Computation of Flow Coefficient via Non-deterministic Approach of Fuzzy Logic Called "SMRGT" Based on Meteorological Properties

Abstract

In light of the current global climate changes, floods have emerged as a significant hydraulic and hydrological challenge on a global scale. The primary contributors to the expansion of impermeable areas and the intensification of flood flow are extensive urbanization, the proliferation of concrete edifices and the construction of asphalt thoroughfares. Anticipating the flow beforehand will be conducive to the successful execution of the task at hand. The objective is to reduce the likelihood of harm to individuals and damage to assets. By accurately determining the flow coefficient, which is a significant factor in flood flow, it is possible to mitigate existing issues to a significant degree. Numerous methodologies for modeling flow coefficients can be found in the extant literature. However, most of these methodologies rely on black-box techniques and are not easily generalizable. Hence, the present investigation has opted for a novel methodology; namely, the fuzzy SMRGT method that takes into account the physical characteristics of the phenomenon and is designed to assist individuals who encounter difficulties in selecting the appropriate quantity, structure and rationale of membership functions and fuzzy rules within a given fuzzy set. The data comprising annual precipitation, temperature and relative humidity measurements was acquired from the Regional Directorate of Meteorology. The model outcomes were juxtaposed with the actual observations. Statistical parameters, such as the coefficient of determination (R2 ), the root mean square error (RMSE), the Nash-Sutcliffe efficiency coefficient (NSE) and the mean absolute percentage error (MAPE), were used to evaluate the performance of the model. The statistical test results were: (RMSE: 0.096, NSE: 0.90, MAPE: 17.3, R2 :0.96). The findings suggest that the SMRGT model is highly effective in accurately forecasting the flow coefficient and represents a robust approach for constructing membership functions and fuzzy rules. KEYWORDS: Fuzzy logic, Uncertainty modeling, SMRGT, Flow coefficient, Precipitation, Mamdani fuzzy inference system.