scholarly journals Predicting Daily Air Pollution Index Based on Fuzzy Time Series Markov Chain Model

Symmetry ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 293 ◽  
Author(s):  
Yousif Alyousifi ◽  
Mahmod Othman ◽  
Rajalingam Sokkalingam ◽  
Ibrahima Faye ◽  
Petronio C. L. Silva
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Markov Chain ◽  
Air Quality ◽  
Pollution Index ◽  
Fuzzy Time Series ◽  
Percentage Error ◽  
Chain Model ◽  
Air Pollution Index ◽  
Proposed Model

Air pollution is a worldwide problem faced by most countries across the world. Prediction of air pollution is crucial in air quality research since it is related to public health effects. The symmetry concept of fuzzy data transformation from a single point (crisp) to a fuzzy number is essential for the forecasting model. Fuzzy time series (FTS) is applied for predicting air pollution; however, it has a limitation caused by utilizing an arbitrary number of intervals. This study involves predicting the daily air pollution index using the FTS Markov chain (FTSMC) model based on a grid method with an optimal number of partitions, which can greatly develop the model accuracy for air pollution. The air pollution index (API) data, which was collected from Klang, Malaysia, is considered in the analysis. The model has been validated using three statistical criteria, which are the root mean (RMSE), the mean absolute percentage error (MAPE), and the Thiels’ U statistic. Also, the model’s validation has been investigated by comparison with some of the famous statistical models. The results of the proposed model demonstrated outperformed the other models. Thus, the proposed model could be a better option in air pollution forecasting that can be useful for managing air quality.

Forecasting of Air Pollution Index with Artificial Neural Network

2013 ◽  
Vol 63 (2) ◽  
Author(s):  
Nur Haizum Abd Rahman ◽  
Muhammad Hisyam Lee ◽  
Mohd Talib Latif ◽  
Suhartono S.
Keyword(s):  
Neural Network ◽  
Air Pollution ◽  
Time Series ◽  
Artificial Neural Network ◽  
Air Quality ◽  
Warning System ◽  
Pollution Index ◽  
Fuzzy Time Series ◽  
Air Pollution Index ◽  
Artificial Neural

In recent years, the arisen of air pollution in urban area address much attention globally. The air pollutants has emerged detrimental effects on health and living conditions. Time series forecasting is the important method nowadays with the ability to predict the future events. In this study, the forecasting is based on 10 years monthly data of Air Pollution Index (API) located in industrial and residential monitoring stations area in Malaysia. The autoregressive integrated moving average (ARIMA), fuzzy time series (FTS) and artificial neural network (ANNs) were used as the methods to forecast the API values. The performance of each method is compare using the root mean square error (RMSE). The result shows that the ANNs give the smallest forecasting error to forecast API compared to FTS and ARIMA. Therefore, the ANNs could be consider a reliable approach in early warning system to general public in understanding the air quality status that might effect their health and also in decision making processes for air quality control and management.

scholarly journals Markov Chain Model Development for Forecasting Air Pollution Index of Miri, Sarawak

2019 ◽  
Vol 11 (19) ◽  
pp. 5190 ◽  
Author(s):  
Nurul Nnadiah Zakaria ◽  
Mahmod Othman ◽  
Rajalingam Sokkalingam ◽  
Hanita Daud ◽  
Lazim Abdullah ◽  
...  
Keyword(s):  
Air Pollution ◽  
Markov Chain ◽  
Air Quality ◽  
Markov Chain Model ◽  
Model Development ◽  
Pollution Index ◽  
Pollution Level ◽  
Chain Model ◽  
Air Pollution Index ◽  
Long Run

A Markov chain is commonly used in stock market analysis, manpower planning, and in many other areas because of its efficiency in predicting long run behavior. However, the Air Quality Index (AQI) suffers from not using a Markov chain in its forecasting approach. Therefore, this paper proposes a simple forecasting tool to predict the future air quality with a Markov chain model. The proposed method introduces the Markov chain as an operator to evaluate the distribution of the pollution level in the long term. Initial state vector and state transition probability were used in forecasting the behavior of Air Pollution Index (API) that has been obtained from the observed frequency for one state shift to another. The study explores that regardless of the present status of API, in the long run, the index shows a probability of 0.9231 for a good state, and a moderate and unhealthy state with a probability of 0.0722 and 0.0037, while for very unhealthy and hazardous states a probability of 0.0001 and 0.0009. The outcome of this study reveals that the model development could be used as a forecasting method that able to help government to project a prevention action plan during hazy weather.

scholarly journals Forecasting Air Pollution Index in Klang by Markov Chain Model

