Prediction of extreme rainfall event using weather pattern recognition and support vector machine classifier

Nayak, Munir Ahmad; Ghosh, Subimal

doi:10.1007/s00704-013-0867-3

Cited by 81 publications

(43 citation statements)

References 33 publications

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“…The ANN and M5 model tree performed similar in terms of accuracy. Reference [166] tested four DT models, i.e., Nayak and Ghosh [120] used SVM and ANN to predict hourly rainfall-runoff using weather patterns. A model of SVM classifier for rainfall prediction was used and the results were compared to ANN and another advanced statistical technique.…”

Section: Short-term Flood Prediction Using Single ML Methodsmentioning

confidence: 99%

“…Thus, they were applied in numerous flood prediction cases with promising results, excellent generalization ability, and better performance, compared to ANNs and MLRs, e.g., extreme rainfall [120], precipitation [43], rainfall-runoff [121], reservoir inflow [122], streamflow [123], flood quantiles [48], flood time series [124], and soil moisture [125].…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

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Flood Prediction Using Machine Learning Models: Literature Review

Mosavi

Öztürk

Chau

2018

Water

976

469

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Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models contributed to risk reduction, policy suggestion, minimization of the loss of human life, and reduction the property damage associated with floods. To mimic the complex mathematical expressions of physical processes of floods, during the past two decades, machine learning (ML) methods contributed highly in the advancement of prediction systems providing better performance and cost-effective solutions. Due to the vast benefits and potential of ML, its popularity dramatically increased among hydrologists.Researchers through introducing novel ML methods and hybridizing of the existing ones aim at discovering more accurate and efficient prediction models. The main contribution of this paper is to demonstrate the state of the art of ML models in flood prediction and to give insight into the most suitable models. In this paper, the literature where ML models were benchmarked through a qualitative analysis of robustness, accuracy, effectiveness, and speed are particularly investigated to provide an extensive overview on the various ML algorithms used in the field. The performance comparison of ML models presents an in-depth understanding of the different techniques within the framework of a comprehensive evaluation and discussion. As a result, this paper introduces the most promising prediction methods for both long-term and short-term floods. Furthermore, the major trends in improving the quality of the flood prediction models are investigated. Among them, hybridization, data decomposition, algorithm ensemble, and model optimization are reported as the most effective strategies for the improvement of ML methods. This survey can be used as a guideline for hydrologists as well as climate scientists in choosing the proper ML method according to the prediction task. Keywords: flood prediction; flood forecasting; flash-flood model, big flood management; hydrologic model; rainfall-runoff, hybrid & ensemble machine learning; artificial neural networks (ANNs); support vector machines (SVM); natural hazards & disasters; adaptive neuro-fuzzy inference system (ANFIS); decision trees (DT); internet of things (IoT); random forest (RF); survey; classification and regression trees (CART), data science; deep learning; big data; bagging, boosting, artificial intelligence (AI); soft computing; extreme event management; time series prediction; multilayer perceptron (MLP); simulated annealing (SA); multivariate adaptive regression splines (MARS), supervised learning IntroductionAmong the natural disasters, floods are the most destructive, causing massive damage to human life, infrastructure, agriculture, and the socioeconomic system.Governments, therefore, are under pressure to develop reliable and accurate maps of flood risk areas and further plan for sustainable flood risk management focusing on prevention, protection, and preparedness [1]. Flood prediction models are of significant impor...

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Section: Short-term Flood Prediction Using Single ML Methodsmentioning

confidence: 99%

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Flood Prediction Using Machine Learning Models: Literature Review

Mosavi

Öztürk

Chau

2018

Water

976

469

View full text Add to dashboard Cite

show abstract

“…Apart from applying hybrid approach, the potential improvement in prediction can be possible if a combination of models can be used to exploit the full potential of the SVM Shrestha and Shukla, 2015;Tehrany et al, 2014;Nayak and Ghosh, 2013;Ballabio and Sterlacchini, 2012;Zhang et al, 2010;Ghosh, 2010;Misra et al, 2009;Behzad et al, 2009;Sreelakshmi et al, 2008;Tripathi et al, 2006;Gill et al, 2006 SVM and in combination with other methods approach in prediction. Thus, the novelty of the present work is to identify the similar clusters of years based on related family of monthly rainfall on a topology-preserving map using an unsupervised growing hierarchical self-organising map.…”

Section: Introductionmentioning

confidence: 98%

Elucidating the role of topological pattern discovery and support vector machine in generating predictive models for Indian summer monsoon rainfall

Chattopadhyay

Chattopadhyay²

2015

Theor Appl Climatol

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The present paper reports a study, where growing hierarchical self-organising map (GHSOM) has been applied to achieve a visual cluster analysis to the Indian rainfall dataset consisting of 142 years of Indian rainfall data so that the yearly rainfall can be segregated into small groups to visualise the pattern of clustering behaviour of yearly rainfall due to changes in monthly rainfall for each year. Also, through support vector machine (SVM), it has been observed that generation of clusters impacts positively on the prediction of the Indian summer monsoon rainfall. Results have been presented through statistical and graphical analyses.

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“…Time series data mining is one of the hot research topics in the domain of knowledge discovery [19]. The data with time series approach is collected over a specific period of time such as daily, weekly, monthly, quarterly or yearly [13].…”

Section: Introductionmentioning

confidence: 99%

Rainfall Prediction in Lahore City using Data Mining Techniques

Aftab¹,

Ahmad²,

Hameed³

et al. 2018

ijacsa

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Abstract-Rainfall prediction has extreme significance in countless aspects and scopes. It can be very helpful to reduce the effects of sudden and extreme rainfall by taking effective security measures in advance. Due to climate variations, an accurate rainfall prediction has become more complex than before. Data mining techniques can predict the rainfall through extracting the hidden patterns among weather attributes of past data. This research contributes by exploring the use of various data mining techniques for rainfall prediction in Lahore city. Techniques include: Support Vector Machine (SVM), Naïve Bayes (NB), k Nearest Neighbor (kNN), Decision Tree (J48) and Multilayer Perceptron (MLP). The dataset is obtained from a weather forecasting website and consists of several atmospheric attributes. For effective prediction, pre-processing technique is used which consists of cleaning and normalization processes. Performance of used data mining techniques is analyzed in terms of precision, recall and f-measure with various ratios of training and test data.

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Prediction of extreme rainfall event using weather pattern recognition and support vector machine classifier

Cited by 81 publications

References 33 publications

Flood Prediction Using Machine Learning Models: Literature Review

Flood Prediction Using Machine Learning Models: Literature Review

Elucidating the role of topological pattern discovery and support vector machine in generating predictive models for Indian summer monsoon rainfall

Rainfall Prediction in Lahore City using Data Mining Techniques

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