Assessing the effectiveness of long short-term memory and artificial neural network in predicting daily ozone concentrations in Liaocheng City

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 25, 2025

Ozone pollution affects food production, human health, and the lives of individuals. Due to rapid industrialization urbanization, Liaocheng has experienced increasing ozone concentration over several years. Therefore, become a major environmental problem in City. Long short-term memory (LSTM) artificial neural network (ANN) models are established predict concentrations City from 2014 2023. The results show general improvement accuracy LSTM model compared ANN model. Compared ANN, an increase determination coefficient (R2), value 0.6779 0.6939, decrease root mean square error (RMSE) 27.9895 μg/m3 27.2140 absolute (MAE) 21.6919 20.8825 μg/m3. prediction is superior terms R, RMSE, MAE. In summary, promising technique for predicting concentrations. Moreover, by leveraging historical data enables accurate predictions future on global scale. This will open up new avenues controlling mitigating pollution.

Language: Английский

Comprehensive Study of Population Based Algorithms

American Journal of Computer Science and Technology, Journal Year: 2024, Volume and Issue: 7(4), P. 195 - 217

Published: Dec. 23, 2024

The exponential growth of industrial enterprise has highly increased the demand for effective and efficient optimization solutions. Which is resulting to broad use meta heuristic algorithms. This study explores eminent bio-inspired population based techniques, including Particle Swarm Optimization (PSO), Spider Monkey (SMO), Grey Wolf (GWO), Cuckoo Search (CSO), Grasshopper Algorithm (GOA), Ant Colony (ACO). These methods which are inspired by natural biological phenomena, offer revolutionary problems solving abilities with rapid convergence rates high fitness scores. investigation examines each algorithm's unique features, properties, operational paradigms, conducting comparative analyses against conventional methods, such as search history, functions express their superiority. also assesses relevance, arithmetic andlogical efficiency, applications, innovation, robustness, andlimitations. findings show transformative potential these algorithms offering valuable wisdom future research enhance broaden upon methodologies. finding assists a guiding researchers enable inventive solutions in advancing field optimization.

Language: Английский