Rainfall prediction with backpropagation method

Wahyuni, Elyza Gustri; Fauzan, L M F; Abriyani, Fadhillah; Muchlis, Nurul Fatikah; Ulfa, Malia

doi:10.1088/1742-6596/983/1/012059

Cited by 11 publications

(7 citation statements)

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“…The design configuration of neural network used the four (4) input variables namely air temperature, air humidity, wind speed, and sunshine duration and 3 output variables which are low rainfall, medium rainfall, and high rainfall. The network shows a reasonable result, as evidenced by the results of the prediction of the system precipitation is the same as the results of manual calculations in Microsoft Excel [14].…”

Section: B Weather Predictions Model and Neural Networksupporting

confidence: 64%

Rain–Induced Flood Prediction for Niger Delta Sub-Region of Nigeria Using Neural Networks

Kabari¹,

Mazi²

2020

EJERS

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Climate change generates so many direct and indirect effects on the environment. Some of those effects have serious consequences. Rain-induced flooding is one of the direct effects of climate change and its impact on the environment is usually devastating and worrisome. Floods are one of the most commonly occurring disasters and have caused significant damage to life, including agriculture and economy. They are usually caused in areas where there is excessive downpour and poor drainage systems. The study uses Feedforward Multilayer Neural Network to perform short-term prediction of the amount of rainfall flood for the Niger Delta sub region of Nigeria given previous rainfall data for a specified period of time. The data for training and testing of the Neural Network was sourced from Weather Underground official web site https://www.wunderground.com. An iterative Methodology was used and implemented in MATLAB. We adopted multi-layer Feedforward Neural Networks. The study accurately predicts the rain-induced flood for the Niger Delta sub region of Nigeria.

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Section: B Weather Predictions Model and Neural Networksupporting

confidence: 64%

Rain–Induced Flood Prediction for Niger Delta Sub-Region of Nigeria Using Neural Networks

Kabari¹,

Mazi²

2020

EJERS

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“…2) Cascade forward model of back propagation: Cascade forward (CF) models mimic feed forward networks, but from the input to each layer to the successive layers, they have a weight relationship. In its ideal relationship, the ANN easily discovers the additional links needed to reinforce its alliance [26][27][28][29][30][31]. The CFBPNN model also looks like FFBPNN, which uses a weight-updating back-propagation algorithm, but the key symptom of this network is that all previous neuron layers are connected to another layer of neurons.…”

Section: ) Feed Forward Backpropagation Modelmentioning

confidence: 99%

A Novel Method for Rainfall Prediction and Classification using Neural Networks

Rajkumar¹,

Subrahmanyam²

2021

IJACSA

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In the field of food production, it is an important and difficult job to maintain water sources for major population centres and reduce the risk of flooding, to forecast rainfall reliably and accurately. Accurate and genuine forecasts of rainfall on monthly and seasonal time scales help to provide beneficiaries with knowledge on the control of water supplies, farm forecasting and integrated crop insurance applications. Present rainfall prediction is the challenging task for the researchers and most of the rainfall prediction techniques are fail in accuracy. For this we propose a new effective hybrid approach for forecasting and classifying rainfall using the neural network and ACO method. The collected rainfall data were preprocessed by filling missing data and normalized by min-max normalization, the processed data is given to various classifiers for evaluating its performance. The performance of the existing and proposed models is compared. Performance comparison of existing feed-forward, cascade-forward and pattern recognition NN classifier and the proposed ACO+feed-forward backpropagation, ACO+ cascade-forward backpropagation and ACO+ pattern recognition NN classifier are done. The entire HNN forecasting protocol consists of pre-processing and choosing the input vector and maximising the number of hidden nodes using ACO and ANN modelling.

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“…While Research [19] predicts rainfall in Dhaka Bangladesh on a monthly basis by utilizing ARIMA. Artificial Neural Network is a method that is often used in the last two periods in predicting rainfall [20][21] [22], in addition ANN is also often used for forecasting reservoir inflow [23], total annual crude oil export [24], and prediction of sea level [23]. Based on the research that has been done, it can be concluded that ANN is the right method to succeed and predict.…”

Section: Related Workmentioning

confidence: 99%

Artificial Neural Network for Rainfall Prediction Base on Historical Rainfall Data by Day

2019

IJATCSE

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Increasingly erratic rainfall has a significant effect on agriculture, and health. One field of agriculture that utilizes rainfall patterns is paddy fields. Rice farmers need a calculation of rainfall to determine the process of hatching and planting rice, especially for farmers who have rainfed rice fields. While in the health sector, rainfall greatly determines the growth of mosquitoes which can lead to dengue fever. The health office needs calculations to anticipate the number of people affected by dengue fever. In this study two data modeling were made in predicting rainfall in the Banyumas area, one data model or scenario using weather data other than rainfall such as temperature, humidity, wind speed, duration of solar radiation. While scenario two only uses historical rainfall data per day. In making models and testing data using the Artificial Neural Network (ANN) algorithm. The results showed that scenario two was better than scenario one after being evaluated using the Root Mean Square Error (RMSE). The RMSE obtained in scenarios one and two are 21,667 and 20,448, respectively. It can be concluded that to predict rainfall in the Banyumas area it is better to use historical rainfall data per day compared to using other weather data.

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Rainfall prediction with backpropagation method

Cited by 11 publications

References 0 publications

Rain–Induced Flood Prediction for Niger Delta Sub-Region of Nigeria Using Neural Networks

Rain–Induced Flood Prediction for Niger Delta Sub-Region of Nigeria Using Neural Networks

A Novel Method for Rainfall Prediction and Classification using Neural Networks

Artificial Neural Network for Rainfall Prediction Base on Historical Rainfall Data by Day

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