Downscaling of Precipitation in Mahanadi Basin, India Using Support Vector Machine, K-Nearest Neighbour and Hybrid of Support Vector Machine with K-Nearest Neighbour

Devak, Manjula; Dhanya, C. T.

doi:10.1007/978-3-319-18663-4_100

Cited by 6 publications

(3 citation statements)

References 6 publications

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“…As part of the downscaling process, the mutual information (MI) was compared with linear CC to pick dominant predictors [26]. Devak and Dhanya examined the effectiveness of Principal Component Analysis (PCA) as a dimension reduction before the statistical downscaling of CMs [27].…”

Section: Introductionmentioning

confidence: 99%

Application of Wavelet Transform for Bias Correction and Predictor Screening of Climate Data

Hosseini Baghanam,

Nourani,

Norouzi

et al. 2023

Sustainability

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Climate model (CM) statistical downscaling requires quality and quantity modifications of the CM’s outputs to increase further modeling accuracy. In this respect, multi-resolution wavelet transform (WT) was employed to determine the hidden resolutions of climate signals and eliminate bias in a CM. The results revealed that the newly developed discrete wavelet transform (DWT)-based bias correction method can outperform the quantile mapping (QM) method. In this study, wavelet coherence analysis was utilized to assess the high common powers and the multi-scale correlation between the predictors and predictand as a function of time and frequency. Thereafter, to rate the most contributing predictors based on potential periodicity, the average variance was calculated, which is named the Scaled Average (SA) measure. Consequently, WT along with Artificial Neural Network (ANN) were applied for bias correction and identifying the dominant predictors for statistical downscaling. The CAN-ESM5 data of Canadian climate models and INM-CM5 data of Russian climate models over two climatic areas of Iran with semi-arid (Tabriz) and humid (Rasht) weather were applied. The projection of future precipitation revealed that Tabriz will experience a 3.4–6.1% decrease in precipitation, while Rasht’s precipitation will decrease by 1.5–2.5%. These findings underscore the importance of refining CM data and employing advanced techniques to assess the potential impacts of climate change on regional precipitation patterns.

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Section: Introductionmentioning

confidence: 99%

Application of Wavelet Transform for Bias Correction and Predictor Screening of Climate Data

Hosseini Baghanam,

Nourani,

Norouzi

et al. 2023

Sustainability

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show abstract

“…Okkan (2015) utilized mutual information (MI) and correlation method to select the potential predictors to downscale GCMs data. In a joint study, Devak & Dhanya (2016) investigated the performance of the principal component analysis (PCA) to decline the dimensionality of GCM variables. The correlation-based analysis is a conventional method to screen and derive the features of the large datasets.…”

Section: Introductionmentioning

confidence: 99%

Wavelet-based predictor screening for statistical downscaling of precipitation and temperature using the artificial neural network method

2022

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One of the challenging issues in statistical downscaling of Climate Models is to select dominant large-scale climate variables (predictors). Correlation-based methods have been revealed to be efficacious to select the predictors; however, traditional correlation analysis has shown limited ability due to the nonstationary and nonlinear nature of climatic time series. Hence, in this study, Wavelet Coherence Transform (WTC) was employed to assess the high common powers and the multi-scale correlation between two time series (i.e., predictand and predictor) as a function of time and frequency. To this end, a coefficient correlation (CC) and a wavelet-based method were used for predictor screening and the results were compared in statistical downscaling. To apply the wavelet-based method, Continuous Wavelet Transform (CWT) was utilized to identify the potent periodicity in the time series of predictands. WTC was applied to determine the coherence between predictors and predictands in the potent periodicities, and Scale Average (SA) wavelet coherency was applied to rank them. In order to implement statistical downscaling, the ANN model was developed. In this study, three climate models including BNU-ESM Can-ESM5, and INM-CM5 have been used. The projection of the future climate based on the ANN downscaling revealed that precipitation will undergo a 7.1–28.92% downward trend, while the temperature will experience a 2.25–4.21 °C increase.

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“…The relationships between catchment-scale hydroclimatic variables (predictands) and large-scale atmospheric information (predictors) are often highly non-linear. Machine learning techniques have been proven effective in capturing highly non-linear relationships between predictors and predictands (Sachindra et al 2013;Devak et al 2015). However, most of the machine learning techniques suffer from the drawback of being black-box in nature, where the relationships between predictors and predictands and the underlying processes remain hidden (Sehgal et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Machine learning for downscaling: the use of parallel multiple populations in genetic programming

Sachindra

Kanae

2019

Stoch Environ Res Risk Assess

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In the implementation of traditional GP algorithm as models are evolved in a single deme (an environment in which a population of models is evolved) it may tend to produce sub-optimal models with poor generalisation skills due to lack of model diversity. As a solution to above issue, in this study the potential of evolving models in parallel multiple demes with different genetic attributes (parallel heterogeneous environments) and subsequent further evolution of some of the fittest models selected from each deme in another deme called the master deme was investigated, in relation to downscaling of large-scale climate data to daily minimum temperature (T min) and daily maximum temperature (T max). It was discovered that independent of the climate regime (i.e. warm or cold) and the geographic location of the observation station, a fraction of the fittest models (e.g. 25%) obtained from the last generation of each deme alone are sufficient for the formulation of a diverse initial population of models for the master deme. Also, independent of the climate regime and the geographic location of the observation station, both daily T min and T max downscaling models developed with the parallel multipopulation genetic programming (PMPGP) algorithm showed better generalisation skills compared to that of models developed with the traditional single deme GP, even when the amount of redundant information in the data of predictors was high. The models developed for daily T min and T max with the PMPGP algorithm simulated fewer unphysically large outliers compared to that of models developed with the GP algorithm.

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Downscaling of Precipitation in Mahanadi Basin, India Using Support Vector Machine, K-Nearest Neighbour and Hybrid of Support Vector Machine with K-Nearest Neighbour

Cited by 6 publications

References 6 publications

Application of Wavelet Transform for Bias Correction and Predictor Screening of Climate Data

Application of Wavelet Transform for Bias Correction and Predictor Screening of Climate Data

Wavelet-based predictor screening for statistical downscaling of precipitation and temperature using the artificial neural network method

Machine learning for downscaling: the use of parallel multiple populations in genetic programming

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