Rainfall data analyzing using moving average (MA) model and wavelet multi-resolution intelligent model for noise evaluation to improve the forecasting accuracy

Akrami, Seyed Ahmad; El-Shafie, Ahmed; Naseri, Mahdi; Santos, Celso Augusto Guimarães

doi:10.1007/s00521-014-1675-0

Cited by 41 publications

(17 citation statements)

References 19 publications

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“…In this paper, it is necessary that the relationship between the spatial nappe wind distributions and the shapes of the buckets at five bucket angles are clarified. Figure 6 depicts the time history curve of the nappe wind of the CB at the measurement point (3.04, 0, 0), and it is smoothed using the moving average method [30]. A total of 400 experimental data points were collected with the interval of 1 s for each measuring point, and the average value is regarded as the magnitude of the nappe wind velocity.…”

Section: Results and Analysismentioning

confidence: 99%

Effects of Bucket Type and Angle on Downstream Nappe Wind Caused by a Turbulent Jet

Lian

Gou

et al. 2019

IJERPH

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The downstream nappe wind caused by flood discharge has a great influence on the rainfall distribution, the operational safety of dams, and their surrounding ecological environments. A physical experiment was conducted to measure the spatial distribution of the downstream nappe wind and the splash for a continuous bucket (CB) and a tongue-shaped bucket (TB) for five bucket angles (40°, 45°, 50°, 55°, and 60°). The experimental results demonstrate that the trajectory width and height of the nappe increase as the angles increase, but the effect on the length is converse. The wind velocity and splash weight of the two buckets decrease along the flowing direction. In the lateral direction, the wind velocity and splash weight for the CB decrease as y increases, but the wind velocity of the TB trends to humplike; its splash weight decreases near the axis of the bucket, and is stable in the other region. In the vertical direction, the velocity for the CB increases and then decreases as z increases, but that for the TB decreases monotonously. The velocity of the wind and weight of the splash for the CB decreases with the increasing angles, but those of the TB peak at 45°. The findings are useful for the more accurate prediction of rainfall.

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Section: Results and Analysismentioning

confidence: 99%

Effects of Bucket Type and Angle on Downstream Nappe Wind Caused by a Turbulent Jet

Lian

Gou

et al. 2019

IJERPH

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“…Being an important step in data standardization [49], data received was pre-processed as it had some gaps with respect to time. There were also few irrelevant data such as, exceptionally high values.…”

Section: Plos Onementioning

confidence: 99%

Optimised neural network model for river-nitrogen prediction utilizing a new training approach

et al. 2020

Self Cite

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In the past few decades, there has been a rapid growth in the concentration of nitrogenous compounds such as nitrate-nitrogen and ammonia-nitrogen in rivers, primarily due to increasing agricultural and industrial activities. These nitrogenous compounds are mainly responsible for eutrophication when present in river water, and for 'blue baby syndrome' when present in drinking water. High concentrations of these compounds in rivers may eventually lead to the closure of treatment plants. This study presents a training and a selection approach to develop an optimum artificial neural network model for predicting monthly average nitrate-N and monthly average ammonia-N. Several studies have predicted these compounds, but most of the proposed procedures do not involve testing various model architectures in order to achieve the optimum predicting model. Additionally, none of the models have been trained for hydrological conditions such as the case of Malaysia. This study presents models trained on the hydrological data from 1981 to 2017 for the Langat River in Selangor, Malaysia. The model architectures used for training are General Regression Neural Network (GRNN), Multilayer Neural Network and Radial Basis Function Neural Network (RBFNN). These models were trained for various combinations of internal parameters, input variables and model architectures. Post-training, the optimum performing model was selected based on the regression and error values and plot of predicted versus observed values. Optimum models provide promising results with a minimum overall regression value of 0.92.

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“…SST data is typically long-time-sequence data, hence many researchers have regarded SSTP as a time-series regression problem, thus applying time-series prediction methods to SSTP. The traditional time-series prediction methods such as Autoregressive (AR) [13], Moving Average (MA) [14] and Autoregressive Moving Average (ARMA) [15] are linear. Yet, SST has non-stationary and nonlinear characteristics, thus these linear methods are not well-suited to the practical application of SSTP.…”

Section: Related Workmentioning

confidence: 99%

“…where P and G are randomly selected from the non-dominated solution set S, and c is the interference coefficient. New individuals can be generated by Equations (13) and (14). Algorithm 2 expresses the complete process of local search.…”

Section: Of 18mentioning

confidence: 99%

Improved Particle Swarm Optimization for Sea Surface Temperature Prediction

Zha

Song

et al. 2020

Energies

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The Sea Surface Temperature (SST) is one of the key factors affecting ocean climate change. Hence, Sea Surface Temperature Prediction (SSTP) is of great significance to the study of navigation and meteorology. However, SST data is well-known to suffer from high levels of redundant information, which makes it very difficult to realize accurate predictions, for instance when using time-series regression. This paper constructs a simple yet effective SSTP model, dubbed DSL (given its origination from methods known as DTW, SVM and LSPSO). DSL is based on time-series similarity measure, multiple pattern learning and parameter optimization. It consists of three parts: (1) using Dynamic Time Warping (DTW) to mine the similarities in historical SST series; (2) training a Support Vector Machine (SVM) using the top-k similar patterns, deriving a robust SSTP model that offers a 5-day prediction window based on multiple SST input sequences; and (3) developing an improved Particle Swarm Optimization (PSO) method, dubbed LSPSO, which uses a local search strategy to achieve the combined requirement of prediction accuracy and efficiency. Our method strives for optimal model parameters (pattern length and interval step) and is suited for long-term series, leading to significant improvements in SST trend predictions. Our experimental validation shows a 16.7% reduction in prediction error, at a 76% gain in operating efficiency. We also achieve a significant improvement in prediction accuracy of non-stationary SST time series, compared to DTW, SVM, DS (i.e., DTW + SVM), and a recent deep learning method dubbed Long-Short Term Memory (LSTM).

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Rainfall data analyzing using moving average (MA) model and wavelet multi-resolution intelligent model for noise evaluation to improve the forecasting accuracy

Cited by 41 publications

References 19 publications

Effects of Bucket Type and Angle on Downstream Nappe Wind Caused by a Turbulent Jet

Effects of Bucket Type and Angle on Downstream Nappe Wind Caused by a Turbulent Jet

Optimised neural network model for river-nitrogen prediction utilizing a new training approach

Improved Particle Swarm Optimization for Sea Surface Temperature Prediction

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