Forecasting of rainfall using ocean-atmospheric indices with a fuzzy neural technique

Srivastava, Gaurav; Panda, Sudhindra N.; Mondal, Pinki; Liu, Junguo

doi:10.1016/j.jhydrol.2010.10.025

Cited by 20 publications

(6 citation statements)

References 24 publications

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“…The ANN is a non-dynamic model that has often been used for time series prediction in hydrology and meteorology (e.g., Bishop, 1995;Olsson et al, 2001;Srivastava et al, 2006;Srivastava et al, 2010;Singh and Borah, 2013;Dash et al, 2010). The ANN is flexible to develop and can predict highly varying physical phenomena.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

The effects of ocean SST dipole on Mongolian summer rainfall

Yasuda

Nandintsetseg²,

Berndtsson

et al. 2017

Geofizika

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Cross-correlations between inter-annual summer rainfall time series (June to August: JJA) for arid Mongolia and global sea surface temperatures (GSST) were calculated for prediction purposes. Prediction of summer rainfall for four vegetation zones, Desert Steppe (DS), Steppe (ST), Forest Steppe (FS), and High Mountain (HM) using GSSTs for time lags of 5, 6, and 7 months prior to JJA rainfall was evaluated. Mongolian summer rainfall is correlated with global SSTs. In particular, the summer rainfall of FS and HM displayed high and statistically sigtime series of the SST differences between SST dipoles (positive -negative) with the summer rainfall time series was larger than the original correlations. To preused. Time series of the SST difference that represents the strength of the dipole were used as input to the ANN model, and Mongolian summer rainfall was predicted 5, 6, and 7 months ahead in time. The predicted summer rainfall compared reasonably well with the observed rainfall in the four different vegetation zones. This implies that the model can be used to predict summer rainfall for the four main Mongolian vegetation zones with good accuracy.

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Section: Artificial Neural Networkmentioning

confidence: 99%

The effects of ocean SST dipole on Mongolian summer rainfall

Yasuda

Nandintsetseg²,

Berndtsson

et al. 2017

Geofizika

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“…In the study, fuzzy logic was used to establish a relationship among the observed and measured nonlinear meteorological phenomena. Srivastava et al [32] aimed to forecast monthly rainfall in India by using Fuzzy-Ranking Algorithm (FRA) with some ocean-atmospheric predictor variables. Artificial Neural Network (ANN) technique was also applied for analyzing nonlinear relationship between inputs of the model and rainfall.…”

Section: Fuzzy Set Theory In Meteorologymentioning

confidence: 99%

Fuzzy Sets in Earth and Space Sciences

Otay

Kahraman

2016

Fuzzy Logic in Its 50th Year

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Earth science refers to the field of science dealing with planet Earth while space science pertains several scientific disciplines studying the upper atmosphere, space, and celestial bodies rather than Earth. The fuzzy set theory is one of the tools that has been recently used in the earth and space sciences. In this chapter, we review and analyze the papers utilizing fuzzy logic in earth and space science problems from Scopus database. The graphical and tabular illustrations are presented for the subject areas, publication years and sources of the papers on earth and space sciences.

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“…Recently, there are many researches focus on rainfall. These researches include statistical approaches, ensemble, Bayesian model, and data mining [3][4][5][6][7][8][9][10][11][12]. Above techniques provide useful information for the rainfall.…”

Section: Introductionmentioning

confidence: 99%

Apply data mining to analyze the rainfall of landslide

Lee

Peng

et al. 2018

MATEC Web Conf.

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Taiwan is listed as extremely dangerous country which suffers from many disasters. The disasters from the landslide result in the loss of agricultural productions, life and property and so on. Many researchers concern about the disasters of landslide, but there are few discussions for the threshold of rainfall for landslide. In this paper, data mining is applied to establish rules and the threshold of rainfall for landslide in Huafan University, Taiwan. These used variables include rainfall, insolation, insolation rate, averaged humidity, averaged temperature, wind speed, and the tilt of inclinometer. The inclinometer is an important instrument for measuring tilt, elevation or depression of an object with respect to gravity. There are 26 inclinometers in Talun mountain area of Huafan University. In this research, the used data were collected from January 2008 to July 2014. In the proposed approach, the regression analysis is used to predict rainfall first. Then, decision tree is used to obtain decision rules and set the threshold of rainfall for landslide. The output of this approach can provide more information for understanding the change of rainfall. The threshold of rainfall could also provide useful information to maintain the security for Huafan University.

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Forecasting of rainfall using ocean-atmospheric indices with a fuzzy neural technique

Cited by 20 publications

References 24 publications

The effects of ocean SST dipole on Mongolian summer rainfall

The effects of ocean SST dipole on Mongolian summer rainfall

Fuzzy Sets in Earth and Space Sciences

Apply data mining to analyze the rainfall of landslide

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