Precipitation Forecasting Using Wavelet-Genetic Programming and Wavelet-Neuro-Fuzzy Conjunction Models

Kisi, Özgür; Shiri, Jalal

doi:10.1007/s11269-011-9849-3

Cited by 152 publications

(48 citation statements)

References 43 publications

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“…In this paper, the maximum fitness was used as stopping condition of the developed GEP models. Many researchers [26] depend on suggested values by Ferreira [27], 30 chromosomes, 8 head sizes, and 3 genes were used for model structure.…”

Section: Gene Expression Programmingmentioning

confidence: 99%

A comparative Study of Data Mining Techniques for Estimating Suspended Sediment Load in River Flow

Al-Aboodi¹,

Ibrahim²,

Abbas³

2018

Eighth International Conference on Advances in Civil and Structural Engineering - CSE 2018

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Abstract-Estimating of Suspended Sediment Load (SSL) in rivers is extremely important for planning and managing of the water resource projects. Support Vector Machine (SVM) and Gene Expression Programming (GEP) techniques are used for estimating SSL in four kilometers along Tigris River, located in upstream Al-Amarah Barrage; Maysan Province; Southern Iraq. Twenty-sections are selected for the purpose of the field measurement of SSL, which include measurement of flow velocity. For applying main object of this research, measured river velocities at these sections are used as the input variables of data mining techniques and the model output is SSL at these river sections. Cross validation method is used to estimate the performance of the models results, a random set of rows is selected to each validation fold after stratifying on the target variable. Three statistical parameters (root mean square error, mean absolute error and coefficient of correlation) are used to evaluate the performance of models. The performances of SVM model are better than GEP model. Data mining techniques specifically (SVM and GEP) are efficient and powerful techniques for modeling suspended sediment load.

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Section: Gene Expression Programmingmentioning

confidence: 99%

A comparative Study of Data Mining Techniques for Estimating Suspended Sediment Load in River Flow

Al-Aboodi¹,

Ibrahim²,

Abbas³

2018

Eighth International Conference on Advances in Civil and Structural Engineering - CSE 2018

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“…This work gave boost to similar works by other researchers. Thus Liu et al (2011) used a wavelet based approach for assessing timing errors in hydrologic predictions, while Kisi and Shiri (2011) applied Wavelet-Genetic Programming and Wavelet-NeuroFuzzy Conjunction Models for forecasting precipitation at different lead time intervals. Okkan (2012) applied NWT for monthly reservoir inflow prediction at Kemer Dam, Turkey by eliminating ineffective sub-time series through regression methods and using effective sub-time series components as new inputs to ANN.…”

Section: Applications In Hydrologymentioning

confidence: 99%

Review of Applications of Neuro-Wavelet Techniques in Water Flows

Dixit

Londhe²,

Deo

2016

INAE Lett

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The flow of water over land and oceans is governed by the natural process of hydrological cycle and hence it is highly random and complex. An estimation or prediction of behavior of such flows is thus very difficult and requires continuous research with latest tools of analysis and forecasting. The methods adopted for this purpose range from earlier empirical equations and numerical methods to modern data driven techniques which follow advances in computer technology. The latter coupled with progress in neuro-science had given rise to the method of artificial neural networks (ANNs) which has been applied abundantly since late 1980s to carry out system modeling and prediction type of exercises in water resources and ocean engineering. Despite wide spread applications of ANNs issues such as non-linearity, non-homogenity and non-stationarity of given information continue to elude high accuracy in the results and thus have prompted extension of conventional ANNs to their combination with other pre-or post-processing tools. The wavelet neural network or neuro-wavelet transform (NWT) is a recent effort in this regard to accurately model or predict different types of parameters characterizing river or ocean processes. This paper presents a review of applications of NWT in hydrologic and Oceanic modeling.

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“…The study revealed that GP displayed a better edge over the other two modelling approaches in all the statistics compared like the mean absolute error (MAE), mean squared relative error (MSRE) and correlation coefficient. Kisi et al (2010) [5] developed a wavelet gene expression programming (WGEP) for forecasting daily precipitation and compared it with wavelet neuro-fuzzy models (WNF). The results showed that WGEP models are effective in forecasting daily precipitation with better performance over WNF models.…”

Section: Genetic Programming As a Modelling Toolmentioning

confidence: 99%

Genetic Programming: Efficient Modeling Tool in Hydrology and Groundwater Management

Janardhanan¹,

Datta²

2012

Genetic Programming - New Approaches and Successful Applications

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Precipitation Forecasting Using Wavelet-Genetic Programming and Wavelet-Neuro-Fuzzy Conjunction Models

Cited by 152 publications

References 43 publications

A comparative Study of Data Mining Techniques for Estimating Suspended Sediment Load in River Flow

A comparative Study of Data Mining Techniques for Estimating Suspended Sediment Load in River Flow

Review of Applications of Neuro-Wavelet Techniques in Water Flows

Genetic Programming: Efficient Modeling Tool in Hydrology and Groundwater Management

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