scholarly journals Temperature Trend Analysis and Extreme High Temperature Prediction Based on Weighted Markov Model in Lanzhou

2021 ◽  
Author(s):  
Zhiqiang Pang ◽  
Zhaoxu Wang
Keyword(s):  
High Temperature ◽  
Markov Model ◽  
Prediction Model ◽  
Trend Analysis ◽  
High Temperatures ◽  
Minimum Temperature ◽  
Maximum Temperature ◽  
Temperature Prediction ◽  
Real Situation ◽  
Change Trend

Abstract In this study, temporIn this study, temporal trend analysis was conducted on the annual and quarterly meteorological variables of Lanzhou from 1951 to 2016, and a weighted Markov model for extremely high-temperature prediction was constructed. Several non-parametric methods were used to analyze the time trend. Considering that sequence autocorrelation may affect the accuracy of the trend test, we performed an autocorrelation test and carried out trend analysis for sequences with autocorrelation after removing correlation. The results show that the maximum temperature, minimum temperature, and average temperature in Lanzhou have a significant rising trend and show different performances in each season. In detail, the maximum temperature in summer does not have a significant change trend, while the minimum temperature in winter is the most significant rising trend, which leads to more and more ”warm winter” phenomenon. Finally, we construct a weighted Markov prediction model for extremely high temperatures and obtain the conclusion that the prediction results by the model are consistent with the real situation. and show different performances in each season. In detail, the maximum temperature in summer does not have a significant change trend, while the minimum temperature in winter is the most significant rising trend, which leads to more and more ”warm winter” phenomenon. Finally, we construct a weighted Markov prediction model for extremely high temperatures and obtain the conclusion that the prediction results by the model are consistent with the real situation.

The influence of pasture distribution and temperature on habitat selection by feral pigs in a semi-arid environment

1998 ◽  
Vol 25 (5) ◽  
pp. 547 ◽  
Author(s):  
Nick Dexter
Keyword(s):  
High Temperature ◽  
High Temperatures ◽  
Radio Telemetry ◽  
Arid Environment ◽  
Food Distribution ◽  
Maximum Temperature ◽  
Feral Pigs ◽  
North West ◽  
Hot Weather ◽  
Two Parameters

The two parameters believed to influence habitat utilisation by feral pigs and wild boar (Sus scrofa) are protection from high temperatures and distribution of food. However, whether there is an interaction between these parameters is unknown. To examine the influence of high temperature on habitat utilisation, the use of four rangeland habitats (shrubland, woodland, riverine woodland, and ephemeral swamps) by feral pigs in north-west New South Wales, Australia, was measured by radio-telemetry during and after a drought. In each habitat, protection from high temperature was indexed once by vegetation cover, at three strata, while over the course of the study, food distribution was indexed by estimating pasture biomass in each habitat. Riverine woodland provided the most shelter from high temperature, followed by woodland, shrubland and ephemeral swamps. On average, ephemeral swamps had the highest pasture biomass, followed by riverine woodland, shrubland and woodland. The amount of pasture in each habitat increased after the drought but changed at different rates. During autumn, spring and summer feral pigs preferred riverine woodland but in winter shrubland was preferred. Multivariate regression indicated that habitat utilisation was significantly influenced by pasture biomass in shrubland and mean maximum temperature in the study area. The results suggest that feral pigs are restricted by high temperatures to more shady habitats during hot weather but when the constraint of high temperature is relaxed they distribute themselves more according to the availability of food.

Homogeneity Evaluation of Historical Rainfall and Temperature Series in Mato Grosso

2019 ◽  
pp. 1-7
Author(s):  
Elizangela Selma da Silva ◽  
José Holanda Campelo Júnior ◽  
Francisco De Almeida Lobo ◽  
Ricardo Santos Silva Amorim
Keyword(s):  
Trend Analysis ◽  
Minimum Temperature ◽  
Statistical Tests ◽  
Kendall Test ◽  
Maximum Temperature ◽  
Data Series ◽  
Positive Trend ◽  
Mato Grosso ◽  
Temperature Climate ◽  
Precipitation And Temperature

