Monthly streamflow forecasting using neuro-wavelet techniques and input analysis

Honorato, André Gustavo da Silva Melo; Silva, Gustavo Barbosa Lima da; Santos, Celso Augusto Guimarães

doi:10.1080/02626667.2018.1552788

Cited by 37 publications

(15 citation statements)

References 59 publications

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“…Aziz et al ( 2014) adopted a regional flood frequency analysis with the use of artificial neural networks to estimate flood quantiles in Australia, and it has been found that such an analysis with ANN generates more accurate analysis result. Honorato et al (2019) also applied neuro-wavelet techniques to predict monthly streamflow. These integrated techniques are tested and later found with the accuracy improvement of the models.…”

Section: Literature Reviewsmentioning

confidence: 99%

A Forecasting Model in Managing Future Scenarios to Achieve the Sustainable Development Goals of Thailand’s Environmental Law: Enriching the Path Analysis-Varima-Ovi Model

Sutthichaimethee

Wahab²

2021

IJEEP

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The objective of this study is to develop a forecasting model for causal factors management in the future in to order to achieve sustainable development goals. This study applies a validity-based concept and the best model called "Path analysis based on vector autoregressive integrated moving average with observed variables" (Path Analysis-VARIMA-OV i Model). The main distinguishing feature of the proposed model is the highly efficient coverage capacity for different contexts and sectors. The model is developed to serve long-term forecasting (2020)(2021)(2022)(2023)(2024)(2025)(2026)(2027)(2028)(2029)(2030)(2031)(2032)(2033)(2034). The results of this study show that all three latent variables (economic growth, social growth, and environmental growth) are causally related. Based on the Path Analysis-VARIMA-OV i Model, the best linear unbiased estimator (BLUE) is detected when the government stipulates a new scenario policy. This model presents the findings that if the government remains at the current future energy consumption levels during 2020-2034, constant with the smallest error correction mechanism, the future CO 2 emission growth rate during 2020-2034 is found to increase at the reduced rate of 8.62% (2020/2034) or equivalent to 78.12 Mt CO 2 Eq. (2020/2034), which is lower than a carrying capacity not exceeding 90.5 Mt CO 2 Eq. (2020-2034). This outcome differs clearly when there is no stipulation of the above scenario. Future CO 2 emission during 2020-2034 will increase at a rate of 40.32% or by 100.92 Mt CO 2 Eq. (2020/2034). However, when applying the Path Analysis-VARIMA-OV i Model to assess the performance, the mean absolute percentage error (MAPE) is estimated at 1.09%, and the root mean square error (RMSE) is estimated at 1.55%. In comparison with other models, namely multiple regression model (MR model), artificial neural network model (ANN model), back-propagation neural network model (BP model), fuzzy analysis network process model (FANAP model), gray model (GM model), and gray-autoregressive integrated moving average model (GM-ARIMA model), the Path Analysis-VARIMA-OV i model is found to be the most suitable tool for a policy management and planning to achieve a sustainability for Thailand.

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Section: Literature Reviewsmentioning

confidence: 99%

A Forecasting Model in Managing Future Scenarios to Achieve the Sustainable Development Goals of Thailand’s Environmental Law: Enriching the Path Analysis-Varima-Ovi Model

Sutthichaimethee

Wahab²

2021

IJEEP

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“…Each of these connections receives a weight, which determines its impact on the cells it connects. Each cell thus has an input, which allows it to receive information from other cells, but also from what is called an activation function, which in the simplest cases is a simple identity of the result obtained by the input and finally an output (Santos & Silva, 2014;Freire et al, 2019;Santos et al, 2019;Honorato et al, 2019).…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Spatial modeling of soil salinity using multiple linear regression, ordinary kriging and artificial neural network methods in the Lower Cheliff plain, Algeria

Noureddine¹,

Achite²,

Santos³

et al. 2019

JUEE

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Soil salinity is one of the most damaging environmental issues worldwide, essentially in arid and semi-arid regions, caused by various factors. Spatial estimation and prediction of salinity is important to predict land evaluation in order to develop and determine leaching factor and the precise management for maximum production. The Lower Cheliff is characterized by the augmentation of rate of soil salinity in 80% of area. In this study, the relationship between both elevation and soil salinity was analysed, giving their role in understanding and estimating the spatial distribution of soil salinity in the Lower Cheliff plain. To conduct this work, 406 samples were taken and analysis of electric conductivity was performed as well as the measurement of the elevation using a GPS. The correlations of soil salinity with elevation were analysed as well. In this study, a great focus on the use of the multiple linear regressions, ordinary kriging and artificial neural network methods was given. The results showed that soil salinity had a good correlation with elevation, and according to the values of coefficient of determination (R 2), root mean square error (RMSE) and mean absolute error (MAE), the superiority of MLP model was implied with the value of R 2 = 0.994, RMSE = 0.63 and MAE = 0.33.

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“…The aforementioned predictions are also related to crucial aspects of energetic planning, such as pricing strategies, expansion of the installed capacity, and the expected availability of energy for short and medium-term horizons (Honorato, et al, 2018;Siqueira et al, 2018;Zhang et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Comparative study of forecasting approaches in monthly streamflow series from Brazilian hydroelectric plants using Extreme Learning Machines and Box & Jenkins models

Belotti

Mendes

Leme

et al. 2021

Journal of Hydrology and Hydromechanics

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Several activities regarding water resources management are dependent on accurate monthly streamflow forecasting, such as flood control, reservoir operation, water supply planning, hydropower generation, energy matrix planning, among others. Most of the literature is focused on propose, compare, and evaluate the forecasting models. However, the decision on forecasting approaches plays a significant role in such models’ performance. In this paper, we are focused on investigating and confront the following forecasting approaches: i) use of a single model for the whole series (annual approach) versus using 12 models, each one responsible for predicting each month (monthly approach); ii) for multistep forecasting, the use of direct and recursive methods. The forecasting models addressed are the linear Autoregressive (AR) and Periodic Autoregressive (PAR) models, from the Box & Jenkins family, and the Extreme Learning Machines (ELM), an artificial neural network architecture. The computational analysis involves 20 time series associated with hydroelectric plants indicated that the monthly approach with the direct multistep method achieved the best overall performances, except for the cases in which the coefficient of variation is higher than two. In this case, the recursive approach stood out. Also, the ELM overcame the linear models in most cases.

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Monthly streamflow forecasting using neuro-wavelet techniques and input analysis

Cited by 37 publications

References 59 publications

A Forecasting Model in Managing Future Scenarios to Achieve the Sustainable Development Goals of Thailand’s Environmental Law: Enriching the Path Analysis-Varima-Ovi Model

A Forecasting Model in Managing Future Scenarios to Achieve the Sustainable Development Goals of Thailand’s Environmental Law: Enriching the Path Analysis-Varima-Ovi Model

Spatial modeling of soil salinity using multiple linear regression, ordinary kriging and artificial neural network methods in the Lower Cheliff plain, Algeria

Comparative study of forecasting approaches in monthly streamflow series from Brazilian hydroelectric plants using Extreme Learning Machines and Box & Jenkins models

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