Weather prediction is a critical assumption in weather forecasting. Weather prediction and has been one of the major scientifically and technologically demanding issues worldwide in the last century. The most significant parameter in a hydrological model is Rainfall. The meticulous Rainfall forecasting is one of the major demanding in the atmospheric research. The factors such as pressure, temperature, humidity, wind speed, mean sea-level etc. are used for rainfall forecasting. This study evaluates multiple classifiers such as Artificial Neural Network (ANN), Naïve Bayes and Support Vector Machine for rainfall prediction in Sulaymaniyah city and describes which one is most suitable to predict the precipitation. The dataset has been collected from weather forecast department in Sulaymaniyah city. Pre-processing technique such as cleaning and normalization processes is used for effective prediction. The data mining approaches are evaluated and the Performance is analyzed regarding precision, recall and f-measure with numerous ratios of training and test data.Contribution/Originality: This paper contributes the first logical analysis for the rainfall forecasting in Sulaymaniyah city. The dataset collection is based on the local forecasting department, Weather Forecast department, in the city. This study tests several supervised learning approaches including (ANN), (SVM), and (NB) to perform a comparative analysis concerning their ability for rainfall prediction in the region.
Climate change has a historical impact at universal and local levels over the past era. Climate change is one of the greatest challenge issues in the globe for meteorological research. Air temperature estimation, in particular, has been measured as a significant feature in weather impression studies on industrial sectors, environmental, ecological, and agricultural. Accurately predicting air temperature guides to measure lifestyle, perform a key character for the government, industries, and public in development activities. In this paper, we investigate the use of various data mining approaches such as Support Vector Machine (SVM), Decision tree (DT), and Naïve Bayes for air temperature prediction within Sulaymaniyah City in Kurdistan, IRAQ. The metrological data is collected from the local Weather Forecast Department in the city within the range 2013 to 2018 inclusive. A dataset for the metrological data was developed and used to train the data mining algorithms. The proposed data mining algorithms were tested on the dataset to predict the air temperature and the performance of these algorithms were compared using standard performance metrics. Support vector machine has accomplished promising performance among using algorithms
Automotive companies tend to view customer satisfaction as a determining factor of customer loyalty. An esteem essential to the long-standing profit of an association is a customer satisfaction. However, when an organization lessens customer leaving by 5%, profits raise by 2-8%. former researches have focused on aspects of customer loyalty such as quality of product and customer fulfillment, although none have qualitatively explored the factors correlated to automobile dealerships businesses in Bosnia and Herzegovina. The aim of this qualitative study in this regard is to study some of the factors that influence customer loyalty in car dealerships. from the dealer principals’ point of view. We collected data from more than 70 dealerships around Bosnia and Herzegovina. It has been observed that 5 different factors have influenced customer loyalty significantly. Besides, physical factors related with facility, customer-oriented factors, product and services-oriented factors, finance-oriented factors and marketing presence was taken over. Customer loyalty is objectively important for strategic marketing planning and represents an important basis for developing a sustainable competitive advantage (Kahreh & Kahreh, 2012). In general, customer loyalty is a favorable attitude from individuals towards repeat purchasing of a company’s products over competitors (Oliver, 2010). The ability to retain customers and make them loyal is critical for continued organizational success (Mohd, Mokhtar, & Yusr, 2016).
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