Journal ArticleDOI
Multiple regression and Artificial Neural Network for long-term rainfall forecasting using large scale climate modes
TLDR
In this paper, the application of Artificial Neural Networks (ANN) and multiple regression analysis (MR) to forecast long-term seasonal spring rainfall in Victoria, Australia was investigated using lagged El Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) as potential predictors.About:
This article is published in Journal of Hydrology.The article was published on 2013-10-30. It has received 209 citations till now. The article focuses on the topics: Multicollinearity & Linear regression.read more
Citations
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Journal ArticleDOI
Flood prediction using machine learning models: Literature review
TL;DR: In this paper, the state-of-the-art machine learning models for both long-term and short-term floods are evaluated and compared using a qualitative analysis of robustness, accuracy, effectiveness and speed.
Journal ArticleDOI
Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia
Ravinesh C. Deo,Mehmet Şahin +1 more
TL;DR: In this paper, a computationally simple, fast and efficient non-linear algorithm known as extreme learning machine (ELM) for the prediction of Effective Drought Index (EDI) in eastern Australia using input data trained from 1957-2008 and the monthly EDI predicted over the period 2009-2011 The predictive variables for the ELM model were the rainfall and mean, minimum, minimum and maximum air temperatures, supplemented by the large-scale climate mode indices of interest as regression covariates, namely the Southern Oscillation Index, Pacific DecadalOscillation, Southern Ann
Journal ArticleDOI
Flood Prediction Using Machine Learning Models: Literature Review
TL;DR: In this paper, the state of the art of ML models in flood prediction and to give insight into the most suitable models are presented. And the major trends in improving the quality of the flood prediction models are investigated.
Journal ArticleDOI
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
Ravinesh C. Deo,Mehmet Şahin +1 more
TL;DR: In this paper, the authors tested the feasibility of the ANN as a data-driven model for predicting the monthly Standardized Precipitation and Evapotranspiration Index (SPEI) for eight candidate stations in eastern Australia using predictive variable data from 1915 to 2005 (training) and simulated data for the period 2006-2012.
Journal ArticleDOI
Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model
TL;DR: A wavelet-based drought model using the extreme learning machine (W-ELM) algorithm where the input data are first screened through the wavelet pre-processing technique for better accuracy is developed to forecast the monthly effective DI (EDI).
References
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Book
Discovering Statistics Using SPSS
Andy P. Field,Jeremy N.V. Miles +1 more
TL;DR: Suitable for those new to statistics as well as students on intermediate and more advanced courses, the book walks students through from basic to advanced level concepts, all the while reinforcing knowledge through the use of SAS(R).
Journal ArticleDOI
A Dipole Mode in the Tropical Indian Ocean
TL;DR: An analysis of observational data over the past 40 years shows a dipole mode in the Indian Ocean: a pattern of internal variability with anomalously low sea surface temperatures off Sumatra and high seasurface temperatures in the western Indian Ocean, with accompanying wind and precipitation anomalies.
Journal ArticleDOI
Some Comments on the Evaluation of Model Performance
TL;DR: In this article, it is suggested that the correlation between model-predicted and observed data, commonly described by Pearson's productmoment correlation coefficient, is an insufficient and often misleading measure of accuracy, and a complement of difference and summary univariate indices is presented as the nucleus of a more informative, albeit fundamentally descriptive, approach to model evaluation.
Journal ArticleDOI
Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications
Holger R. Maier,Graeme C. Dandy +1 more
TL;DR: The steps that should be followed in the development of artificial neural network models are outlined, including the choice of performance criteria, the division and pre-processing of the available data, the determination of appropriate model inputs and network architecture, optimisation of the connection weights (training) and model validation.
Journal ArticleDOI
Artificial Neural Network Modeling of the Rainfall‐Runoff Process
TL;DR: In this paper, the authors presented a new procedure (entitled linear least squares simplex, or LLSSIM) for identifying the structure and parameters of three-layer feed forward ANN models and demonstrated the potential of such models for simulating the nonlinear hydrologic behavior of watersheds.
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