Unorganized Machines for Seasonal Streamflow Series Forecasting

Siqueira, Hugo Valadares; Boccato, Levy; Attux, Romis; Filho, Christiano Lyra

doi:10.1142/s0129065714300095

Cited by 42 publications

(64 citation statements)

References 36 publications

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“…e employment of ELM in the field of hydrology and particularly in river flow modeling demonstrated a very promising and enhancement in the modeling process [14]. e first attempt of modeling river flow using ELM was established in [21]. e authors developed an ELM model to capture the associated nonlinearity of the seasonal river inflow of the Brazilian Hydropower.…”

Section: Introductionmentioning

confidence: 99%

Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Faris

Al‐Ansari

2020

Complexity

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e capability of the extreme learning machine (ELM) model in modeling stochastic, nonlinear, and complex hydrological engineering problems has been proven remarkably. e classical ELM training algorithm is based on a nontuned and random procedure that might not be efficient in convergence of excellent performance or possible entrapment in the local minima problem. is current study investigates the integration of a newly explored metaheuristic algorithm (i.e., Salp Swarm Algorithm (SSA)) with the ELM model to forecast monthly river flow. Twenty years of river flow data time series of the Tigris river at the Baghdad station, Iraq, is used as a case study. Different input combinations are applied for constructing the predictive models based on antecedent values. e results are evaluated based on several statistical measures and graphical presentations. e river flow forecast accuracy of SSA-ELM outperformed the classical ELM and other artificial intelligence (AI) models. Over the testing phase, the proposed SSA-ELM model yielded a satisfactory enhancement in the level accuracies (8.4 and 13.1 percentage of augmentation for RMSE and MAE, respectively) against the classical ELM model. In summary, the study ascertains that the SSA-ELM model is a qualified data-intelligent model for monthly river flow prediction at the Tigris river, Iraq.

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Section: Introductionmentioning

confidence: 99%

Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Faris

Al‐Ansari

2020

Complexity

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“…Many real-time series present seasonal behaviors, mainly due to variations in the weather during a time window [42]. For example, the temperature changes according to the season, being higher in the summer and lower in the winter, and the rainfall periods are different in each season depending on the location [25,42].…”

Section: Deseasonalization and Stationarizationmentioning

confidence: 99%

“…These methodologies are important candidates to solve mapping problems, since they are capable of approximating any nonlinear functions if they are limited, continuous, differentiable and defined in a compact space [24]. The ANN are characterized by an intrinsic learning capability and a present generalization ability [24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks to Estimate the Influence of Vehicular Emission Variables on Morbidity and Mortality in the Largest Metropolis in South America

et al. 2020

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The emission of pollutants from vehicles is presented as a prime factor deteriorating air quality. Thus, seeking public policies encouraging the use and the development of more sustainable vehicles is paramount to preserve populations’ health. To better understand the health risks caused by air pollution and exclusively by mobile sources urges the question of which input variables should be considered. Therefore, this research aims to estimate the impacts on populations’ health related to road transport variables for São Paulo, Brazil, the largest metropolis in South America. We used three Artificial Neural Networks (ANN) (Multilayer Perceptron—MLP, Extreme Learning Machines—ELM, and Echo State Neural Networks—ESN) to estimate the impacts of carbon monoxide, nitrogen oxides, ozone, sulfur dioxide, and particulate matter on outcomes for respiratory diseases (morbidity—hospital admissions and mortality). We also used unusual inputs, such as road vehicles fleet, distributed and sold fuels amount, and vehicle average mileage. We also used deseasonalization and the Variable Selection Methods (VSM) (Mutual Information Filter and Wrapper). The results showed that the VSM excluded some variables, but the best performances were reached considering all of them. The ELM achieved the best overall results to morbidity, and the ESN to mortality, both using deseasonalization. Our study makes an important contribution to the following United Nations Sustainable Development Goals: 3—good health and well-being, 7—affordable and clean energy, and 11—sustainable cities and communities. These research findings will guide government about future legislations, public policies aiming to warranty and improve the health system.

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“…The most important characteristic of these networks is their hidden layer stands untrained, allowing them to train only the output layer in a minimum mean square error sense, which confers a very fast adjust process to the networks [5]. The ELM, proposed by Huang et al [6], are feedforward networks, quite similar to the traditional Multilayer Perceptron (MLP) [7].…”

Section: IImentioning

confidence: 99%

“…Then, to predictions tasks, the ELM may present adequate results even to unknown input data, when it is trained. The most common way to adjust the output layer weights is the application of the MoorePenrose pseudoinverse operation, which guarantees the best solution, by means of a deterministic solution [5]. Unlike the ELM, the ESN are recursive networks endowed by feedback loops of information.…”

Section: IImentioning

confidence: 99%