The homogeneity investigation of a series can be performed through several nonparametric statistical tests, which serve to detect artificial changes or non-homogeneities in climatic variables. The objective of this work was to evaluate two methodologies to verify the homogeneity of the historical climatological series of precipitation and temperature in Mato Grosso state. The series homogeneity evaluation was performed using the following non-parametric tests: Wald-Wolfowitz (for series with one or no interruption), Kruskal-Wallis (for series with two or more interruptions), and Mann-Kendall (for time series trend analysis). The results of the precipitation series homogeneity analysis from the National Waters Agency stations, analyzed by the Kruskal-Wallis and Wald-Wolfowitz tests, presented 61.54% of homogeneous stations, being well distributed throughout Mato Grosso state, whereas those of the trend analysis allowed to identify that 87.57% of the rainfall-gauging stations showed a concentrated positive trend, mainly in the rainy season. Out of the conventional stations of the National Institute of Meteorology of Mato Grosso, seven were homogeneous for the precipitation variable, five for maximum temperature and four stations were homogeneous for minimum temperature. For the trend analysis in the 11 stations, positive trends of random nature were observed, suggesting increasing alterations in the analyzed variables. Therefore, the trend analysis performed by the Mann-Kendall test in the precipitation, and maximum and minimum temperature climate series, indicated that several data series showed increasing trends, suggesting a possible increase in precipitation and temperature values over the years. The results of the Kruskal-Wallis and Wald-Wolfowitz tests for homogeneity presented more than 87% of homogeneous stations.

scholarly journals A Comparative Study of Ensemble Classifiers for Paddy Blast Disease Prediction Model

2020 ◽  
Vol 8 (7) ◽  
pp. 09-17
Author(s):  
Varsha M., Dr. Poornima B.
Keyword(s):  
Prediction Model ◽  
Minimum Temperature ◽  
Blast Disease ◽  
Weather Data ◽  
Maximum Temperature ◽  
Research Station ◽  
Disease Prediction ◽  
Features Selection ◽  
Ensemble Classifiers ◽  
Epidemic Disease

Paddy blast has become most epidemic disease in many rice growing countries. Various statistical methods have been used for the prediction of paddy blast but previously used methods failed in predicting diseases with good accuracy. However the need to develop new model that considers both weather factors and non weather  data called blast disease data that influences paddy disease to grow. Given this point we developed ensemble classifier based paddy disease prediction model taking weather data from January 2013 to December 2019 from Agricultural and Horticulture Research Station Kathalgere Davangere District. For the predictive model we collected 7 kinds of weather data and 7 kinds of disease related data that includes Minimum Temperature, Maximum Temperature, Temperautre Difference,Relative Humidity, Stages of Paddy Cultivation, Varities of seeds, Season of cropping and so on. It is observed and analyzed that Minimum Temperature, Humidity and Rainfall has huge correlation with occurrence of disease. Since some of the variables are non numeric to convert them to numeric data one hot encoding approach is followed and to improve efficiency of ensemble classifiers  4 different filter based features selection methods are used such as Pearson’s correlation, Mutual information, ANNOVA F Value, Chi Square. Three different ensemble classifiers are used as predictive models and classifiers are compared it is observed that Bagging ensemble technique has achieved  accuracy of 98% compared to Adaboost of 97% and Voting classifier of 88%. Other classification metrics are used evaluate different classifiers like precision, recall, F1 Score, ROC and precision recall score. Our proposed ensemble classifers for paddy blast disease prediction has achieved high precision and high recall but when the solutions of model are closely looked bagging classifier is better compared to other ensemble classifers that are proposed in predicting paddy blast disease.

scholarly journals Growth Performance and Concentrations of Thyroid Hormones and Growth Hormone in Plasma of Broilers at High Temperatures

1987 ◽  
Vol 40 (4) ◽  
pp. 443 ◽  
Author(s):  
A P Sinurat ◽  
D Balnave ◽  
GH McDowell
Keyword(s):  
Growth Hormone ◽  
High Temperature ◽  
Plasma Concentration ◽  
High Temperatures ◽  
Broiler Chickens ◽  
High Ambient Temperature ◽  
Molar Ratio ◽  
Maximum Temperature ◽  
Food Conversion ◽  
Plasma Growth Hormone

