Evaluating and Forecasting the Probability of Lightning Occurrence in Rasht City

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GHASEMI AFSANEH; AMANOLLAHI JAMIL

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Journal: Geography and Environmental Sustainability Year:2020 | Volume:10 | Issue:34 Page(s): 21-35

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Title

Evaluating and Forecasting the Probability of Lightning Occurrence in Rasht City

Author(s)

GHASEMI AFSANEH | AMANOLLAHI JAMIL | Issue Writer Certificate

Keywords

LightningQ1

PredictionQ2

RashtQ1

Data Mining ModelsQ1

Comparative CriteriaQ1

Abstract

Lightning is one of the most severe weather hazards that will cause significant economic, social and environmental damage each year. The Prediction of a Lightning is a very difficult task due to the spatial and temporal expansion of weather either physically or dynamically. Therefore, timely forecasting of Lightning and evaluation of the best data mining model is effective in reducing damage. In this research, the data of the years 2012_2018 of the Meteorological Station of Rasht were used, including dependent variable of occurrence and non-occurrence of Lightning during 7 years and independent variables of factors affecting Lightning including temperature, relative humidity, cloudy, wind speed, wind direction, pressure air and Previous day's Lightning. After preprocessing and processing data, Data Mining Models including Classification & Regression Tree (CART), Chi-squared Automatic Interaction Detector (CHAID), Induction of Decision Trees (C5) and neural networks Radial Basis Function (RBF), Multi Layer Perceptron (MLP) and Support Vector Machine (SVM) were used in SPSS Modeler Ver 20 software. The results of the models were compared with the Comparative Criteria and the Receiver operating characteristic (ROC) curve. According to the results of the models, the probability of Lightning occurrence is higher in the months of May, June and July than in other months and the rate of occurrence from spring to winter has a decreasing trend, while in winter it is at least. CHAID tree with a specificity rate of 0. 794 and a minimum false positive rate of 0. 205 and the SVM model with a correct Prediction of 0. 773 and an error rate of 0. 475 and precision of 0. 855 have optimum performance compared with other models.

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APA: Copy

GHASEMI, AFSANEH, & AMANOLLAHI, JAMIL. (2020). Evaluating and Forecasting the Probability of Lightning Occurrence in Rasht City. GEOGRAPHY AND ENVIRONMENTAL SUSTAINABILITY, 10(34 ), 21-35. SID. https://sid.ir/paper/365045/en

Vancouver: Copy

GHASEMI AFSANEH, AMANOLLAHI JAMIL. Evaluating and Forecasting the Probability of Lightning Occurrence in Rasht City. GEOGRAPHY AND ENVIRONMENTAL SUSTAINABILITY[Internet]. 2020;10(34 ):21-35. Available from: https://sid.ir/paper/365045/en

IEEE: Copy

AFSANEH GHASEMI, and JAMIL AMANOLLAHI, “Evaluating and Forecasting the Probability of Lightning Occurrence in Rasht City,” GEOGRAPHY AND ENVIRONMENTAL SUSTAINABILITY, vol. 10, no. 34 , pp. 21–35, 2020, [Online]. Available: https://sid.ir/paper/365045/en

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