Application of the Artificial Neural Network model for prediction of monthly Standardized Precipitation and Evapotranspiration Index using hydrometeorological parameters and climate indices in eastern Australia

Deo, Ravinesh C.; Şahin, Mehmet

doi:10.1016/j.atmosres.2015.03.018

Cited by 231 publications

(113 citation statements)

References 100 publications

(100 reference statements)

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“…Owing to its prior application in hydrology [ACHARYA et al 2014;DEO, ŞAHIN 2015a], the present study has extended the application of ELM algorithm-based models [HUANG et al 2006] to forecasting daily urban water demand (UWD). Based on state-of-the-art single-layer feed-forward network algorithms, ELMs are similar to feed-forward backpropagation ANNs (ANN FFBP ) and least square support vector regression (LSSVR).…”

Section: Theoretical Overview Extreme Learning Machinementioning

confidence: 99%

“…The algorithm is used to minimize the mean squared error of the predicted and observed UWD [TIWARI, ADAMOWSKI 2013]. In our study, an LM algorithm that uses an approximation to the Hessian matrix was used as follows [DEO, ŞAHIN 2015a]:…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…Widely applied in hydrology, the popular ANN FFBP class of ANN models, equipped with multilayer perceptron functional neurons [ABBOT, MARO-HASY 2012;ADAMOWSKI et al 2012c;DEO, ŞAHIN 2015a;KESKIN, TERZI 2006;MEKANIK et al 2013] was used in the present study. The ANN architecture is designed to successively update the model parameters (weighted connections and neuron biases) to drive the empirical error to a set tolerance through each iteration (epochs) of forward passing of updated parameters and backward propagation of the errors to tune them.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

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Water demand forecasting using extreme learning machines

Tiwari¹,

Adamowski²,

Adamowski³

2016

Journal of Water and Land Development

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The capacity of recently-developed extreme learning machine (ELM) modelling approaches in forecasting daily urban water demand from limited data, alone or in concert with wavelet analysis (W) or bootstrap (B) methods (i.e., ELM, ELM W , ELM B ), was assessed, and compared to that of equivalent traditional artificial neural network-based models (i.e., ANN, ANN W , ANN B ). The urban water demand forecasting models were developed using 3-year water demand and climate datasets for the city of Calgary, Alberta, Canada. While the hybrid ELM B and ANN B models provided satisfactory 1-day lead-time forecasts of similar accuracy, the ANN W and ELM W models provided greater accuracy, with the ELM W model outperforming the ANN W model. Significant improvement in peak urban water demand prediction was only achieved with the ELM W model. The superiority of the ELM W model over both the ANN W or ANN B models demonstrated the significant role of wavelet transformation in improving the overall performance of the urban water demand model.

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Section: Theoretical Overview Extreme Learning Machinementioning

confidence: 99%

Section: Artificial Neural Networkmentioning

confidence: 99%

Section: Artificial Neural Networkmentioning

confidence: 99%

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Water demand forecasting using extreme learning machines

Tiwari¹,

Adamowski²,

Adamowski³

2016

Journal of Water and Land Development

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“…Genetic Algorith [22] and Artificial Neural Network [23,24,25,26], Multi-Layer Perceptron (MLP), Functional Link Artificial Neural Network (FLANN) and Legendre Polynomial Equation (LPE) [27], Multiple Linear Regression (MLR) techniques [28] were introduced for Rainfall prediction.…”

Section: Related Workmentioning

confidence: 99%

Development and Analysis of Artificial Neural Network Models for Rainfall Prediction by Using Time-Series Data

Mishra¹,

Soni²,

Sharma³

et al. 2018

IJISA

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Abstract-Time Series data is large in volume, highly dimensional and continuous updating. Time series data analysis for forecasting, is one of the most important aspects of the practical usage. Accurate rainfall forecasting with the help of time series data analysis will help in evaluating drought and flooding situations in advance. In this paper, Artificial Neural Network (ANN) technique has been used to develop one-month and twomonth ahead forecasting models for rainfall prediction using monthly rainfall data of Northern India. In these model, Feed Forward Neural Network (FFNN) using Back Propagation Algorithm and Levenberg-Marquardt training function has been used. The performance of both the models has been assessed based on Regression Analysis, Mean Square Error (MSE) and Magnitude of Relative Error (MRE). Proposed ANN model showed optimistic results for both the models for forecasting and found one month ahead forecasting model perform better than two months ahead forecasting model. This paper also gives some future directions for rainfall prediction and time series data analysis research.

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“…Physical models usually required more effort and various hydrological variables to simulate the elemental physical processes of the watershed (Yaseen, Kisi, & Demir, 2016). Whereas, soft computing approaches have shown the capability to capture the non-linearity relationship between the predictors and predicted without advance knowledge with less inputs hydrological parameters (Afan, El-Shafie, Yaseen, Hameed, Wan Mohtar, & Hussain, 2014;Deo & Şahin, 2015;Deo, Samui, & Kim, 2015;Fahimi, Yaseen, & El-shafie, 2016).…”

Section: Introductionmentioning

confidence: 99%