Examination and evaluation of multi-source monthly scale fusion precipitation product in China based on machine learning algorithm

Grid format precipitation products have better spatial monitoring capabilities compared to ground meteorological station observations, but there are significant differences in performance among different products. This article evaluates the accuracy of nine monthly scale precipitation products TRMM, GPM, CMORPH, CHIRPS, ERA5, ERA5 Land, PERSIANN, PERSIANN-CDR, PERSIANN-CCS in China, and selects five better precipitation products from them. XGBoost is used to select the best precipitation products Three machine learning algorithms, random forest and multiple linear regression, were used for data fusion. Research has found that TRMM, GPM, CMORPH, CHIRPS, and PERSIANN-CDR products have relatively good accuracy. In high altitude and arid regions, the error of precipitation products significantly increases. After machine learning algorithm fusion, the optimal XGBoost algorithm model significantly improves product correlation coefficient, and significantly reduces root mean square error and bias. The three algorithms have shown good accuracy in each month, with XGBoost algorithm model products performing better in summer and random forest algorithm model products performing better in winter. Moreover, the three algorithm model products have shown high accuracy in different regions. Compared with the five original products before the fusion, the accuracy of the three algorithm model products has improved. The product fused with XGBoost algorithm has more variation and local precipitation details compared to the optimal original GPM product and meteorological station interpolation product in space

Study of Ammonia Concentration Characteristics and Optimization in Broiler Chamber during Winter Based on Computational Fluid Dynamics

Poultry breeding is one of the most significant components of agriculture and an essential link of material exchange between humans and nature. Moreover, poultry breeding technology has a considerable impact on the life quality of human beings, and could even influence the survival of human beings. As one of the most popular poultry, broiler has a good economic benefit due to its excellent taste and fast growing cycle. This paper aims to improve the efficiency of raising broilers by understanding the impact of ammonia concentration distribution within a smart broiler breeding chamber, and the rationality of the system’s design. More specifically, we used computational fluid dynamics (CFD) technology to simulate the process of ammonia production and identify the characteristics of ammonia concentration. Based on the simulation results, the structure of the broiler chamber was reformed, and the ammonia uniformity was significantly improved after the structural modification of the broiler chamber and the ammonia concentration in the chamber had remained extremely low. In general, this study provides a reference for structural optimization of the design of broiler chambers and the environmental regulation of ammonia

Medial reward and lateral non-reward orbitofrontal cortex circuits change in opposite directions in depression

The first brain-wide voxel-level resting state functional-connectivity neuroimaging analysis of depression is reported, with 421 patients with major depressive disorder and 488 controls. Resting state functional connectivity between different voxels reflects correlations of activity between those voxels and is a fundamental tool in helping to understand the brain regions with altered connectivity and function in depression. One major circuit with altered functional connectivity involved the medial orbitofrontal cortex BA 13, which is implicated in reward, and which had reduced functional connectivity in depression with memory systems in the parahippocampal gyrus and medial temporal lobe, especially involving the perirhinal cortex BA 36 and entorhinal cortex BA 28. The Hamilton Depression Rating Scale scores were correlated with weakened functional connectivity of the medial orbitofrontal cortex BA 13. Thus in depression there is decreased reward-related and memory system functional connectivity, and this is related to the depressed symptoms. The lateral orbitofrontal cortex BA 47/12, involved in non-reward and punishing events, did not have this reduced functional connectivity with memory systems. Second, the lateral orbitofrontal cortex BA 47/12 had increased functional connectivity with the precuneus, the angular gyrus, and the temporal visual cortex BA 21. This enhanced functional connectivity of the non-reward/punishment system (BA 47/12) with the precuneus (involved in the sense of self and agency), and the angular gyrus (involved in language) is thus related to the explicit affectively negative sense of the self, and of self-esteem, in depression. A comparison of the functional connectivity in 185 depressed patients not receiving medication and 182 patients receiving medication showed that the functional connectivity of the lateral orbitofrontal cortex BA 47/12 with these three brain areas was lower in the medicated than the unmedicated patients. This is consistent with the hypothesis that the increased functional connectivity of the lateral orbitofrontal cortex BA 47/12 is related to depression. Relating the changes in cortical connectivity to our understanding of the functions of different parts of the orbitofrontal cortex in emotion helps to provide new insight into the brain changes related to depression, which are considered in the Discussion

