scholarly journals Improved Rainfall Prediction Using Combined Pre-Processing Methods and Feed-Forward Neural Networks

J ◽  
2019 ◽  
Vol 2 (1) ◽  
pp. 65-83 ◽  
Author(s):  
Duong Tran Anh ◽  
Thanh Duc Dang ◽  
Song Pham Van
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wavelet Transform ◽  
Moving Average ◽  
Monthly Rainfall ◽  
Feed Forward ◽  
Rainfall Prediction ◽  
Feed Forward Neural Networks ◽  
Artificial Neural ◽  
Seasonal Decomposition

Rainfall prediction is a fundamental process in providing inputs for climate impact studies and hydrological process assessments. Rainfall events are, however, a complicated phenomenon and continues to be a challenge in forecasting. This paper introduces novel hybrid models for monthly rainfall prediction in which we combined two pre-processing methods (Seasonal Decomposition and Discrete Wavelet Transform) and two feed-forward neural networks (Artificial Neural Network and Seasonal Artificial Neural Network). In detail, observed monthly rainfall time series at the Ca Mau hydrological station in Vietnam were decomposed by using the two pre-processing data methods applied to five sub-signals at four levels by wavelet analysis, and three sub-sets by seasonal decomposition. After that, the processed data were used to feed the feed-forward Neural Network (ANN) and Seasonal Artificial Neural Network (SANN) rainfall prediction models. For model evaluations, the anticipated models were compared with the traditional Genetic Algorithm and Simulated Annealing algorithm (GA-SA) supported by Autoregressive Moving Average (ARMA) and Autoregressive Integrated Moving Average (ARIMA). Results showed both the wavelet transform and seasonal decomposition methods combined with the SANN model could satisfactorily simulate non-stationary and non-linear time series-related problems such as rainfall prediction, but wavelet transform along with SANN provided the most accurately predicted monthly rainfall.

scholarly journals Rainfall Forecasting Using Various Artificial Neural Network Techniques - A Review

2021 ◽  
pp. 506-526
Author(s):  
Nisha Thakur ◽  
Sanjeev Karmakar ◽  
Sunita Soni
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Moving Average ◽  
Weather Prediction ◽  
Optimization Technique ◽  
Back Propagation ◽  
Rainfall Forecasting ◽  
Rainfall Prediction ◽  
Artificial Neural ◽  
Review Reports

The present review reports the work done by the various authors towards rainfall forecasting using the different techniques within Artificial Neural Network concepts. Back-Propagation, Auto-Regressive Moving Average (ARIMA), ANN , K- Nearest Neighbourhood (K-NN), Hybrid model (Wavelet-ANN), Hybrid Wavelet-NARX model, Rainfall-runoff models, (Two-stage optimization technique), Adaptive Basis Function Neural Network (ABFNN), Multilayer perceptron, etc., algorithms/technologies were reviewed. A tabular representation was used to compare the above-mentioned technologies for rainfall predictions. In most of the articles, training and testing, accuracy was found more than 95%. The rainfall prediction done using the ANN techniques was found much superior to the other techniques like Numerical Weather Prediction (NWP) and Statistical Method because of the non-linear and complex physical conditions affecting the occurrence of rainfall.

Near Infrared Spectroscopy and Artificial Neural Network to Characterise Olive Fruit and Oil Online for Process Optimisation

2015 ◽  
Vol 23 (2) ◽  
pp. 111-121 ◽  
Author(s):  
Yosra Allouche ◽  
Estrella Funes López ◽  
Gabriel Beltrán Maza ◽  
Antonio Jiménez Márquez
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wavelet Transform ◽  
Near Infrared ◽  
Moving Average ◽  
Coefficient Of Determination ◽  
Hammer Mill ◽  
Predictive Capacity ◽  
Olive Fruit ◽  
Artificial Neural

A sensor-software based on an artificial neural network (SS-ANN) was designed for real-time characterisation of olive fruit (pulp/stone ratio, extractability index, moisture and oil contents) and the potential characteristics of the extracted oil (free acidity, peroxide index, K232 and K270, pigments and polyphenols) in olive paste prior to the kneading step. These predictions were achieved by measuring variables related to olive fruit at the crushing stage, including the type of hammer mill (single grid, double grid and Listello), sieve diameter (4 mm, 5 mm, 6 mm and 7 mm), hammer rotation speed (from 2000 rpm to 3000 rpm), temperature before crushing and mill room temperature. These were related to the near infrared (NIR) spectra from online scanned freshly milled olive paste in the malaxer with data pretreated by either the moving average or wavelet transform technique. The networks obtained showed good predictive capacity for all the parameters examined. Based on the root mean square error of prediction ( RMSEP), residual predictive deviation ( RPD) and coefficient of determination of validation ( r2), the models that used the wavelet preprocessing procedure were more accurate than those that used the moving average. As examples, for moisture and polyphenols, RMSEP values were 1.79% and 87.80 mg kg−1, and 1.46% and 61.50 mg kg−1, respectively for the moving average and wavelet transform. Similar results were found for the other parameters. In conclusion, these results confirm the feasibility of SS-ANN as a tool for optimising the olive oil elaboration process.