2019 ◽  
Vol 8 (6S3) ◽  
pp. 535-539
Keyword(s):  
Air Pollution ◽  
Markov Chain ◽  
Markov Chain Model ◽  
Model Development ◽  
Pollution Index ◽  
Fuzzy Time Series ◽  
Pollution Level ◽  
Chain Model ◽  
Air Pollution Index ◽  
Advantages And Disadvantages

The main purpose of analyze future air quality is to maintain the environment in good and healthy condition. Current techniques applied to forecast the air pollution index were ARIMA, SARIMA, Artificial Neural Network, Fuzzy Time Series, Machine Learning, etc. Thus, each technique has its own advantages and disadvantages in the variables, model selection and model accuracy determination. This study aims to forecast air pollution index by developing a Markov Chain model in Klang district, Selangor state which is one of the most polluted area in Malaysia. The Markov Chain model development is a stochastic process sequence that depends on the previous successive event in time. In this model development, state transition matrix and probability are the main concept in determine the future behavior of Air Pollution Index which depends on the present state of the process. The result shows that the developed model is a simple and good performance model that will forecast and evaluate the distribution of the pollution level in long term.

Air quality management using genetic algorithm based heuristic fuzzy time series model

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lalit Bhagat ◽  
Gunjan Goyal ◽  
Dinesh C.S. Bisht ◽  
Mangey Ram ◽  
Yigit Kazancoglu
Keyword(s):  
Genetic Algorithm ◽  
Air Pollution ◽  
Time Series ◽  
Air Quality ◽  
Quality Management ◽  
Fuzzy Time Series ◽  
Selection Algorithm ◽  
Content Type ◽  
Proposed Model ◽  
Adaptive Time

PurposeThe purpose of this paper is to provide a better method for quality management to maintain an essential level of quality in different fields like product quality, service quality, air quality, etc.Design/methodology/approachIn this paper, a hybrid adaptive time-variant fuzzy time series (FTS) model with genetic algorithm (GA) has been applied to predict the air pollution index. Fuzzification of data is optimized by GAs. Heuristic value selection algorithm is used for selecting the window size. Two algorithms are proposed for forecasting. First algorithm is used in training phase to compute forecasted values according to the heuristic value selection algorithm. Thus, obtained sequence of heuristics is used for second algorithm in which forecasted values are selected with the help of defined rules.FindingsThe proposed model is able to predict AQI more accurately when an appropriate heuristic value is chosen for the FTS model. It is tested and evaluated on real time air pollution data of two popular tourism cities of India. In the experimental results, it is observed that the proposed model performs better than the existing models.Practical implicationsThe management and prediction of air quality have become essential in our day-to-day life because air quality affects not only the health of human beings but also the health of monuments. This research predicts the air quality index (AQI) of a place.Originality/valueThe proposed method is an improved version of the adaptive time-variant FTS model. Further, a nature-inspired algorithm has been integrated for the selection and optimization of fuzzy intervals.

scholarly journals A FUZZY TIME SERIES-MARKOV CHAIN MODEL TO FORECAST FISH FARMING PRODUCT

Kursor ◽  
2019 ◽  
Vol 9 (4) ◽  
Author(s):  
Bagus Dwi Saputra
Keyword(s):  
Time Series ◽  
Markov Chain ◽  
Time Series Data ◽  
Markov Chain Model ◽  
Fish Farming ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Percentage Error ◽  
Chain Model ◽  
Price Fluctuations

Price is one of the important things that need to concern as defining factor of the profit or loss of product selling as the result of price fluctuations that are very difficult to control. Price fluctuations are caused by many factors including weather, stock availability, demand and others. One of the steps to solve the price fluctuations problem is by making a forecast of fish incoming prices. The purpose of this study is to apply Markov chain’s fuzzy time series to forecast farming fish prices. Markov chain fuzzy time series is one of the prediction methods to predict time series data that has advantages in the implentation of historical data, flexible, and high level of data forecasting accuracy. This study used fish prices at November 2018. The results showed that markov chain fuzzy time series showed very accurate forecasting results with a mean error percentage of absolute percentage error (MAPE) of 1.4% so the accuracy of the Markov chain fuzzy time series method is 98, 6%.

Prediction of Air Pollution Index in Kuala Lumpur using fuzzy time series and statistical models

2019 ◽  
Vol 13 (1) ◽  
pp. 77-88 ◽  
Author(s):  
Jian Wei Koo ◽  
Shin Wee Wong ◽  
Ganeshsree Selvachandran ◽  
Hoang Viet Long ◽  
Le Hoang Son
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Statistical Models ◽  
Pollution Index ◽  
Fuzzy Time Series ◽  
Kuala Lumpur ◽  
Air Pollution Index
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