 0.45 mm.