Predicting Weather Forecasting State Based on Data Mining Classification Algorithms

Abstract

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.

Time Series Analysis and Neural Network Optimization of Tourist Destination Heat Prediction Model

“…In this module, we compare the performances of the proposed model with the conventional techniques to validate its effectiveness and robustness in heat prediction. The existing techniques utilized for comparative assessment include Support Vector Machine (SVM) [8], K-Nearest Neighbor (KNN) [9], Long Short Term Memory (LSTM) [10], Deep Feed forward Neural Networks (DFNN) [11], and Temporal Convolutional Neural Network (TCNN) [12]. The parameters utilized for comparative analysis include accuracy, MAE, and computational time.…”

“…The output of the dense layer is forwarded into the output layer of DRNN architecture, which forecasts the heat based on the learned patterns and trends. The output of the DRNN is mathematically represented in Eqn (9)…”

Machine learning algorithms-based decision support model for diabetes

“…forecasting weather [23], text recognition [24], identifying power quality issues [25], and much more.…”

Pendekatan Data Mining Untuk Analisa Curah Hujan Menggunakan Metode Regresi Linear Berganda (Studi Kasus: Kabupaten Aceh Utara)

“…Prakiraan cuaca sudah dilakukan sejak dulu, namun biasanya hanya berdasarkan pengamatan pola kejadian, namun peramalan seperti ini terbukti tidak dapat diandalkan [8]. Prakiraan cuaca adalah proses memprediksi status atmosfer untuk wilayah atau wilayah tertentu lokasi dengan memanfaatkan teknologi terkini [9]. Ketapatan dalam memprediksi hujan akan berpengaruh di banyak sektor seperti kelautan, pertanian, transportasi, penanggulangan bencana dan lainnya [10], maka diperlukan metode dalam memprediksi curah hujan, salah satunya dengan data mining.…”