Responses of broiler chickens to a high ambient temperature (35�C) were measured in two experiments. In one experiment temperatures were increased abruptly from 21�C to a daily range of 21-35�C whereas, in the other, temperatures were increased more gradually over 6 days. The high temperatures were maintained for 5 h/day. In' both experiments, birds exposed to the high temperatures ate less food and gained less liveweight than birds maintained at 21�C. Efficiency of food conversion to liveweight gain and body composition were not affected by high temperature but there was a tendency for thyroid weight to decrease. Overall, the plasma concentration of triiodothyronine (T 3) decreased and the plasma concentration of thyroxine (T4) increased, resulting in a decreased T/T4 molar ratio, during exposure to high temperature. The concentration of plasma growth hormone, but not plasma reverse T 3' was increased by high temperature. The initial responses to increased temperature were variable, with birds exposed more gradually adjusting relatively well until the maximum temperature was increased to 35�C. All heated birds readjusted quickly to the daily reduction in temperature to 21�C.

scholarly journals Long-Term Trend Analysis in Annual and Seasonal Precipitation, Maximum and Minimum Temperatures in the Southwest United States

Climate ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 142
Author(s):  
Koffi Djaman ◽  
Komlan Koudahe ◽  
Ansoumana Bodian ◽  
Lamine Diop ◽  
Papa Malick Ndiaye
Keyword(s):  
United States ◽  
Trend Analysis ◽  
Minimum Temperature ◽  
Test Method ◽  
Maximum Temperature ◽  
Crop Season ◽  
Southwest United States ◽  
Annual Minimum Temperature ◽  
Increasing Trend

The objective of this study is to perform trend analysis in the historic data sets of annual and crop season [May–September] precipitation and daily maximum and minimum temperatures across the southwest United States. Eighteen ground-based weather stations were considered across the southwest United States for a total period from 1902 to 2017. The non-parametric Mann–Kendall test method was used for the significance of the trend analysis and the Sen’s slope estimator was used to derive the long-term average rates of change in the parameters. The results showed a decreasing trend in annual precipitation at 44.4% of the stations with the Sen’s slopes varying from −1.35 to −0.02 mm/year while the other stations showed an increasing trend. Crop season total precipitation showed non-significant variation at most of the stations except two stations in Arizona. Seventy-five percent of the stations showed increasing trend in annual maximum temperature at the rates that varied from 0.6 to 3.1 °C per century. Air cooling varied from 0.2 to 1.0 °C per century with dominant warming phenomenon at the regional scale of the southwest United States. Average annual minimum temperature had increased at 69% of the stations at the rates that varied from 0.1 to 8 °C over the last century, while the annual temperature amplitude showed a decreasing trend at 63% of stations. Crop season maximum temperature had significant increasing trend at 68.8% of the stations at the rates varying from 0.7 to 3.5 °C per century, while the season minimum temperature had increased at 75% of the stations.

High-temperature survival is limited by food availability in first-instar locust nymphs

2010 ◽  
Vol 58 (5) ◽  
pp. 323 ◽  
Author(s):  
James D. Woodman
Keyword(s):  
High Temperature ◽  
Food Availability ◽  
High Temperatures ◽  
Water Loss ◽  
Local Population ◽  
Maximum Temperature ◽  
Upper Limit ◽  
First Instar ◽  
Low Humidity ◽  
Very High

The Australian plague locust, Chortoicetes terminifera (Walker), is often exposed to high temperature and low humidity in semiarid and arid environments. Early-instar survival under these conditions is an important prerequisite for the formation of high-density aggregations in summer and autumn generations. The present study investigates how first-instar C. terminifera respond to high temperature and low humidity using measures of total body water content, physiological and behavioural transitions during temperature increase, critical upper limit, and mortality relative to food availability. The critical upper limit for fed nymphs was very high at 53.3 ± 1.0°C, with death preceded by a clear progression of changes in behaviour, gas exchange, water loss and excretion. At more ecologically relevant high temperatures, food availability allowed nymphs to behaviourally respond to increased water loss, and the resulting physiological maintenance of water reserves provided cross-tolerance to heat relative to exposure duration and maximum temperature as well as the rate of warming. While very high mortality was recorded at ≥45°C in 6-h direct-exposure experiments, a highly exposed and very poorly vegetated summer environment would be required for local population failures from current high temperatures and low humidity alone.

