A novel nowcasting (estimation) model based on an adaptive network neutrosophic hesitant fuzzy inference system (ANNHFIS): a case study of Istanbul

Abstract

Although biomass power plants are cleaner than fossil-fuel-based plants, they emit nitrogen dioxide (NO2), which can degrade urban air quality and pose respiratory health risks. Therefore, reliable estimation (nowcasting) of NO2 levels around these facilities is crucial for public health and air quality management. This study proposes an adaptive network-based neutrosophic hesitant fuzzy inference system optimized by particle swarm optimization (ANNHFIS-PSO) to estimate NO2 concentrations near biomass plants in Istanbul. To our knowledge, this is the first adaptive neuro-fuzzy inference system (ANFIS)-based framework that incorporates neutrosophic hesitant fuzzy sets to represent environmental uncertainty. The proposed model integrates a neural network with neutrosophic hesitant fuzzy membership functions and employs a hybrid learning scheme that combines PSO-based global optimization with Adam-based fine-tuning to capture nonlinear relationships. Its performance was benchmarked against multilayer perceptron artificial neural network (MLP-ANN), ANFIS-PSO, grid-search-tuned ANFIS (ANFIS-GS), long short-term memory (LSTM) network and ANNHFIS-GS. Model accuracy was evaluated using metrics including root mean square error (RMSE) and coefficient of determination (R & sup2;). On the test dataset, ANNHFIS-PSO achieved an RMSE of 3.6488 & micro;g/m & sup3; and an R & sup2; of 0.8938, yielding the lowest RMSE and a high R & sup2; among the evaluated models. These results suggest that the proposed approach may support decision-making for air quality management near biomass plants.