CRITICAL ENVIRONMENTAL SITUATION IN THE MIDDLE REACHES OF THE YELLOW RIVER IN CHINA

This paper researches and analyses the critical envirormental situation in the Middle Reaches of the Yellow River and compiles the map of critical environmental situation of this area (1:2,000,000). Five types of environmental situation (ES) are divided, namely, conflict ES, critical ES, crisis ES, disastrous ES and accidental ES and 7 groups of main factors are used to identify and classify the critical environmental situation after considering the speciality of this region and the law of guiding factors. They are pollution, endemic disease, soil erosion, drought and water-shortage, forest degeration, wind-erosion and desertification, and soil salinization. Based on mapping and analysis, the paper also concludes the regional distribution law of critical environmental situation of this region and divides it into 8 small districts through combining the critical envirormental situation, regional distribution law and guiding factors. This can provide scientific basis and reference for preserving and renovating the environments with different fragile types and fragile levels.

Deep learning-based method for detecting anomalies in electromagnetic environment situation

The anomaly detection of electromagnetic environment situation(EMES) has essential reference value for electromagnetic equipment behavior cognition and battlefield threat assessment.In this paper,we proposed a deep learning-based method for detecting anomalies in EMES to address the problem of relatively low efficiency of electromagnetic environment situation anomaly detection(EMES-AD).Firstly,the convolutional kernel extracts the static features of different regions of the EMES.Secondly,the dynamic features of the region are obtained by using a recurrent neural network(LSTM).Thirdly,the Spatio-temporal features of the region are recovered by using a de-convolutional network and then fused to predict the EMES.The structural similarity algorithm(SSIM) is used to determine whether it is anomalous.We developed the detection framework,de-signed the network parameters,simulated the data sets containing different anomalous types of EMES,and carried out the detection experiments.The experimental results show that the proposed method is effective.

Applied machine learning in greenhouse simulation;new application and analysis

Prediction the inside environment variables in greenhouses is very important because they play a vital role in greenhouse cultivation and energy lost especially in cold and hot regions.The greenhouse environment is an uncertain nonlinear system which classical modeling methods have some problems to solve it.So the main goal of this study is to select the best method between Artificial Neural Network(ANN)and Support Vector Machine(SVM)to estimate three different variables include inside air,soil and plant temperatures(Ta,Ts,Tp)and also energy exchange in a polyethylene greenhouse in Shahreza city,Isfahan province,Iran.The environmental factors which influencing all the inside temperatures such as outside air temperature,wind speed and outside solar radiation were collected as data samples.In this research,13 different training algorithms were used for ANN models(MLPRBF).Based on K-fold cross validation and Randomized Complete Block(RCB)methodology,the best model was selected.The results showed that the type of training algorithm and kernel function are very important factors in ANN(RBF and MLP)and SVM models performance,respectively.Comparing RBF,MLP and SVM models showed that the performance of RBF to predict Ta,Tp and Ts variables is better according to small values of RMSE and MAPE and large value of R2 indices.The range of RMSE and MAPE factors for RBF model to predict Ta,Tp and Ts were between 0.07 and 0.12
C and 0.28-0.50%,respectively.Generalizability and stability of the RBF model with 5-fold cross validation analysis showed that this method can use with small size of data groups.The performance of best model(RBF)to estimate the energy lost and exchange in the greenhouse with heat transfer models showed that this method can estimate the real data in greenhouse and then predict the energy lost and exchange with high accuracy.