scholarly journals LightGBM Low-Temperature Prediction Model Based on LassoCV Feature Selection

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Shangqi Duan ◽  
Shuangde Huang ◽  
Wei Bu ◽  
Xingke Ge ◽  
Haidong Chen ◽  
...  
Keyword(s):  
Feature Selection ◽  
Low Temperature ◽  
Prediction Model ◽  
Minimum Temperature ◽  
Power Grid ◽  
Daily Minimum Temperature ◽  
Short Term ◽  
Temperature Prediction ◽  
Data Set ◽  
Daily Minimum

Icing disasters on power grid transmission lines can easily lead to major accidents, such as wire breakage and tower overturning, that endanger the safe operation of the power grid. Short-term prediction of transmission line icing relies to a large extent on accurate prediction of daily minimum temperature. This study therefore proposes a LightGBM low-temperature prediction model based on LassoCV feature selection. A data set comprising four meteorological variables was established, and time series autocorrelation coefficients were first used to determine the hysteresis characteristics in relation to the daily minimum temperature. Subsequently, the LassoCV feature selection method was used to select the meteorological elements that are highly related to minimum temperature, with their lag characteristics, as input variables, to eliminate noise in the original meteorological data set and reduce the complexity of the model. On this basis, the LightGBM low-temperature prediction model is established. The model was optimized through grid search and crossvalidation and validated using daily minimum surface temperature data from Yongshan County (station number 56489), Zhaotong City, Yunnan Province. The root mean square error, MAE, and MAPE of the model minimum temperature prediction after feature selection are shown to be 1.305, 0.999, and 0.112, respectively. These results indicate that the LightGBM prediction model is effective at predicting low temperatures and can be used to support short-term icing prediction.

scholarly journals A Comparative Study of Ensemble Classifiers for Paddy Blast Disease Prediction Model

2020 ◽  
Vol 8 (9) ◽  
pp. 05-13
Author(s):  
Varsha M., Dr. Poornima B.
Keyword(s):  
Prediction Model ◽  
Minimum Temperature ◽  
Blast Disease ◽  
Weather Data ◽  
Maximum Temperature ◽  
Research Station ◽  
Disease Prediction ◽  
Features Selection ◽  
Ensemble Classifiers ◽  
Epidemic Disease

Paddy blast has become most epidemic disease in many rice growing countries. Various statistical methods have been used for the prediction of paddy blast but previously used methods failed in predicting diseases with good accuracy. However the need to develop new model that considers both weather factors and non weather  data called blast disease data that influences paddy disease to grow. Given this point we developed ensemble classifer based paddy disease prediction model taking weather data from January 2013 to December 2019 from Agricultural and Horticulture Research Station Kathalgere Davangere District. For the predictive model we collected 7 kinds of weather data and 7 kinds of disease related data that includes Minimum Temperature, Maximum Temperature, Temperautre Difference,Relative Humidity, Stages of Paddy Cultivation, Varities of seeds, Season of cropping and so on. It is observed and analyzed that Minimum Temperature, Humidity and Rainfall has huge correlation with occurrence of disease. Since some of the variables are non numeric to convert them to numeric data one hot encoding approach is followed and to improve efficiency of ensemble classifiers  4 different filter based features selection methods are used such as Pearson’s correlation, Mutual information, ANNOVA F Value, Chi Square. Three different ensemble classifiers are used as predictive models and classifiers are compared it is observed that Bagging ensemble technique has achieved  accuracy of 98% compared to Adaboost of 97% and Voting classifier of 88%. Other classification metrics are used evaluate different classifiers like precision, recall, F1 Score, ROC and precision recall score. Our proposed ensemble classifers for paddy blast disease prediction has achieved high precision and high recall but when the solutions of model are closely looked bagging classifier is better compared to other ensemble classifers that are proposed in predicting paddy blast disease.

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