A forest fire is a natural disaster that destroys forest resources, thus having a severe impact on humans and on the animals and plants that depend on the forest environment. This paper presents a model for predicting the scale of forest wildfires of Alberta, Canada. A fire's scale is determined by the combination of the fire's duration and the size of the area it burns. Our prediction model enables fire rescuers to take appropriate measures to minimize damage caused by a wildfire based on its predicted scale in the fire's early stages. The modeling data were collected from the Canada National Fire Database (CNFDB) published by Natural Resources Canada, which includes wildfire and meteorological data for Alberta, Canada. The size of the burned area and the fire's duration were used to estimate the scale of a wildfire. After multi-collinearity testing and feature normalization, the data were divided into training and testing sets. Taking the meteorological factors as input values, a backpropagation neural network (BPNN), a recurrent neural network (RNN), and long short-term memory (LSTM) were implemented to establish prediction models. Of these classification methods, LSTM exhibited the highest accuracy, 90.9%. The results indicate that it is feasible to predict the scale of a forest wildfire at the beginning of its occurrence using meteorological information.

To achieve a rational allocation of limited water resources, and formulation of an appropriate irrigation system, this research studied the change characteristics of stem water content (StWC) in plant and its response to environmental factors. In this study, the StWC and environmental factors of Lagerstroemia indica in Beijing were continuously observed by a BD-IV plant stem water content sensor and a forest microclimate monitoring station from 2017 to 2018. The variation of StWC and its correlation with environmental factors were analyzed. The results showed that the StWC of Lagerstroemia indica varies regularly day and night during the growth cycle. Meanwhile, the rising time, valley time, and falling time of StWC were various at the different growth stages of Lagerstroemia indica. The results of correlation analysis between StWC and environmental factors indicated that the StWC of Lagerstroemia indica was positively correlated with air relative humidity, while it was negatively correlated with total radiation and air temperature. The multiple regression equation of StWC and environmental factors of Lagerstroemia indica was StWC=11.789−1.402Rn−0.931T−1.132Ws+0.933RH−3.368ST+2.168SMC, and the coefficient of determination of the equation was of 0.87. Furthermore, the results illustrated that the irrigation should pay attention to supplementing irrigation in time during the peak growing season of fruit.

To achieve a rational allocation of limited water resources, and formulation of an appropriate irrigation system, this research studied the change characteristics of stem water content (StWC) in plant and its response to micro-environmental factors. In this study, the StWC and micro-environmental factors of Lagerstroemia indica in Beijing were continuously observed by BD-IV plant stem water content sensor and a forest microclimate monitoring station from 2017 to 2018. The variation of StWC and its correlation with environmental factors were analyzed. The results showed the StWC of Lagerstroemia indica varies regularly day and night during the growth cycle. Meanwhile, the rising time, valley time, and falling time of StWC were various at the different growth stages of Lagerstroemia indica. The results of correlation analysis between StWC and micro-environmental factors indicated that the StWC of Lagerstroemia indica was positively correlated with air relative humidity, while it was negatively correlated with total radiation and air temperature. The multiple regression equation of StWC and micro-environmental factors of Lagerstroemia indica was StWC = 11.789-1.402Rn-0.931T-1.132Ws+0.933RH-3.368ST+2.168SMC, and the coefficient of determination of the equation was of 0.87. Furthermore, the results illustrated that the irrigation should pay attention to supplementing irrigation in time during the peak growing season of fruit.

To achieve a rational allocation of limited water resources, and formulation of an appropriate irrigation system, this research studied the change characteristics of stem water content (StWC) in plant and its response to micro-environmental factors. In this study, the StWC and micro-environmental factors of Lagerstroemia indica in Beijing were continuously observed by BD-IV plant stem water content sensor and a forest microclimate monitoring station from 2017 to 2018. The variation of StWC and its correlation with environmental factors were analyzed. The results showed the StWC of Lagerstroemia indica varies regularly day and night during the growth cycle. Meanwhile, the rising time, valley time, and falling time of StWC were various at the different growth stages of Lagerstroemia indica. The results of correlation analysis between StWC and micro-environmental factors indicated that the StWC of Lagerstroemia indica was positively correlated with air relative humidity, while it was negatively correlated with total radiation and air temperature. The multiple regression equation of StWC and micro-environmental factors of Lagerstroemia indica was StWC = 11.789-1.402Rn-0.931T-1.132Ws+0.933RH-3.368ST+2.168SMC, and the coefficient of determination of the equation was of 0.87. Furthermore, the results illustrated that the irrigation should pay attention to supplementing irrigation in time during the peak growing season of fruit.

To achieve a rational allocation of limited water resources, and formulation of an appropriate irrigation system, this research studied the change characteristics of stem water content (StWC) in plant and its response to micro-environmental factors. In this study, the StWC and micro-environmental factors of Lagerstroemia indica in Beijing were continuously observed by BD-IV plant stem water content sensor and a forest microclimate monitoring station from 2017 to 2018. The variation of StWC and its correlation with environmental factors were analyzed. The results showed the StWC of Lagerstroemia indica varies regularly day and night during the growth cycle. Meanwhile, the rising time, valley time, and falling time of StWC were various at the different growth stages of Lagerstroemia indica. The results of correlation analysis between StWC and micro-environmental factors indicated that the StWC of Lagerstroemia indica was positively correlated with air relative humidity, while it was negatively correlated with total radiation and air temperature. The multiple regression equation of StWC and micro-environmental factors of Lagerstroemia indica was StWC = 11.789-1.402Rn-0.931T-1.132Ws+0.933RH-3.368ST+2.168SMC, and the coefficient of determination of the equation was of 0.87. Furthermore, the results illustrated that the irrigation should pay attention to supplementing irrigation in time during the peak growing season of fruit.

To achieve a rational allocation of limited water resources, and formulation of an appropriate irrigation system, this research studied the change characteristics of stem water content (StWC) in plant and its response to micro-environmental factors. In this study, the StWC and micro-environmental factors of Lagerstroemia indica in Beijing were continuously observed by BD-IV plant stem water content sensor and a forest microclimate monitoring station from 2017 to 2018. The variation of StWC and its correlation with environmental factors were analyzed. The results showed the StWC of Lagerstroemia indica varies regularly day and night during the growth cycle. Meanwhile, the rising time, valley time, and falling time of StWC were various at the different growth stages of Lagerstroemia indica. The results of correlation analysis between StWC and micro-environmental factors indicated that the StWC of Lagerstroemia indica was positively correlated with air relative humidity, while it was negatively correlated with total radiation and air temperature. The multiple regression equation of StWC and micro-environmental factors of Lagerstroemia indica was StWC = 11.789-1.402Rn-0.931T-1.132Ws+0.933RH-3.368ST+2.168SMC, and the coefficient of determination of the equation was of 0.87. Furthermore, the results illustrated that the irrigation should pay attention to supplementing irrigation in time during the peak growing season of fruit.