Artificial Neural Network based Weather Prediction using Back Propagation Technique

Kakar, Saboor Ahmad; Sheikh, Naveed; Naseem, Adnan; Iqbal, Saleem; Rehman, Abdul; Kakar, Aziz ullah; Kakar, Bilal Ahmad; Kakar, Hazrat Ali; Khan, Bilal

doi:10.14569/ijacsa.2018.090859

Cited by 15 publications

(9 citation statements)

References 13 publications

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“…Various papers and researches proposed weather forecasting models to use an ANN and CNN in order to accurately predict the weather and help solve all of the problems. Since forecast data is nonlinear which follows certain irregular patterns and trends, many conventional techniques can be used to improve model productivity and achieve predictions that are better than the previous forecasts [12] [13]. However, ANN has proven to be a more effective method of increasing reliability and accuracy.…”

Section: Introductionmentioning

confidence: 99%

An evaluation of CNN and ANN in prediction weather forecasting: A review

Kareem

Hamad

Askar

2021

Sustainable Engineering and Innovation

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Artificial intelligence through deep neural networks is now widely used in a variety of applications that have profoundly altered human livelihoods in a variety of ways. People's daily lives have become much more convenient. Image recognition, smart recommendations, self-driving vehicles, voice translation, and a slew of other neural network innovations have had a lot of success in their respective fields. The authors present the ANN applied in weather forecasting. The prediction technique relies solely upon learning previous input values from intervals in order to forecast future values. And also, Convolutional Neural Networks (CNNs) are a form of deep learning technique that can help classify, recognize, and predict trends in climate change and environmental data. However, due to the inherent difficulties of such results, which are often independently identified, non-stationary, and unstable CNN algorithms should be built and tested with each dataset and system separately. On the other hand, to eradicate error and provides us with data that is virtually identical to the real value we need Artificial Neural Networks (ANN) algorithms or benefit from it. The presented CNN model's forecasting efficiency was compared to some state-of-the-art ANN algorithms. The analysis shows that weather prediction applications become more efficient when using ANN algorithms because it is really easy to put into practice.

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Section: Introductionmentioning

confidence: 99%

An evaluation of CNN and ANN in prediction weather forecasting: A review

Kareem

Hamad

Askar

2021

Sustainable Engineering and Innovation

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show abstract

“…ANN is an alternative method to efficiently predict time series. [45,46]. For modelling and predicting complicated time series, ANN may be used for researchers in many time series applications, such as seasonal prevision [18], Weather forecasting, prediction of electric demand, air emissions, etc.…”

Section: Neural Networkmentioning

confidence: 99%

Predicting Weather Forecasting State Based on Data Mining Classification Algorithms

Kareem

Abdulazeez

Hasan

2021

AJRCoS

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Weather forecasting is the process of predicting the status of the atmosphere for certain regions or locations by utilizing recent technology. Thousands of years ago, humans tried to foretell the weather state in some civilizations by studying the science of stars and astronomy. Realizing the weather conditions has a direct impact on many fields, such as commercial, agricultural, airlines, etc. With the recent development in technology, especially in the DM and machine learning techniques, many researchers proposed weather forecasting prediction systems based on data mining classification techniques. In this paper, we utilized neural networks, Naïve Bayes, random forest, and K-nearest neighbor algorithms to build weather forecasting prediction models. These models classify the unseen data instances to multiple class rain, fog, partly-cloudy day, clear-day and cloudy. These model performance for each algorithm has been trained and tested using synoptic data from the Kaggle website. This dataset contains (1796) instances and (8) attributes in our possession. Comparing with other algorithms, the Random forest algorithm achieved the best performance accuracy of 89%. These results indicate the ability of data mining classification algorithms to present optimal tools to predict weather forecasting.

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“…In more advanced models, the non-linear transformation is adapted by some continuous functions. NN is very popular for prediction with few attributes such as stock market prediction [17], weather forecasting [18] and customer churn [19]. NN was used by [20] in mapping out the poverty in Mexico.…”

Section: Related Workmentioning

confidence: 99%

Household Overspending Model Amongst B40, M40 and T20 using Classification Algorithm

Othman¹,

Bakar²,

Sani³

et al. 2020

IJACSA

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The family economy is a critical indicator of the well-being of a family institution. It can be seen by the total income and how well the household finances is managed. In Malaysia, the household income level is categorized as B40, M40 and T20. These categories can also indicate the poverty level of the household. Overspending is a phenomenon where the monthly expenses are more than the household's total income, which affects economic wellbeing. Finding important factors that affect the spending patterns among the household can reveal the causes of overspending. It will assist the government in mitigating such problems. Availability of 4 million household expenditure records obtained from the survey conducted in 2016 by the Department of Statistics Malaysia eases the aim of this study to develop a household overspending model by using machine learning. The model is developed using 12 household demographic attributes with 14451 household records. The attributes are the number of households, area, state, strata, race, highest certificate, marital status, gender, housing, income, total expenditure, and category as attributes class. The model development employs five machine learning algorithms namely decision tree, Naïve Bayes, Neural network, Support Vector Machines, Nearest Neighbour. The results show that the decision tree through J48 algorithm has produced the easiest rule to be interpreted. The model shows four attributes which were income, state, races and number of households that highly influence the overspending problem. Based on the research finding, it can be concluded that these attributes are essential for improving the indicator measure for Malaysian Family Wellbeing Index in the aspect of overspending.

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Artificial Neural Network based Weather Prediction using Back Propagation Technique

Cited by 15 publications

References 13 publications

An evaluation of CNN and ANN in prediction weather forecasting: A review

An evaluation of CNN and ANN in prediction weather forecasting: A review

Predicting Weather Forecasting State Based on Data Mining Classification Algorithms

Household Overspending Model Amongst B40, M40 and T20 using Classification Algorithm

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