Visualization of rainfall data using functional data analysis

Hael, Mohanned Abduljabbar; Yong-sheng, Yuan; Saleh, Bassiouny

doi:10.1007/s42452-020-2238-x

Cited by 9 publications

(5 citation statements)

References 35 publications

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“…Beyaztas & Yaseen (2019) proposed a FTS model to construct a reliable predictive strategy of drought interval occurrences. Hael et al (2020) used FTS model to provide a clear understanding of the rainfall patterns and predict future rainfall curves. The numerical results produced by the studies mentioned above have shown that the FDA-based models perform better than Box-Jenkins and AI-based models in forecasting hydrological timeseries data.…”

Section: Research Motivation and Enthusiasmmentioning

confidence: 99%

A functional autoregressive model based on exogenous hydrometeorological variables for river flow prediction

Beyaztaş¹,

Shang²,

Yaseen³

2021

Preprint

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In this research, a functional time series model was introduced to predict future realizations of river flow time series. The proposed model was constructed based on a functional time series's correlated lags and the essential exogenous climate variables. Rainfall, temperature, and evaporation variables were hypothesized to have substantial functionality in river flow simulation. Because an actual time series model is unspecified and the input variables' significance for the learning process is unknown in practice, it was employed a variable selection procedure to determine only the significant variables for the model. A nonparametric bootstrap model was also proposed to investigate predictions' uncertainty and construct pointwise prediction intervals for the river flow curve time series. Historical datasets at three meteorological stations (Mosul, Baghdad, and Kut) located in the semi-arid region, Iraq, were used for model development. The prediction performance of the proposed model was validated against existing functional and traditional time series models. The numerical analyses revealed that the proposed model provides competitive or even better performance than the benchmark models. Also, the incorporated exogenous climate variables have substantially improved the modeling predictability performance. Overall, the proposed model indicated a reliable methodology for modeling river flow within the semi-arid region.

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Section: Research Motivation and Enthusiasmmentioning

confidence: 99%

A functional autoregressive model based on exogenous hydrometeorological variables for river flow prediction

Beyaztaş¹,

Shang²,

Yaseen³

2021

Preprint

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“…Simulation of drought interval and drought changes were analysed by [29,30]. [31][32][33] studied rainfall variability modeling, pattern identification, and outlier detection. Other relevant studies include work of [34][35][36][37][38], are also preferred for acquiring information about the useful application of AFPC in hydrology.…”

Section: Literature Reviewmentioning

confidence: 99%

Outlier Detection using Nonparametric Depth-Based Techniques in Hydrology

Hussain¹

2020

IJACSA

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Several issues arise when extending the methods of outlier detection from a single dimension to a higher dimension. These issues include limited methods for visualization, marginal methods inadequacy, lacking a natural order and limitation in parametric modeling. The intension to overcome and address such limitations the nonparametric outlier identifier, based on depth functions, is introduced. These identifiers comprise of four threshold type outlyingness functions for outlier detection that are Mahalanobis distance, Tukey depth, spatial Mahalanobis depth, and projection depth. The object of the present research is the application of the proposed nonparametric technique in hydrology. The study is intended to be executed in two different frameworks that are multivariate hydrological data analysis and functional hydrological data analysis. The event of a flood is graphically represented by hydrograph whose components are used for computing flood characteristics that are peak(p) and volume(v). These characteristics are frequently employed for the various types of analysis in the multivariate study. Whereas, hydrograph is exhaustively employed in the analysis of functional data so that all the important information regarding flood event are not missed while analysis. The proposed technique in a multivariate framework is applied to the bivariate flood characteristics while in functional framework proposed approach is applied to the initial two scores of principal components denoted as , since initial two principal components capture major variation of data employed for analysis.

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“…In the context of climate change studies, the estimation of the quantity of precipitation is a task currently performed by applying multivariate regression techniques [29] , [30] . Alternatively, the FDA techniques have been used to study the rainfall from descriptive and classification point of view [31] , [32] . We would like also to highlight the use of techniques that take into account the temporal and spatial dependence of the observations, such as kriging, both from the traditional approach [29] , [31] and also using the new functional approach [33] .…”

Section: Introductionmentioning

confidence: 99%

Predicting rainfall and irrigation requirements of corn in Ecuador

Flores,

Llambo,

Loza

et al. 2023

Heliyon

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Visualization of rainfall data using functional data analysis

Cited by 9 publications

References 35 publications

A functional autoregressive model based on exogenous hydrometeorological variables for river flow prediction

A functional autoregressive model based on exogenous hydrometeorological variables for river flow prediction

Outlier Detection using Nonparametric Depth-Based Techniques in Hydrology

Predicting rainfall and irrigation requirements of corn in Ecuador

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