Imputation of missing daily rainfall data; A comparison between artificial intelligence and statistical techniques

Wangwongchai, Angkool; Waqas, Muhammad; Dechpichai, Porntip; Hlaing, Phyo Thandar; Ahmad, Shakeel; Humphries, Usa Wannasingha

doi:10.1016/j.mex.2023.102459

Cited by 15 publications

(1 citation statement)

References 29 publications

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“…Support Vector Machine (SVM) is widely acknowledged for its application in automated classification, lending itself to predictive. Polynomial Regression, on the other hand, represents an extension of Linear Regression, offering predictive capabilities tailored to address non-linear data patterns (Mordensky et al, 2023;Murugan et al, 2023;Wangwongchai et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Prediction Of Cryptocurrency Prices Using Lstm, Svm And Polynomial Regression

Al Giffary,

Sulianta

2024

AJESH

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The rapid development of information technology, especially the Internet, has facilitated users with a quick and easy way to seek information. With these convenience offered by internet services, many individuals who initially invested in gold and precious metals are now shifting into digital investments in form of cryptocurrencies. However, investments in crypto coins are filled with uncertainties and fluctuation in daily basis. This risk posed as significant challenges for coin investors that could result in substantial investment losses. The uncertainty of the value of these crypto coins is a critical issue in the field of coin investment. Forecasting, is one of the methods used to predict the future value of these crypto coins. By utilizing the models of Long Short Term Memory, Support Vector Machine, and Polynomial Regression algorithm for forecasting, a performance comparison is conducted to determine which algorithm model is most suitable for predicting crypto currency prices. The mean square error is employed as a benchmark for the comparison. By applying those three constructed algorithm models, the Support Vector Machine uses a linear kernel to produce the smallest mean square error compared to the Long Short Term Memory and Polynomial Regression algorithm models, with a mean square error value of 0.02.

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Section: Introductionmentioning

confidence: 99%

Prediction Of Cryptocurrency Prices Using Lstm, Svm And Polynomial Regression

Al Giffary,

Sulianta

2024

AJESH

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Assessment of Different Methods for Estimation of Missing Rainfall Data

Hırca,

Eryılmaz Türkkan

2024

Water Resour Manage

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Missing data is a common problem encountered in various fields, including clinical research, environmental sciences and hydrology. In order to obtain reliable results from the analysis, the data inventory must be completed. This paper presents a methodology for addressing the missing data problem by examining the missing data structure and missing data techniques. Simulated datasets were created by considering the number of missing data, missing data pattern and missing data mechanism of real datasets containing missing values, which are often overlooked in hydrology. Considering the missing data pattern, the most commonly used methods for missing data analysis in hydrology and other fields were applied to the created simulated datasets. Simple imputation techniques and expectation maximization (EM) were implemented in SPSS software and machine learning techniques such as k-nearest neighbor (kNN), together with the hot-deck were implemented in the Python programming language. In the performance evaluation based on error metrics, it is concluded that the EM method is the most suitable completion method. Homogeneity analyses were performed in the Mathematica programming language to identify possible changes and inconsistencies in the completed rainfall dataset. Homogeneity analyses revealed that most of the completed rainfall datasets are homogeneous at class 1 level, consistent and reliable and do not show systematic changes in time.

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Spatio-temporal rainfall variability and trends using a Kriging-interpolation and Innovative trend analysis approach: the case of Wolaita zone, south Ethiopia

Bojago

2024

Discov Sustain

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Imputation of missing daily rainfall data; A comparison between artificial intelligence and statistical techniques

Cited by 15 publications

References 29 publications

Prediction Of Cryptocurrency Prices Using Lstm, Svm And Polynomial Regression

Prediction Of Cryptocurrency Prices Using Lstm, Svm And Polynomial Regression

Assessment of Different Methods for Estimation of Missing Rainfall Data

Spatio-temporal rainfall variability and trends using a Kriging-interpolation and Innovative trend analysis approach: the case of Wolaita zone, south Ethiopia

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