A Hybrid Seasonal Box Jenkins-ANN Approach for Water Level Forecasting in Thailand

Nualtong, Kittipol; Panityakul, Thammarat; Khwanmuang, Piyawan; Chinram, Ronnason; Kirtsaeng, Sukrit

doi:10.13189/eer.2021.090301

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…In this research, thanks to an expert, the essential elements were: former users' benefit, new users' benefit, sewerage works investment, drinking water works investment, population growth, inflation, sewerage operation, and maintenance costs, and drinking water operation and maintenance works. Of course, this is a simplified model since, for drinking water works, other elements, as stated by [33], are necessary. After that, the distribution of the variable was selected depending on the variables' data nature.…”

Section: Methodsmentioning

confidence: 99%

A Monte Carlo Simulation for the Improvement of Drinking Water and Sewerage Services in a Northern Settlement in Peru

Carhuanayocc¹,

Cisneros²,

Condori³

et al. 2022

eer

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Clean water and sewerage systems are essential for global and local development. In Perú, even though most of its urban population has access to these services, it does not mean that those services are enough for the population's necessities. Moreover, they usually do not have acceptable quality. Consequently, it is necessary to invest public funds to provide clean water and sewerage systems to the possible people. However, it does not mean spending money carelessly on projects that might not fulfill people's demands. Then, the current research applied a Monte Carlo-based risk analysis for a public investment project seeking to improve those services in a poor human settlement on the northern coast of Peru. Hence, the Monte Carlo simulation was employed along with its sensitivity analysis. The research found that the project had an excellent chance of being sustainable. Still, it was necessary to make real improvements in the service to meet the population's needs, for instance, by improving the water quality or expanding the hours of clean water supply. Also, due to the investment's quantity, the project's sustainability depends on population growth and public expense efficiency. Finally, the importance of correctly maintaining the works to keep and improve those services was highlighted.

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Section: Methodsmentioning

confidence: 99%

A Monte Carlo Simulation for the Improvement of Drinking Water and Sewerage Services in a Northern Settlement in Peru

Carhuanayocc¹,

Cisneros²,

Condori³

et al. 2022

eer

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“…In the mid-20th century, vinyl records became the dominant format for music distribution. Record labels competed to sign popular artists and released albums on vinyl, which were sold in record stores [13]. Physical distribution channels played a crucial role in the success of artists and albums.…”

Section: Background Of the Music Industrymentioning

confidence: 99%

Exploring the Business-Culture Relationship with Box-Jenkins ARIMA Analysis for Forecasting the Path and Future Prospects of the Popular Music Industry

Jin,

Kim

2023

IJRITCC

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Time series analysis plays a crucial role in understanding and predicting the path and future prospects of industries, including the popular music industry. This paper constructed an Box-Jenkins ARIMA (BJ-ARIMA) methodology to analyze the time series data in the popular music industry, with a focus on the relationship between business and culture. By employing the Box-Jenkins approach, BJ-ARIMA forecast future trends and make informed predictions about the development of the industry. Identification, estimation, and diagnostic testing using the BJ-ARIMA framework are the three main components of the Box-Jenkins approach. Autocorrelation and partial autocorrelation plots are analyzed in the identification phase to help choose the best BJ-ARIMA model. The estimation phase involves fitting the selected BJ-ARIMA model to the historical data, using techniques such as maximum likelihood estimation. Finally, BJ-ARIMA diagnostic checking is performed to ensure the model's adequacy and reliability. The findings of BJ-ARIMA analysis will provide a solid foundation for forecasting trends and making informed decisions in the dynamic and evolving world of the popular music industry.

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“…Hybridisation has emerged as a promising technique for overcoming numerous drawbacks of standalone methods while also improving prediction accuracy (Hajirahimi & Khashei, 2022). There are several types of hybrid models, e.g., the hybrid model that combines a physical model and a machine learning model (Nualtong et al, 2021;Xu et al, 2019), and the hybrid stochastic-ML model proposed by Koutsoyiannis and Montanari (Koutsoyiannis & Montanari, 2022). Papacharalampous (Papacharalampous et al, 2019) showed that the hybrid stochastic-ML model is better than single stochastic or ML models.…”

Section: Introductionmentioning

confidence: 99%

Application of hybrid machine learning models and data pre-processing to predict water level of watersheds: Recent trends and future perspective

et al. 2022

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The community's well-being and economic livelihoods are heavily influenced by the water level of watersheds. The changes in water levels directly affect the circulation processes of lakes and rivers that control water mixing and bottom sediment resuspension, further affecting water quality and aquatic ecosystems. Thus, these considerations have made the water level monitoring process essential to save the environment. Machine learning hybrid models are emerging robust tools that are successfully applied for water level monitoring. Various models have been developed, and selecting the optimal model would be a lengthy procedure. A timely, detailed, and instructive overview of the models' concepts and historical uses would be beneficial in preventing researchers from overlooking models' potential selection and saving significant time on the problem. Thus, recent research on water level prediction using hybrid machines is reviewed in this article to present the "state of the art" on the subject and provide some suggestions on research methodologies and models. This comprehensive study classifies hybrid models into four types algorithm parameter optimisation-based hybrid models (OBH), pre-processingbased hybrid models (PBH), the components combination-based hybrid models (CBH), and hybridisation of parameter optimisation-based with preprocessing-based hybrid models (HOPH); furthermore, it explains the pre-processing of data in detail. Finally, the most popular optimisation methods and future perspectives and conclusions have been discussed.

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A Hybrid Seasonal Box Jenkins-ANN Approach for Water Level Forecasting in Thailand

Cited by 5 publications

References 8 publications

A Monte Carlo Simulation for the Improvement of Drinking Water and Sewerage Services in a Northern Settlement in Peru

A Monte Carlo Simulation for the Improvement of Drinking Water and Sewerage Services in a Northern Settlement in Peru

Exploring the Business-Culture Relationship with Box-Jenkins ARIMA Analysis for Forecasting the Path and Future Prospects of the Popular Music Industry

Application of hybrid machine learning models and data pre-processing to predict water level of watersheds: Recent trends and future perspective

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