Deep Learning Framework for Precipitation Prediction Using Cloud Images

作     者：Mirza Adnan Baig Ghulam Ali Mallah Noor Ahmed Shaikh 

作者机构：Shah Abdul Latif UniversityKhairpur77150Pakistan 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第72卷第8期

页      面：4201-4213页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Machine vision SIFT features dark cloud patterns precipitation agriculture 

摘      要：Precipitation prediction(PP)have become one of the significant research areas of deep learning(DL)and machine vision(MV)techniques are frequently used to predict the weather variables(WV).Since the climate change has left significant impact upon weather variables(WV)and continuously changes are observed in temperature,humidity,cloud patterns and other *** cloud images contain sufficient information to predict the precipitation pattern but due to changes in climate,the complex cloud patterns and rapid shape changing behavior of clouds are difficult to consider for rainfall *** of rainfall would provide more meticulous assistance to the farmers to know about the weather conditions and to care their cash *** research proposes a framework to classify the dark cloud patterns(DCP)for prediction of *** framework consists upon three steps to classify the cloud images,first step tackles noise reduction operations,feature selection and preparation of *** step construct the decision model by using convolutional neural network(CNN)and third step presents the performance visualization by using confusion matrix,precision,recall and accuracy *** research contributes(1)real-world clouds datasets(2)method to prepare datasets(3)highest classification accuracy to predict estimated as 96.90%.