Dynamic coupling of support vector machine and K-nearest neighbour for downscaling daily rainfall

Devak, Manjula; Dhanya, C. T.; Gosain, A. K.

doi:10.1016/j.jhydrol.2015.03.051

Cited by 41 publications

(11 citation statements)

References 45 publications

(50 reference statements)

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“…Also, the EEMD-SVM is more accurate in predicting the LTSF monthly. This is because SVM has been incredibly robust and efficient in nonlinear noise mixed data (Devak et al, 2015). Besides, the potential of decomposition might be more prominent in predicting the TSF dataset in the EEMD-ANN or EEMD-SVM model than the standalone model because the hybrid model can overcome the shortcomings of the standalone model to produce a synergetic impact on prediction.…”

Section: Discussionmentioning

confidence: 99%

Development of novel hybrid machine learning models for monthly thunderstorm frequency prediction over Bangladesh

et al. 2021

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Accurate thunderstorm frequency (TSF) prediction is of great significance under climate extremes for reducing potential damages. However, TSF prediction has received little attention because a thunderstorm event is a combination of intricate and unique weather scenarios with high instability, making it difficult to predict. To close this gap, we proposed a novel hybrid machine learning model through hybridization of data pre-processing Ensemble Empirical Mode Decomposition (EEMD) with two state-of-arts models namely artificial neural network (EEMD-ANN), support vector machine (EEMD-SVM) for TSF prediction at three categories of yearly frequencies over Bangladesh. We were demarcated the yearly TSF datasets into three categories for the period 1981-2016 recorded at 28 sites; high (March-June), moderate (July-October), and low (November-February) TSF months. The performance of the proposed EEMD-ANN and EEMD-SVM hybrid models was compared 2 with classical ANN, SVM, Autoregressive Integrated Moving Average (ARIMA). EEMD-ANN and EEMD-SVM hybrid models showed 8.02%-22.48% higher performance precision in terms of root mean square error (RMSE) compared to other models at high, moderate and low-frequency categories. Eleven out of 21 input parameters were selected based on the Random Forest (RF) variable importance analysis. The sensitivity analysis results showed that each input parameter was positively contributed to building the best model of each category and thunderstorm days are the most contributing parameters influencing TSF prediction. The proposed hybrid models outperformed the conventional models where EEMD-ANN is the most skillful for high TSF prediction, and EEMD-SVM is for moderate and low TSF prediction. The findings indicate the potential of hybridization of EEMD with the conventional models for improving prediction precision. The hybrid model developed in this work can be adopted for TSF prediction in Bangladesh as well as different parts of the world.

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Section: Discussionmentioning

confidence: 99%

Development of novel hybrid machine learning models for monthly thunderstorm frequency prediction over Bangladesh

et al. 2021

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“…These are NB, C4.5, SVM, ANN, and RF, which are all supervised learning methods. A notable aspect of the supervised machine learning methods is that they select suitable methods together with parameters and features that are deemed suitable [21][22][23][24][25]. Two main experiments were carried out in order to evaluate the performance of the classifiers.…”

Section: Machine Learning Modelsmentioning

confidence: 99%

“…Rainfall forecasting gained the attention of many researchers' as it has interesting challenges represented by the complexity that lies beneath predicting specific factors that are linked to rainfall like wind and humidity [21][22][23], [35][36]. Current techniques for rainfall prediction utilize only individual classifiers such as neural networks [21], the knearest neighbours [22], support vector machine [22] and others. Based on encouraging results from the ensemble methods application in various fields, the ensemble classification technique is applied to rainfall prediction by leveraging three linear algebraic combiners: majority voting, average probability, and maximum probability.…”

Section: ) Statistical Reasoningmentioning

confidence: 99%

Ensemble Learning for Rainfall Prediction

Sani¹,

Rahman²,

Adam³

et al. 2020

IJACSA

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Climate change research is a discipline that analyses the varying weather patterns for a particular period of time. Rainfall forecasting is the task of predicting particular future rainfall amount based on the measured information from the past, including wind, humidity, temperature, and so on. Rainfall forecasting has recently been the subject of several machine learning (ML) techniques with differing degrees of both short-term and also long-term prediction performance. Although several ML methods have been suggested to improve rainfall forecasting, the task of appropriate selection of technique for specific rainfall durations is still not clearly defined. Therefore, this study proposes an ensemble learning to uplift the effectiveness of rainfall prediction. Ensemble learning as an approach that combines multiple ML multiple rainfall prediction classifiers, which include Naïve Bayes, Decision Tree, Support Vector Machine, Random Forest and Neural Network based on Malaysian data. More specifically, this study explores three algebraic combiners: average probability, maximum probability, and majority voting. An analysis of our results shows that the fused ML classifiers based on majority voting are particularly effective in boosting the performance of rainfall prediction compared to individual classification.

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“…e results suggested that the simulation was useful in creating weather, while the efficiency of generating immense precipitation occurrences was reduced compared to the temperature and evaporation parameters. In another study [34], a vibrant model to reduce day-to-day precipitation in the Indian Mahanadi Basin was developed. e model was constructed based on integrating the K-nearest neighbour (KNN) and SVM model.…”

Section: Projections Of Climate Changementioning

confidence: 99%

The Practical Influence of Climate Change on the Performance of Road Stormwater Drainage Infrastructure

Al-Ghadi

Mohtar

Razali

et al. 2020

Journal of Engineering

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The atmospheric temperature globally has risen during the last few centuries, causing global warming and climate dynamics. The impact of global warming has caused environmental effects that include increased rainfall intensity, which is recognised as the leading cause of flooding, and destruction and devastation to the surrounding environment, infrastructure, and human life. Road stormwater drainage systems are used for removing and controlling excess runoff water to the right way. However, regardless of enhanced technologies, the reliability of drainage system schemes remains a major undertaking for water and hydraulic engineers alike. In this study, a thorough evaluation of the methods employed in previous studies and research on rainfall forecasting models was undertaken. Further to that, the effect resulting from climate change on structures of drainage infrastructure is considered in addition to the methods to enhance drainage system challenges with recent models developed for advanced drainage and waterway requirements that need to be designed in consideration of climate change.

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Dynamic coupling of support vector machine and K-nearest neighbour for downscaling daily rainfall

Cited by 41 publications

References 45 publications

Development of novel hybrid machine learning models for monthly thunderstorm frequency prediction over Bangladesh

Development of novel hybrid machine learning models for monthly thunderstorm frequency prediction over Bangladesh

Ensemble Learning for Rainfall Prediction

The Practical Influence of Climate Change on the Performance of Road Stormwater Drainage Infrastructure

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