Prediction of water quality index (WQI) using support vector machine (SVM) and least square-support vector machine (LS-SVM)

Leong, Wei Chiat; Bahadori, Alireza; Jie, Zhang; Ahmad, Zainal Arifin

doi:10.1080/15715124.2019.1628030

Cited by 150 publications

(70 citation statements)

References 11 publications

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“…Support Vector Machine (SVM) is a very special learning algorithm, which is characterized by the capacity control of decision function, the use of kernel functions and the scarcity of the solution [ 76 ]. It has many advanced features with good generalization capabilities and fast computing capabilities [ 77 ]. The support vector machine adopts a risk function composed of empirical error and a regularization term derived based on the principle of structural risk minimization, which is a promising method for predicting financial time series [ 78 ].…”

Section: Methodsmentioning

confidence: 99%

A Labeling Method for Financial Time Series Prediction Based on Trends

Wang

et al. 2020

Entropy

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Time series prediction has been widely applied to the finance industry in applications such as stock market price and commodity price forecasting. Machine learning methods have been widely used in financial time series prediction in recent years. How to label financial time series data to determine the prediction accuracy of machine learning models and subsequently determine final investment returns is a hot topic. Existing labeling methods of financial time series mainly label data by comparing the current data with those of a short time period in the future. However, financial time series data are typically non-linear with obvious short-term randomness. Therefore, these labeling methods have not captured the continuous trend features of financial time series data, leading to a difference between their labeling results and real market trends. In this paper, a new labeling method called “continuous trend labeling” is proposed to address the above problem. In the feature preprocessing stage, this paper proposed a new method that can avoid the problem of look-ahead bias in traditional data standardization or normalization processes. Then, a detailed logical explanation given, a definition of continuous trend labeling was proposed and also an automatic labeling algorithm was given to extract the continuous trend features of financial time series data. Experiments on the Shanghai Composite Index and Shenzhen Component Index and some stocks of China showed that our labeling method is a much better state-of-the-art labeling method in terms of classification accuracy and some other classification evaluation metrics. The results of the paper also proved that deep learning models such as LSTM and GRU are more suitable for dealing with the prediction of financial time series data.

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Section: Methodsmentioning

confidence: 99%

A Labeling Method for Financial Time Series Prediction Based on Trends

Wang

et al. 2020

Entropy

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“…The high number of dependent variables requires higher computing power to process the large dataset. Several researches have carried out input sensitivity for the determination of the relative importance of each parameter in the specific area of study [151][152][153][154][155][156][157][158][159]. The analysis is conducted through the leave in one out approach by excluding one parameter to determine the percentage of contribution to the calculation of water quality index [151,152].…”

Section: Figure 3 Relation Of Input Attributes Modelling Techniques and Performance Metrics Of Water Quality Prediction Reviewed In This mentioning

confidence: 99%

Water, Soil and Air Pollutants’ Interaction on Mangrove Ecosystem and Corresponding Artificial Intelligence Techniques Used in Decision Support Systems - A Review

et al. 2021

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The feasibility of artificial intelligence (AI) as a predictive model for thorough efficacy analysis on environmental pollution applied on mangrove forests are discussed. Mangrove forests are among the most productive and biological diverse ecosystems on the planet. However, due to environmental pollution and climate change, mangrove forests are in serious decline. Despite crucial issues pertaining mangrove forests, the law enforcement on the ecosystem is still dubious due to the lack of evidence and data that could provide accurate analysis and prediction. The main highlight of this review elaborates on pollutant markers in soil, water, and air, by correlating these three aspects to the sustainability of mangrove ecosystem. The research gap identified from this review suggests the application of an integrated environmental prediction system for practical environmental insights. A predictive model for environmental decision-making could be developed by integrating meteorological, climatological, hydrological, atmospheric, and heavy metal concentration to understand the interaction between each factor for an efficient solution of pollutant reduction scheme involving mangrove ecosystems. INDEX TERMS Mangrove estuarine; pollutant interaction; environmental quality modelling; integrated environmental decision system.

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“…Then this type of image classification approach falls under object-based change detection ( ) (Zhang et al 2018 ). When the comparison is made between two images using methodologies, they are compared based on standard classification features, i.e., “user accuracy ( ) (Tong and Feng 2020 )”, “producer accuracy ( ) (Tong and Feng 2020 )”, “commission error ( ) (Agariga et al 2021 ),” “omission error ( ) (Agariga et al 2021 ),” “overall accuracy ( ) (Tong and Feng 2020 )”, and “kappa coefficient ( K p ) (Tong and Feng 2020 ).” Some of the prominent image classification techniques using object formation to classify an image are “Maximum likelihood classification ( )” (Soni et al 2021 ), “Spectral angle mapper ( )” (Wang et al 2021 ), “Support vector machine ( )” (Leonga et al 2021 ), “Minimum distance classification ( )” (Nie et al 2021 ), “Parallelepiped classification (PC)” (Kundu et al 2021 ), and “Spectral information divergence (SID)” (Hunt 2021 ). Brief details of these classification techniques and their methodologies are presented in these reviews.…”

Section: Background Of Pbcd (Glcm) and Obcd Techniquesmentioning

confidence: 99%

Modeling of texture quantification and image classification for change prediction due to COVID lockdown using Skysat and Planetscope imagery

Shakya

Ramola

Vidyarthi

2021

Model. Earth Syst. Environ.

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This research work models two methods together to provide maximum information about a study area. The quantification of image texture is performed using the “grey level co-occurrence matrix ( )” technique. Image classification-based “object-based change detection ( )” methods are used to visually represent the developed transformation in the study area. Pre-COVID and post-COVID (during lockdown) panchromatic images of Connaught Place, New Delhi, are investigated in this research work to develop a model for the study area. Texture classification of the study area is performed based on visual texture features for eight distances and four orientations. Six different image classification methodologies are used for mapping the study area. These methodologies are “Parallelepiped classification ( ),” “Minimum distance classification ( ),” “Maximum likelihood classification ( ),” “Spectral angle mapper ( ),” “Spectral information divergence ( )” and “Support vector machine ( ).” calculations have provided a pattern in texture features contrast, correlation, , and . Maximum classification accuracy of and are obtained for pre-COVID and post-COVID image data through classification technique. Finally, a model is presented to analyze before and after COVID images to get complete information about the study area numerically and visually.

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Prediction of water quality index (WQI) using support vector machine (SVM) and least square-support vector machine (LS-SVM)

Cited by 150 publications

References 11 publications

A Labeling Method for Financial Time Series Prediction Based on Trends

A Labeling Method for Financial Time Series Prediction Based on Trends

Water, Soil and Air Pollutants’ Interaction on Mangrove Ecosystem and Corresponding Artificial Intelligence Techniques Used in Decision Support Systems - A Review

Modeling of texture quantification and image classification for change prediction due to COVID lockdown using Skysat and Planetscope imagery

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