Search

This study proposes a new five-parameter distribution called the gamma-exponentiated Weibull–Poisson (GEWP) distribution. As an extension of the exponentiated Weibull–Poisson family, the GEWP distribution offers a more flexible tool for analyzing a wider variety of data due to its theoretically and practically advantageous properties. It encompasses established distributions like the exponential, Weibull, and exponentiated Weibull. The development of the GEWP distribution proposed in this paper is obtained by combining the gamma–exponentiated Weibull (GEW) and the exponentiated Weibull–Poisson (EWP) distributions. Therefore, it serves as an extension of both the GEW and EWP distributions. This makes the GEWP a viable alternative for describing the variability of occurrences, enabling analysis in situations where GEW and EWP may be limited. This paper analyzes the probability distribution functions and provides the survival and hazard rate functions, the sub-models, the moments, the quantiles, and the maximum likelihood estimation of the GEWP distribution. Then, the numerical experiments for the parameter estimation of GEWP distribution for some finite sample sizes are presented. Finally, the comparative study of GEWP distribution and its sub-models is investigated via the goodness of fit test with real datasets to illustrate its potentiality. [ABSTRACT FROM AUTHOR]

This study applies statistical methods and deep learning techniques to forecast the daily average PM2.5 concentration in northern Thailand, where the concentration is usually high and exceeds the safe level. The data used in the analysis are collected from January 2018 to December 2020 from 16 air monitoring stations. The statistical methods used are Holt-Winters exponential smoothing (ETS), autoregressive integrated moving average (ARIMA), and dynamic linear model (DLM). The deep learning techniques considered in this study are the recurrent neural network (RNN) and long-short term memory (LSTM). To compare the predictive performance of both methods, we use the root mean square error (RMSE). The result indicates that statistical methods, especially ARIMA, perform better than the deep learning techniques in most stations. Moreover, LSTM tends to provide higher accuracy than the RNN, especially with more number of nodes. [ABSTRACT FROM AUTHOR]

The water quality index (WQI) is an aggregated indicator used to represent the overall quality of water for any intended use. It is typically calculated from several biological, chemical, and physical parameters. Assessment of factors that affect the WQI is then essential. Climate change is expected to impact a wide range of water quality issues; hence, climate variables are likely to be significant factors to evaluate the WQI. We propose three statistical models; multiple linear regression (MLR), artificial neuron network (ANN), and Gaussian process regression (GPR) to assess the WQI using the climate variables. The data is the WQI of Ping River, which flows through the provinces in the north of Thailand. The climate variables are temperature, humidity, total rainfall, and evaporation. A comparison between these models is determined by model prediction accuracy scores. The results show that the total rainfall is the most significant variable to predict the WQI for the Ping River. Although these three methods can predict the WQI relatively good, overall, the GPR model performs better than the MLR and the ANN. Besides, the GPR is more flexible as it can relax some restrictions and assumptions. Therefore, the GPR is appropriate to assess the WQI under the climate variables for the Ping River. [ABSTRACT FROM AUTHOR]