Study on the Occurrence and Epidemic Regularity and Region Division of Rice Blast in Nanchong City

With the aim to improve the level of monitoring and warning as well as the comprehensive control of rice blast disease, and to feasibly reduce the disease threat in Nanchong City, the methods of GPS and GIS, systematical monitoring and field survey, rice blast resistance identification, physiologic races of rice blast monitoring, and meteorological data analysis were performed to study the occurrence and epidemic region division, precise demarcation and occurrence and epidemic regularity of rice blast in Nanchong City. This study first completed the epidemic region division and precise demarcation; first clarified the initial affection（beginning period） locations, occurrence characteristics, epidemic trends and characteristics; explicated the existence of four epidemic peak periods of rice blast in the field, where the damage areas of first peak period played a decisive role during the blast epidemic years; in late May, the cumulative occurrence areas and annual occurrence areas presented higher positive correlation with the correlation coefficient of 0.817;and established a prediction model of occurrence areas per year based on the disease field rate at the end of boot stages and the diseased plant rate at dough stages. The results of investigation on the impact factors investigation of blast disease in Nanchong in recent years suggested that the internal causes were the decrease or loss of blast resistance of the rice cultivars, as well as the increase of physiological races with strong resistance to rice blast and the emergence of new virulent physiologic varieties; the external causes were suitable temperature, too much rainy, and sunlight shortage. Between 2010 and 2015, the short-term forecast accuracy for rice blast in Nanchong was up to 100%, and medium-and long-term forecast accuracy was also up to 98% and 95%, respectively, which increased by 5-15% than that before 1997, thereby making the control effect of rice blast in Nanchong increased by 15-30%.

Landslide prediction based on improved principal component analysis and mixed kernel function least squares support vector regression model

Landslide probability prediction plays an important role in understanding landslide information in advance and taking preventive measures.Many factors can influence the occurrence of landslides,which is easy to have a curse of dimensionality and thus lead to reduce prediction accuracy.Then the generalization ability of the model will also decline sharply when there are only small samples.To reduce the dimension of calculation and balance the model’s generalization and learning ability,this study proposed a landslide prediction method based on improved principal component analysis(PCA)and mixed kernel function least squares support vector regression(LSSVR)model.First,the traditional PCA was introduced with the idea of linear discrimination,and the dimensions of initial influencing factors were reduced from 8 to 3.The improved PCA can not only weight variables but also extract the original feature.Furthermore,combined with global and local kernel function,the mixed kernel function LSSVR model was framed to improve the generalization ability.Whale optimization algorithm(WOA)was used to optimize the parameters.Moreover,Root Mean Square Error(RMSE),the sum of squared errors(SSE),Mean Absolute Error(MAE),Mean Absolute Precentage Error(MAPE),and reliability were employed to verify the performance of the model.Compared with radial basis function(RBF)LSSVR model,Elman neural network model,and fuzzy decision model,the proposed method has a smaller deviation.Finally,the landslide warning level obtained from the landslide probability can also provide references for relevant decision-making departments in emergency response.

Occurrence and Epidemic Regularity of Wheat Stripe Rust in Nanchong City of Sichuan Province

In order to realize monitoring and early warning and comprehensive management of wheat stripe rust and to reduce its occurrence in Nanchong City, the occurrence and epidemic regularity of wheat stripe rust in Nanchong was studied by system monitoring and general survey, resistance identification, physiological race monitoring and meteorological data analysis. The initial occurrence location and spreading pathway of Puccinia striiformis f. sp. tritici （Pst） were first verified; there were two infection peaks of wheat stripe rust in Nanchong and one to three epidemic peaks in fields, in which the occurrence area of the first epidemic peak played a pivotal role in disease prevalence in that year; the cumulative occurrence area in late January was positively correlated with annual occurrence area, with the correlation coefficient of 0.769 ; the prediction model for infected field rate, diseased plant rate and annual occurrence area was established. The internal reason for heavy occurrence and prevalence of wheat stripe rust in Nanchong was the decline or loss of wheat resistance against stripe rust, as well as the appearance of physiological races of Pst, which later became dominant races. Large fluctuation of temperature in warm winter and spring and more fog and dew days were external reasons responsible for prevalence of stripe rust. From 2002 to 2014 ,the accuracy rate of short-term prediction of wheat stripe rust reached 100%, while that of me- dium-term and long-term prediction reached over 98% and 95%, respectively, 5% -15% higher than that of the years before 1998.

A Systematic Study of Soil Collanses for Railway Slopes

A set of soil collapse prediction and prevention swtem for railway slopes is builtis this paper. Based on the field investisation, Oreen-Ampt model, the quantitytheory and computeraided decision-making sgutem, convereion tables ofworking rainfall ,grading tables of resistant ability to rainfall, and the warningrairifall levels are made, forming the chief part of a practical computer-aideddecisionmaking system. Usins the system, the danser degree of railway slopescan be predicted, and the reinforcins ensineerins and the flood control workcan also be arranged ratiofially.