A hybrid Forecasting Model of Discharges based on Support Vector Machine

Li, Shijin; Jiang, Lingling; Zhu, Yuemin; Bo, Ping

doi:10.1016/j.proeng.2012.01.695

Cited by 4 publications

(3 citation statements)

References 5 publications

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“…It is a decision-making tool or planning tool used to help the management or many businesses in its effort to handle the uncertainties of the future, which relies mainly on data obtained from the past and present and then carry out analysis of the trends. Shijin et al (3) described forecasting as a vital study field in evaluating the hydrological data series. Raicharoen (4) stated that time series forecasting is an act of knowing the future when the past is understood.…”

Section: A T E R I a L S A N D M E T H O D Smentioning

confidence: 99%

Forecasting Drought With Arima Model and Standardized Precipitation Index (Spi)

Salisu¹

2019

Sci Prcd Ser

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Drought forecasting is an important forecasting procedure for preparing and managing water resources for all creatures. Natural disasters across the regions such as flooding, earthquakes, droughts etc. have caused damages to life as a result of which numerous researches have been conducted to assist in reducing the phenomenon. Consequently, therefore, this study considered using Auto-Regressive Integrated Moving Average (ARIMA) model in forecasting drought using Standardized Precipitation Index (SPI) as a forecasting tool which was used to measure and classify drought. The models are developed to forecast the SPI series. Results indicated the forecasting ability of the ARIMA models which increases as the timescales. The study is aimed at using ARIMA method for modeling SPI data series. The studies used data set made up of 624 months, obtained from 1954 to 2008. In the analysis only SPI3 series was non-seasonal while others have seasonality and Seasonal ARIMA was carried out, SPI12 was significant compared with the forecasting accuracy alongside the diagnostic checking having a minimum error of RMSE and MAE in both testing and training phases. The research contributes to the discovering of feasible forecasting of drought and demonstrates that the established model is good and appropriate for forecasting drought.

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Section: A T E R I a L S A N D M E T H O D Smentioning

confidence: 99%

Forecasting Drought With Arima Model and Standardized Precipitation Index (Spi)

Salisu¹

2019

Sci Prcd Ser

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“…Runoff forecasting is a classical hydrological problem falls under the Hydrological time series analysis [36]. A new framework for analyzing time series data called Time Series Data Mining (TSDM) is introduced in this study.…”

Section: Time Series Data Miningmentioning

confidence: 99%

“…Clustering is among one of the unsupervised learning methods. Its main goal is to identify structure in an unlabeled data set by objectively organizing data into homogeneous groups where the within-group-object similarity is minimized and the between-group-object dissimilarity is maximized [36]. Clustering is the process of grouping the data into classes or clusters so that objects within a cluster have high similarity in comparison to one another, but are very dissimilar to objects in other clusters.…”

Section: Cluster Analysismentioning

confidence: 99%

Discovering Flood Recession Pattern in Hydrological Time Series Data Mining during the Post Monsoon Period

Mishra¹,

Chandran²,

Dwivedi³

et al. 2014

IJCA

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This paper examines the flood recession pattern for the river discharge data in the river Brahmaputra basin. The months from October to December comes under the post monsoon season. In this paper, with the help of time series data mining techniques, the analysis has made for hydrological daily discharge time series data, measured at the Panchratna station during the post monsoon in the river Brahmaputra under Brahmaputra and Barak Basin Organization after the high flood. Statistical analysis has made for standardization of data. K-means clustering, Dynamic Time Warping(DTW), Agglomerative Hierarchical Clustering(AHC) and Ward's criterion are used to cluster and discover the discharge patterns in terms of the autoregressive model. A forecast model has been developed for the discharge process. For validation of the recession pattern, Gauge-Discharge Curve, Water Label Hydrographs, Rainfall Bar Graphs have been developed and also discharge recession coefficient has been calculated. This study gives the behavioral characteristics of rivers discharge during recession of high floods with the time series data mining.

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A Comparison of Machine Learning Techniques for Modeling River Flow Time Series: The Case of Upper Cauvery River Basin

Patel

Ramachandran

2014

Water Resour Manage

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A hybrid Forecasting Model of Discharges based on Support Vector Machine

Cited by 4 publications

References 5 publications

Forecasting Drought With Arima Model and Standardized Precipitation Index (Spi)

Forecasting Drought With Arima Model and Standardized Precipitation Index (Spi)

Discovering Flood Recession Pattern in Hydrological Time Series Data Mining during the Post Monsoon Period

A Comparison of Machine Learning Techniques for Modeling River Flow Time Series: The Case of Upper Cauvery River Basin

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