River flow forecasting using different artificial neural network algorithms and wavelet transform

2009 ◽  
Vol 36 (1) ◽  
pp. 26-38 ◽  
Author(s):  
Turgay Partal
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Wavelet Transform ◽  
Back Propagation ◽  
Propagation Model ◽  
River Flows ◽  
Feed Forward ◽  
Feed Forward Back Propagation ◽  
Artificial Neural

In this study, the wavelet–neural network structure that combines wavelet transform and artificial neural networks has been employed to forecast the river flows of Turkey. Discrete wavelet transforms, which are useful to obtain to the periodic components of the measured data, have significantly positive effects on artificial neural network modeling performance. Generally, the feed-forward back-propagation method was studied with respect to artificial neural network applications to water resources data. In this study, the performance of generalized neural networks and radial basis neural networks were compared with feed-forward back-propagation methods. Six different models were studied for forecasting of monthly river flows. It was seen that the wavelet and feed-forward back-propagation model was superior to the other models in terms of selected performance criteria.

scholarly journals Rainfall Prediction using Artificial Neural Network in Semi-Arid mountainous region, Saudi Arabia

2021 ◽  
Vol 32 (4) ◽  
pp. 1-11
Author(s):  
Roohul Abad Khan ◽  
Rachida El Morabet ◽  
Javed Mallick ◽  
Mohammed Azam ◽  
Viola Vambol ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Arid Region ◽  
Prediction Models ◽  
Moving Average ◽  
Mountainous Region ◽  
Rainfall Prediction ◽  
Artificial Neural ◽  
Semi Arid Region ◽  
Semi Arid

Rainfall prediction using Artificial Intelligence technique is gaining attention nowadays. Semi-arid region receives rainfall below potential evapotranspiration but more than arid region. However, in mountainous semi-arid region high rainfall intensity makes it highly variable. This renders rainfall prediction difficult by applying normal techniques and calls for data pre-processing. This study presents rainfall prediction in semi-arid mountainous region of Abha, KSA. The study adopted Moving Average (Method) for data pre-processing based on 2 years, 3 years, 4 years, 5 years and 10 years. The Artificial Neural Network (ANN) was trained for a period of 1978-2016 rainfall data. The neural network was validated against the existing data of period 1997-2006. The trained neural network was used to predict for period of 2017-2025. The performance of the model was evaluated against AAE, MAE, RMSE, MASE and PP. The mean absolute error was observed least in 2 years moving average model. However, the most accurate prediction models were obtained from 2 years moving average and 5 year moving average. The study concludes that ANN coupled with MA have potential of predicting rainfall in Semi-Arid mountainous region.

scholarly journals Rainfall Prediction Using Artificial Neural Network (ANN) for tarai Region of Uttarakhand

2019 ◽  
pp. 1-7 ◽  
Author(s):  
Pooja Yadav ◽  
Atish Sagar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Feed Forward ◽  
Rainfall Prediction ◽  
Feed Forward Back Propagation ◽  
Propagation Algorithm ◽  
Term Prediction ◽  
Artificial Neural

Rainfall prediction is clearly of great importance for any country. One would like to make long term prediction, i.e. predict total monsoon rainfall a few weeks or months and in advance short term prediction, i.e. predict rainfall over different locations a few days in advance [1]. Predicted by using its correlation with observed parameter. Several regression and neural network based models are currently available. While Artificial Neural Network provide a great deal of promise, they also embody much uncertainty [2,3]. In this paper, different artificial neural network models have been created for the rainfall prediction of Uttarakhand region in India. These ANN models were created using training algorithms namely, feed-forward back propagation algorithm [4,5]. The number of neurons for all the models was kept at 10. The mean squared error was measured for each model and the best accuracy was obtained by the feed-forward back propagation algorithm with MSE value as low as 0.00547823.

Low-Cost Hardware Implementation of Human Blood Identification Device Using Feed-Forward Artificial Neural Network on FPGA

2018 ◽  
Author(s):  
Rizki Eka Putri ◽  
Denny Darlis
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Low Cost ◽  
Image Resolution ◽  
Blood Type ◽  
Feed Forward ◽  
Testing Facility ◽  
System Input ◽  
Artificial Neural ◽  
Short Time

This article was under review for ICELTICS 2018 -- In the medical world there is still service dissatisfaction caused by lack of blood type testing facility. If the number of tested blood arise, a lot of problems will occur so that electronic devices are needed to determine the blood type accurately and in short time. In this research we implemented an Artificial Neural Network on Xilinx Spartan 3S1000 Field Programable Gate Array using XSA-3S Board to identify the blood type. This research uses blood sample image as system input. VHSIC Hardware Discription Language is the language to describe the algorithm. The algorithm used is feed-forward propagation of backpropagation neural network. There are 3 layers used in design, they are input, hidden1, and output. At hidden1layer has two neurons. In this study the accuracy of detection obtained are 92%, 92%, 92%, 90% and 86% for 32x32, 48x48, 64x64, 80x80, and 96x96 pixel blood image resolution, respectively.

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