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An Intelligent System for Filling of Missing Values in Weather Data


Maqsood Ali Solangi, Dr. Ghulam Ali Mallah, Shagufta Naz, Jamil Ahmed Chandio, Muhammad Bux Soomro


Vol. 23  No. 9  pp. 95-99


Recently Machine Learning has been considered as one of the active research areas of Computer Science. The various Artificial Intelligence techniques are used to solve the classification problems of environmental sciences, biological sciences, and medical sciences etc. Due to the heterogynous and malfunctioning weather sensors a considerable amount of noisy data with missing is generated, which is alarming situation for weather prediction stockholders. Filling of these missing values with proper method is really one of the significant problems. The data must be cleaned before applying prediction model to collect more precise & accurate results. In order to solve all above stated problems, this research proposes a novel weather forecasting system which consists upon two steps. The first step will prepare data by reducing the noise; whereas a decision model is constructed at second step using regression algorithm. The Confusion Matrix will be used to evaluation the proposed classifier.


Machine Learning, Missing Values, data cleaning, Weather Prediction