A study of optimal model lag and spatial inputs to artificial neural network for rainfall forecasting

Luk, Kin Choi; Ball, James E; Sharma, Ashish

doi:10.1016/s0022-1694(99)00165-1

Cited by 263 publications

(123 citation statements)

References 7 publications

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“…Consequently many studies are using different ML algorithms to demonstrate nearly coincident or in some cases, even better prediction yields than the GCM models. In fact, recent studies that compared rainfall predictions using ML models with the physical models demonstrated dramatic improvements in the prediction capability of the former models Marohasy, 2012, 2014;Luk et al, 2000;Mekanik et al, 2013;Nasseri et al, 2008). In particular, the work of Marohasy (2012, 2014) that compared rainfall prediction from an ML algorithm with the POAMA used in Australia over geographically distinct regions in Queensland found that the former approach was superior as evidenced by the lower root mean square errors, lower mean absolute errors and higher correlation coefficients between the observed and modeled rainfall values.…”

Section: Introductionmentioning

confidence: 99%

Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia

Deo

Şahin

2015

Atmospheric Research

260

102

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The prediction of future drought is an effective mitigation tool for assessing adverse consequences of drought events on vital water resources, agriculture, ecosystems and hydrology. Data-driven model predictions using machine learning algorithms are promising tenets for these purposes as they require less developmental time, minimal inputs and are relatively less complex than the dynamic or physical model. This paper authenticates 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 and maximum air temperatures, supplemented by the large-scale climate mode indices of interest as regression covariates, namely the Southern Oscillation Index, Pacific Decadal Oscillation, Southern Annular Mode and the Indian Ocean Dipole moment. To demonstrate the effectiveness of the proposed datadriven model a performance comparison in terms of the prediction capabilities and learning speeds was conducted between the proposed ELM algorithm and the conventional artificial neural network (ANN) algorithm trained with Levenberg-Marquardt back propagation. The prediction metrics certified an excellent performance of the ELM over the ANN model for the overall test sites, thus yielding Mean Absolute Errors, Root-Mean Square Errors, Coefficients of Determination and Willmott's Indices of Agreement of 0.277, 0.008, 0.892 and 0.93 (for ELM) and 0.602, 0.172, 0.578 and 0.92 (for ANN) models. Moreover, the ELM model was executed with learning speed 32 times faster and training speed 6.1 times faster than the ANN model. An improvement in the prediction capability of the drought duration and severity by the ELM model was achieved. Based on these results we aver that out of the two machine learning algorithms tested, the ELM was the more expeditious tool for prediction of drought and its related properties.

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Section: Introductionmentioning

confidence: 99%

Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia

Deo

Şahin

2015

Atmospheric Research

260

102

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“…Regardless the type and dimensions of flood, the following measures should be taken in predicting flood in a flood warning system [14] and [15].…”

Section: Predicting Precipitation and Floodmentioning

confidence: 99%

Flood Management, Flood Forecasting and Warning System

Hajibabaei¹,

Ghasemi²

2017

IJSEA

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Flood phenomenon annually causes many human and financial losses so that flood has even had many losses in countries which are properly developed in river bank protection and construction of reservoir dams aiming to control flood. Today, controlling flood through its management is discussed and one of efficient and effective tools in this field is non-structural flood management as the complementary of structural ones that can have a very important role in controlling flood and consequently reducing the losses derived by that. Flood forecasting and warning systems are considered as one of the non-structural management tools which has given high importance within recent decades and providing information about flood and fast and easy reaction after receiving mentioned information to have minimum losses in flood areas as well as providing some information to manage water resources are of the aims of this system.

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“…ANNs appear to be a useful alternative to traditional statistical techniques for modelling the complex hydrological system, as has been successfully employed in the modelling of various aspects of hydrological processes. Previous studies have demonstrated that ANNs have received much attention with respect to streamflow forecasting (Hu et al 2001, Shamseldin et al 2002, Dolling and Varas 2003, Muhamad and Hassan 2005, Firat 2008, Kisi 2008, Keskin and Taylan 2009, Wang et al 2009), rainfall forecasting (Luk et al 2000, Rajurkar et al 2002, Shamseldin et al 2007, Hung et al 2009), groundwater management (Affandi andWatanabe 2007, Birkinshaw et al 2008) and water quality management (Maier and Dandy 2000). However, there are some disadvantages of ANNs due their network structure, which is hard to determine and usually established using a trial-and-error approach (Kisi 2004).…”

Section: Introductionmentioning

confidence: 99%

Streamflow forecasting using least-squares support vector machines

Shabri

Suhartono

2012

Hydrological Sciences Journal

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This paper investigates the ability of a least-squares support vector machine (LSSVM) model to improve the accuracy of streamflow forecasting. Cross-validation and grid-search methods are used to automatically determine the LSSVM parameters in the forecasting process. To assess the effectiveness of this model, monthly streamflow records from two stations, Tg Tulang and Tg Rambutan of the Kinta River in Perak, Peninsular Malaysia, were used as case studies. The performance of the LSSVM model is compared with the conventional statistical autoregressive integrated moving average (ARIMA), the artificial neural network (ANN) and support vector machine (SVM) models using various statistical measures. The results of the comparison indicate that the LSSVM model is a useful tool and a promising new method for streamflow forecasting.

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A study of optimal model lag and spatial inputs to artificial neural network for rainfall forecasting

Cited by 263 publications

References 7 publications

Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia

Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia

Flood Management, Flood Forecasting and Warning System

Streamflow forecasting using least-squares support vector machines

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