AI powered solution for radio link failure prediction based on link features and weather forecast

Radio link sustainability gets affected by weather adversities such as snow, fog, cloud, rain, thunderstorm, etc. A proactive solution in radio failure scenarios is necessary to overcome economic loss and maintain the Quality of Service (QoS). To address issue, our work contributes towards building
 a machine-learning-based predict when generic regional forecast data, key performance indices spatial nature data are available. After rigorous preprocessing, ensembling models like logistic regression, random forest, light BGM, XGBoost gradient boosting classifiers were trained Link Failure (RLF) for two cases i.e., day-1-predict day-5-predict. Since it classification use case, metrics used precision, recall, F1 score. The classifier was better compared other with an score 0.95 both