Rainfall Forecasting Based on Ensemble Empirical Mode Decomposition and Neural Networks

Beltrán-Castro, Juan; Valencia-Aguirre, Juliana; Orozco-Alzate, Mauricio; Castellanos-Domínguez, Germán; González, Carmelo Javier León

doi:10.1007/978-3-642-38679-4_47

Cited by 16 publications

(6 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The findings of the study show that the network that used a lower lag performs better than the ones that use high lag. Beltrn-Castro et al [12] employed Ensemble Empirical Mode Decomposition (EEMD) for daily rainfall prediction. The original data was divided into a set of simple components.…”

Section: Related Workmentioning

confidence: 99%

Artificial Neural Network Models for Rainfall Prediction

Dada

Yakubu

Oyewola

2021

EJECE

View full text Add to dashboard Cite

Rainfall prediction is an important meteorological problem that can greatly affect humanity in areas such as agriculture production, flooding, drought, and sustainable management of water resources. The dynamic and nonlinear nature of the climatic conditions have made it impossible for traditional techniques to yield satisfactory accuracy for rainfall prediction. As a result of the sophistication of climatic processes that produced rainfall, using quantitative techniques to predict rainfall is a very cumbersome task. The paper proposed four non-linear techniques such as Artificial Neural Networks (ANN) for rainfall prediction. ANN has the capacity to map different input and output patterns. The Feed Forward Neural Network (FFNN), Cascade Forward Neural Network (CFNN), Recurrent Neural Network (RNN), and Elman Neural Network (ENN) were used to predict rainfall. The dataset used for this work contains some meteorological variables such as temperature, wind speed, humidity, rainfall, visibility, and others for the year 2015-2019. Simulation results indicated that of all the proposed Neural Network (NN) models, the Elman NN model produced the best performance. We also found out that Elman NN has the best performance for the year 2018 (having the lowest RMSE, MSE, and MAE of 6.360, 40.45, and 0.54 respectively). The results indicated that NN algorithms are robust, dependable, and reliable algorithms that can be used for daily, monthly, or yearly rainfall prediction.

show abstract

Section: Related Workmentioning

confidence: 99%

Artificial Neural Network Models for Rainfall Prediction

Dada

Yakubu

Oyewola

2021

EJECE

View full text Add to dashboard Cite

show abstract

“…These error metrics show the difference between the forecasted values and original (real DO concentration) values and the smaller the differences; better is the performance of the proposed novel hybrid EEMD-based LSTM forecasting model. The error metrics formulas are given as: 19) where N denotes the number of data points, V i and F i represent the real and forecasted values, respectively.…”

Section: Performance Evaluation Metricsmentioning

confidence: 99%

Dissolved Oxygen Forecasting in Aquaculture: A Hybrid Model Approach

Eze

Ajmal

2020

Applied Sciences

View full text Add to dashboard Cite

Dissolved oxygen (DO) concentration is a vital parameter that indicates water quality. We present here DO short term forecasting using time series analysis on data collected from an aquaculture pond. This can provide the basis of data support for an early warning system, for an improved management of the aquaculture farm. The conventional forecasting approaches are commonly characterized by low accuracy and poor generalization problems. In this article, we present a novel hybrid DO concentration forecasting method with ensemble empirical mode decomposition (EEMD)-based LSTM (long short-term memory) neural network (NN). With this method, first, the sensor data integrity is improved through linear interpolation and moving average filtering methods of data preprocessing. Next, the EEMD algorithm is applied to decompose the original sensor data into multiple intrinsic mode functions (IMFs). Finally, the feature selection is used to carefully select IMFs that strongly correlate with the original sensor data, and integrate into both inputs for the NN. The hybrid EEMD-based LSTM forecasting model is then constructed. The performance of this proposed model in training and validation sets was compared with the observed real sensor data. To obtain the exact evaluation accuracy of the forecasted results of the hybrid EEMD-based LSTM forecasting model, four statistical performance indices were adopted: mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE). Results are presented for the short term (12-h) and the long term (1-month) that are encouraging, indicating suitability of this technique for forecasting DO values.

show abstract

“…The decomposition shows that each sub-sequence reveals the disparate intrinsic features of the original signal. EMD method is usually applied for original signal decomposition into its intrinsic multi-scale characteristics [20]. Generally, prediction methods that are based on signal's multi-scale characteristics are widely applied in different fields like short-term rainfall forecasting [21], short-term traffic flow prediction [22][23][24] and short-term wind power forecasting [25][26].…”

Section: Related Literature Reviewmentioning

confidence: 99%

Dissolved Oxygen Forecasting in Aquaculture: A Hybrid Model Approach

Eze¹,

Ajmal²

2020

Preprint

View full text Add to dashboard Cite

Dissolved Oxygen (DO) concentration is a vital parameter that indicates water quality. DO short term forecasting using time series analysis on data collected from an aquaculture pond is presented here. This can provide data support for an early warning system for an improved management of the aquaculture farm. The conventional forecasting approaches are commonly characterized by low accuracy and poor generalization problems. In this article, we present a novel hybrid DO concentration forecasting method with ensemble empirical mode decomposition (EEMD) based LSTM (Long short-term memory) neural network (NN). With this method, first, the sensor data integrity is improved through linear interpolation and moving average filtering methods of data preprocessing. Next, the EEMD algorithm is applied to decompose the original sensor data into multiple intrinsic mode functions (IMFs). Finally, the feature selection is used to carefully select IMFs that are strongly correlated with the original sensor data and integrate both into inputs for the NN. The hybrid EEMD-based LSTM forecasting model is then constructed. Performance of this proposed model in training and validation sets was compared with the observed real sensor data. To obtain the exact evaluation accuracy of the forecasted results of the hybrid EEMD-based LSTM forecasting model, three statistical performance indices were adopted: MAE, MSE, and RMSE. Results presented for short term (12-hour period) and long term (1-month period) give a strong indication of suitability of this method for forecasting DO values.

show abstract

Rainfall Forecasting Based on Ensemble Empirical Mode Decomposition and Neural Networks

Cited by 16 publications

References 16 publications

Artificial Neural Network Models for Rainfall Prediction

Artificial Neural Network Models for Rainfall Prediction

Dissolved Oxygen Forecasting in Aquaculture: A Hybrid Model Approach

Dissolved Oxygen Forecasting in Aquaculture: A Hybrid Model Approach

Contact Info

Product

Resources

About