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

Chattopadhyay, Manojit; Chattopadhyay, Surajit

doi:10.1007/s00704-015-1544-5

Cited by 12 publications

(3 citation statements)

References 68 publications

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“…Logistic regression was chosen and implemented on GIS system for quantitative prediction of rainfall and landslide in the study area [30]. SVM have been proved to impact positively the prediction of the Indian summer monsoon rainfall [31]. Also, hybrid models combining random forest (RF) and SVM have been used to predict amount of rainfall in rainfall occurring days [32].…”

Section: Learning: Rainfall Probability Estimationmentioning

confidence: 99%

“…data. Support vector machines (SVM) [31] outperform logistic regression in binary classification, especially with growing training dataset [32]. When variables are mapped to higher dimensional space through divergent SVM kernels, nonlinear classification can be achieved by identifying maximum margin hyper plane between two sides [18,19].…”

Section: Estimated Ltr Probabilitymentioning

confidence: 99%

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Raspberry Pi Based Intelligent Wireless Sensor Node for Localized Torrential Rain Monitoring

Fang

et al. 2016

Journal of Sensors

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Wireless sensor networks are proved to be effective in long-time localized torrential rain monitoring. However, the existing widely used architecture of wireless sensor networks for rain monitoring relies on network transportation and back-end calculation, which causes delay in response to heavy rain in localized areas. Our work improves the architecture by applying logistic regression and support vector machine classification to an intelligent wireless sensor node which is created by Raspberry Pi. The sensor nodes in front-end not only obtain data from sensors, but also can analyze the probabilities of upcoming heavy rain independently and give early warnings to local clients in time. When the sensor nodes send the probability to back-end server, the burdens of network transport are released. We demonstrate by simulation results that our sensor system architecture has potentiality to increase the local response to heavy rain. The monitoring capacity is also raised.

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Section: Learning: Rainfall Probability Estimationmentioning

confidence: 99%

Section: Estimated Ltr Probabilitymentioning

confidence: 99%

Raspberry Pi Based Intelligent Wireless Sensor Node for Localized Torrential Rain Monitoring

Fang

et al. 2016

Journal of Sensors

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“…ANN outperformed established statistical benchmarks in 73% of the papers reviewed (Adya & Collopy, 1998). A recent study has revealed that about 12 different types of neural networks were used for Indian summer monsoon rainfall (ISMR) prediction since 2003 to 2014 and among these ANN accounts for 48% ISMR prediction approaches (Chattopadhyay & Chattopadhyay, 2016). The feed-forward neural network with backpropagation learning has been used for forecasting of ISMR.…”

Section: Introductionmentioning

confidence: 99%

Predictability assessment of northeast monsoon rainfall in India using sea surface temperature anomaly through statistical and machine learning techniques

2018

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The socio‐economic growth of India is adversely affected by the abnormal meteorological phenomena of floods and droughts. Thus, rainfall prediction is highly desirable for livelihood and sustainability. In this study, northeast monsoon rainfall (NEMR) is predicted over the Indian peninsular region for the months of October, November, and December using a global sea surface temperature (SST) anomaly as a predictor by linear regression (LR), artificial neural network (ANN), and extreme learning machine (ELM) techniques. The predictions are made by LR, ANN, and ELM models for the period 1990–2016 using the training input (1871–1989) of different time series samples of global SST anomaly data collected from Hadley Centre SST data set (HadSST3). Principal component analysis was used for dimensionality reduction of the data sets, and its application substantially improved the predictive ability of the machine learning techniques. The performance of the PC‐ELM technique with the ensemble method (ESM 8–9) training window is found to be more accurate than the LR and ANN techniques and provides minimal error scores as per the statistical analysis. This study concludes that the global SST anomaly has the potential to be used as a predictor for northeast monsoon rainfall and useful for long‐range climatic projections.

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Potential Application of Advanced Computational Techniques in Prediction of Groundwater Resource of India

Malakar

Mukherjee

Sarkar

2018

Springer Hydrogeology

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

Cited by 12 publications

References 68 publications

Raspberry Pi Based Intelligent Wireless Sensor Node for Localized Torrential Rain Monitoring

Raspberry Pi Based Intelligent Wireless Sensor Node for Localized Torrential Rain Monitoring

Predictability assessment of northeast monsoon rainfall in India using sea surface temperature anomaly through statistical and machine learning techniques

Potential Application of Advanced Computational Techniques in Prediction of Groundwater Resource of India

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