Comparative study of neural network, fuzzy logic and linear transfer function techniques in daily rainfall‐runoff modelling under different input domains

Lohani, A. K.; Goel, N. K.; Bhatia, Karamjit

doi:10.1002/hyp.7831

Cited by 71 publications

(34 citation statements)

References 20 publications

(4 reference statements)

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“…The results showed that ARMA and K-Nearest-Neighbors (KNN) performed prediction better than ANN and its variants when the correlation between input and output was low. Lohani [11] compared ANN, FIS and linear transfer model for daily rainfall-runoff model under different input domains. The results showed that FIS outperformed linear model and ANN.…”

Section: Related Workmentioning

confidence: 99%

Rainfall prediction in the northeast region of Thailand using Modular Fuzzy Inference System

Kajornrit

Wong

Fung

2012

2012 IEEE International Conference on Fuzzy Systems

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Abstract-In water management systems, accurate rainfall forecasting is indispensable for operation and management of reservoir, and flooding prevention because it can provide an extension of lead-time of the flow forecasting. In general, time series prediction has been widely applied to predict rainfall data. The conventional time series prediction models or artificial neural networks can be used to perform this task. However, such models are difficult to interpret by human analyst. From a hydrologist's point of view, the accuracy of the prediction and understanding the prediction model are equally important. This study proposes the use of a Modular Fuzzy Inference System (Mod FIS) to predict monthly rainfall data in the northeast region of Thailand. The experimental results show that the proposed model can be a good alternative method to provide both accurate results and human-understandable prediction mechanism.

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Section: Related Workmentioning

confidence: 99%

Rainfall prediction in the northeast region of Thailand using Modular Fuzzy Inference System

Kajornrit

Wong

Fung

2012

2012 IEEE International Conference on Fuzzy Systems

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“…Various types of rainfall-runoff models can be found in the literature, varying from empirical models to highly sophisticated physical processes. Empirical models could be established based on statistical techniques (Brocca et al, 2011;Neal et al, 2013) or advanced machine learning algorithms (Lohani et al, 2011); such models can be effectively employed to analyze rainfall and runoff on the basis of historical time series data. In addition, physical-process models focus on simulating hydrological processes in a basin based on a set of mathematical equations governing physical processes of water flow and surfaces (Aronica et al, 2012;Chiew et al, 1993;Beven et al, 1984;Birkel et al, 2010;Grimaldi et al, 2013).…”

Section: A Review Of Related Work On Flood Susceptibility Predictionmentioning

confidence: 99%

A Bayesian framework based on a Gaussian mixture model and radial-basis-function Fisher discriminant analysis (BayGmmKda V1.1) for spatial prediction of floods

Bui

Hoang

2017

Geosci. Model Dev.

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Abstract. In this study, a probabilistic model, named as BayGmmKda, is proposed for flood susceptibility assessment in a study area in central Vietnam. The new model is a Bayesian framework constructed by a combination of a Gaussian mixture model (GMM), radial-basis-function Fisher discriminant analysis (RBFDA), and a geographic information system (GIS) database. In the Bayesian framework, GMM is used for modeling the data distribution of flood-influencing factors in the GIS database, whereas RBFDA is utilized to construct a latent variable that aims at enhancing the model performance. As a result, the posterior probabilistic output of the BayGmmKda model is used as flood susceptibility index. Experiment results showed that the proposed hybrid framework is superior to other benchmark models, including the adaptive neuro-fuzzy inference system and the support vector machine. To facilitate the model implementation, a software program of BayGmmKda has been developed in MATLAB. The BayGmmKda program can accurately establish a flood susceptibility map for the study region. Accordingly, local authorities can overlay this susceptibility map onto various land-use maps for the purpose of land-use planning or management.

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“…The results have shown that ARMA and KNearest-Neighbors (KNN) have performed prediction better than ANN and its variants when the correlation between input and output is low. Lohani [25] has compared ANN, FIS and linear transfer model for daily rainfall-runoff model under different input domains. The results illustrate that FIS has outperformed linear model and ANN.…”

Section: Co-published By Atlantis Press and Taylor And Francismentioning

confidence: 99%

Effective Features and Hybrid Classifier for Rainfall Prediction

KavithaRani¹,

Govardhan²

2014

IJCIS

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Rainfall prediction has emerged as a challenging time-series prediction problem in recent years. In this paper, we propose a novel rainfall prediction technique using effective feature indicators and a hybrid technique. Our proposed model consists of three phases, namely, layer model simulation, training phase and testing phase. At the outset, the input rainfall dataset is preprocessed using the feature indicators. There are five feature indicators used in the preprocessing step namely, channel index (CI), ulcer index (UI), rate of change (ROC), relative strength index (RSI) and average directional movement index (ADX). Subsequently, feature matrices are formed based on the preprocessed rainfall data. Once the feature matrix is formed, the prediction is done based on the hybrid classifier. In the hybrid classifier, artificial bee colony algorithm is combined with the genetic algorithm for training the feed forward neural network. The performance of the algorithm is analyzed with the help of real datasets gathered from Rayalaseema, Aandhra and Telangana regions. Finally, from comparative analysis it is established that the proposed rainfall prediction yields better result (MAC=4.0672) when compared with Artificial Bee Colony with Neural Network.

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Comparative study of neural network, fuzzy logic and linear transfer function techniques in daily rainfall‐runoff modelling under different input domains

Cited by 71 publications

References 20 publications

Rainfall prediction in the northeast region of Thailand using Modular Fuzzy Inference System

Rainfall prediction in the northeast region of Thailand using Modular Fuzzy Inference System

A Bayesian framework based on a Gaussian mixture model and radial-basis-function Fisher discriminant analysis (BayGmmKda V1.1) for spatial prediction of floods

Effective Features and Hybrid Classifier for Rainfall Prediction

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