تحلیل شرایط آب و هوایی با یادگیری عمیق مبتنی بر انتخاب ویژگی با الگوریتم یادگیری کلاغ

الگوریتم‌های فراابتکاری روش‌های حل مسئله‌ای است که از رویداد‌های موجود در طبیعت و یا رفتار جانداران الگوبرداری شده است. در این الگوریتم‌ها شیوه‌های حل مسئله در جانداران مورد مدلسازی و الگوبرداری قرار گرفته شده است تا بتوان راه‌حلهای بهینه را استخراج نمود. الگوریتم‌های فراابتکاری در زمبنه‌های مختلف دارای کاربرد می‌باشند که یکی از آنها بهینه‌سازی پارامترهای یادگیری ماشین و یادگیری عمیق است. شبکه‌های عصبی یادگیری عمیق کاربردهای زیادی در موضوعات مختلف مانند پیش بینی، طبقه‌بندی و تشخیص الگو دارند. یکی از کاربردهای مهم شبکه‌های عصبی یادگیری عمیق، موضوع پیش بینی وضعیت آب و هوایی است. شبکه عصبی lstm یک روش یادگیری عمیق است که می‌تواند برای تشخیص وضعیت آب و هوایی استفاده شود. در لایه اول شبکه یادگیری عمیق lstm، از انتخاب ویژگی خودکار و در لایه آخر فاز طبقه‌بندی خودکار انجام می‌شود. در این مقاله برای کاهش دادن خطای پیش بینی و طبقه‌بندی شبکه یادگیری عمیق lstm یک رویکرد دو مرحله‌ای برای بهبود این شبکه یادگیری عمیق ارایه می‌شود. در فاز اول از الگوریتم یادگیری کلاغ برای انتخاب ویژگی در لایه اول شبکه lstm استفاده می‌شود تا یادگیری روی ویژگی‌های مهم متمرکز شود. ارزیابی‌ها نشان داد دقت روش پیشنهادی در پیش بینی وضعیت آب و هوایی برابر 96.92% است و این در حالی است که اگر برای پیش بینی از انتخاب ویژگی استفاده نشود و فقط از شبکه یادگیری عمیق استفاده شود آنگاه دقت روش پیشنهادی در حدود 93.21% است. ارزیابی‌ها نشان می‌دهد دقت روش پیشنهادی برای پیش بینی وضعیت آب و هوایی از روش lstm و mlp بیشتر است.

weather analysis with deep learning based on feature selection with crow learning algorithm

deep learning neural networks have many applications in various topics such as prediction, classification and pattern recognition. lstm neural network is a deep learning method that can be used to detect weather conditions. in the first phase, the crow’s learning algorithm is used to select the feature in the first layer of the lstm network. the evaluations showed that the accuracy of the proposed method is 96.92% and without using the feature selection, it is about 93.21%. the evaluations show that the accuracy of the proposed method for forecasting the weather condition is higher than lstm and mlp methods.keywords: deep learning, lstm network, crow learning algorithm, weather forecasting1. introductionmeta-heuristic algorithms are problem solving methods that are modeled on the events in nature or the behavior of living beings so that optimal solutions can be extracted. collective intelligence algorithms [1] are a kind of meta-heuristic algorithms that are modeled on the behavior of living beings that live in a group and social life, such as hunting behavior, hyena optimization algorithm, whale optimization algorithm, etc. is. meta-heuristic algorithms can be divided into different categories based on the method of problem solving, one of which is shown in the research [2] in 2020 according to the diagram in figure (1) and can be seen. meta-heuristic algorithms are divided into 4 different groups and categories based on their performance:figure 1: classification of meta-heuristic algorithms into different categories [2]meta-heuristic algorithms are used in various fields, one of which is the optimization of machine learning and deep learning parameters. one of the applications of machine learning and deep learning is in weather forecasting. in this article, to improve the accuracy of the lstm network, the optimization of important features using the learning method in crows has been used.2. lstm learning networkin the short term, long memory neural networks are actually a type of recurrent neural networks [3].in the lstm network, with the help of the sigmoid function that is applied element by element, the input, forgetting and output gate layers produce vectors whose all dimensions are between zero and one or close to both. the general structure of lstm deep learning neural network is as shown in figure (2):figure 2. the structure of long memory networks, in the short term3. proposed modelfigure (3). the framework of the proposed method is shown.the evaluation or minimization function of the following two factors shows how well a feature vector has competence:• average prediction error with neural network• number of features selectedthe calculation of error index e is as follows:the population of crows is stored in a matrix:each crow needs to remember the most optimal position:in the crow learning algorithm, there are two phases of horizontal and vertical learning. vertical learning from parents is horizontal learning from brothers and sisters.it is used to select a sister or vector randomly.6 k= 3+[rand×(i-3)] i≥3in the crow’s learning algorithm, the probability of receiving a reward for crows is equal to rpprob.lf is the value of the learning factor in crows.amount of reward for crows:reinforcement of learning for parents is used in the crow algorithm as follows.it is used to search for food with the stealing mechanism as follows:4-implementation and analysis4-1-implementation parameterstable (1): implementation parameters of the proposed methodfigure 4: lstm implementation parameters in the proposed method4-2-evaluation indicesone of the important indicators for predicting weather conditions is the mean squared error mse index, and to evaluate the proposed method, you can use the classification and prediction indicators of accuracy, recall and accuracy:4-3- analysis of the proposed methodin the diagram of figure (5), the prediction error in the feature selection phase in combination with the neural network is shown, and in figure (6), the output of lstm deep learning in weather forecasting is depicted.figure 5: reduction of prediction error in feature selection phase with 10 iterationsfigure 6: reducing the prediction error in the classification phase with lstmtable 2: average prediction indices of the proposed methodfigure 7: comparison of the mse error of the proposed method with prediction methodsfigure 8: comparison of the accuracy of the proposed method with prediction methodsfigure 9: comparison of recall of the proposed method with prediction methodsfigure 10: comparing the precision of the proposed method with prediction methodsfigure 11: comparison of the accuracy index of the proposed method in weather forecasting5. conclusionlstm network is a deep learning method that can be used to predict weather conditions. in the proposed method to increase the prediction accuracy of lstm neural network, intelligent feature selection is used using a combination of crow learning algorithm and crow search. experiments showed that the proposed method has an accuracy of 96.92%, a sensitivity of 95.82%, and an accuracy of 96.34%, and it is more accurate for predicting weather conditions than multilayer neural network, recurrent neural network, and lstm method.reference:[1] xue, j., shen, b. (2020). a novel swarm intelligence optimization approach: sparrow search algorithm. systems science control engineering, 8(1), 22-34.[2] li, s., chen, h., wang, m., heidari, a. a., mirjalili, s. (2020). slime mould algorithm: a new method for stochastic optimization. future generation computer systems.[3] fu, j., chu, j., guo, p., chen, z. (2019). condition monitoring of wind turbine gearbox bearing based on deep learning model. ieee access, 7, 57078-57087.[4] sampaio, p. s., almeida, a. s., brites, c. m. (2021). use of artificial neural network model for rice quality prediction based on grain physical parameters. foods, 10(12), 3016.