Title: RiCSO-based RiDeep LSTM: rider competitive swarm optimiser enabled rider deep LSTM for air quality prediction

Authors: Deepika Dadasaheb Patil; T.C. Thanuja; Bhuvaneshwari C. Melinamath

Addresses: Computer Science and Engineering, Sanjay Ghodawat University, India; Vishveshvaraya Technological University, Belgavi, India ' Department of VLSI and Embedded System, Visvesvaraya Technological University, India ' Computer Science and Engineering, BMSIT&M College, Bangalore, India

Abstract: This paper is for air quality prediction. Here, the time-series data is considered for the effective prediction of air quality. Moreover, missing value imputation is applied in this model to perform pre-processing. The technical indicators are extracted as features for the effectual prediction of air quality. The rider deep long short-term memory (LSTM) is also included for predicting air quality, trained by a developed RCSO algorithm. Moreover, the developed rider competitive swarm optimisation (RCSO) approach is newly devised by incorporating rider optimisation algorithm (ROA) and competitive swarm optimiser (CSO). The performance of the developed air quality prediction model is evaluated using several error metrics. The introduced air quality prediction system obtained a minimum mean square error (MSE) of 0.10, a root mean square error (RMSE) of 0.31, a mean absolute percentage error (MAPE) of 8.34%, and mean absolute scaled error (MASE) of 0.30. The results demonstrated that the developed RCSO-based rider deep LSTM model attained better performance than other techniques.

Keywords: air quality prediction; competitive swarm optimiser; CSO; rider optimisation algorithm; ROA; rider deep LSTM; triple exponential moving average; TEMA.

DOI: 10.1504/IJIDS.2025.144261

International Journal of Information and Decision Sciences, 2025 Vol.17 No.1, pp.51 - 75

Received: 20 May 2021
Received in revised form: 04 Oct 2021
Accepted: 17 Oct 2021
Published online: 04 Feb 2025 *