A simulation of traffic noise emissions at a roundabout based on a cellular automaton model

The calculation and evaluation of traffic noise is an important task in urban road design. Roundabouts are a common form of urban road intersection. The complexity of traffic operations makes the calculation of traffic noise near a roundabout challenging. To explore traffic noise at roundabouts, a cellular automaton traffic flow model for a two-lane roundabout is established. Based on this model, a dynamic simulation method for traffic noise at roundabouts is proposed. The traffic operation and noise emissions at a roundabout are simulated. The vehicle speed distribution and traffic noise distribution at the roundabout are analysed, and the relationship between the traffic volume and sound power level of the cells is discussed. Finally, the proposed method is compared with existing traffic noise models, and the accuracy and efficiency of the proposed method are verified. The results of this paper show that the speed distribution and noise emission distribution at the roundabout are not uniform. When the traffic volume increases to saturation, the noise emission on the ring road will not keep increasing, and the sound power level of the cells on the inner ring is approximately 2 dBA higher than that of the outer ring. The methods and results in this paper may be valuable for road traffic design and noise control

Research on the Influence of Small-Scale Terrain on Precipitation

Terrain plays an important role in the formation, development and distribution of local precipitation and is a major factor leading to locally abnormal weather in weather systems. Although small-scale topography has little influence on the spatial distribution of precipitation, it interferes with precipitation fitting. Due to the arbitrary combination of small, medium and large-scale terrain, complex terrain distribution is formed, and small-scale terrain cannot be clearly defined and removed. Based on the idea of bidimensional empirical mode decomposition (BEMD), this paper extracts small-scale terrain data layer by layer to smooth the terrain and constructs a macroterrain model for different scales in Central China. Based on the precipitation distribution model using multiple regression, precipitation models (B0, B1, B2 and B3) of different scales are constructed. The 18-year monthly average precipitation data of each station are compared with the precipitation simulation results under different scales of terrain and TRMM precipitation data, and the influence of different levels of small-scale terrain on the precipitation distribution is analysed. The results show that (1) in Central China, the accuracy of model B2 is much higher than that of TRMM model A and monthly precipitation model B0. The comprehensive evaluation indexes are increased by 3.31% and 1.92%, respectively. (2) The influence of different levels of small-scale terrain on the precipitation distribution is different. The first- and second-order small-scale terrain has interference effects on precipitation fitting, and the third-order small-scale terrain has an enhancement effect on precipitation. However, the effect of small-scale topography on the precipitation distribution is generally reflected as interference

Extracting Tea Plantations from Multitemporal Sentinel-2 Images Based on Deep Learning Networks

Tea is a special economic crop that is widely distributed in tropical and subtropical areas. Timely and accurate access to the distribution of tea plantation areas is crucial for effective tea plantation supervision and sustainable agricultural development. Traditional methods for tea plantation extraction are highly dependent on feature engineering, which requires expensive human and material resources, and it is sometimes even difficult to achieve the expected results in terms of accuracy and robustness. To alleviate such problems, we took Xinchang County as the study area and proposed a method to extract tea plantations based on deep learning networks. Convolutional neural network (CNN) and recurrent neural network (RNN) modules were combined to build an R-CNN model that can automatically obtain both spatial and temporal information from multitemporal Sentinel-2 remote sensing images of tea plantations, and then the spatial distribution of tea plantations was predicted. To confirm the effectiveness of our method, support vector machine (SVM), random forest (RF), CNN, and RNN methods were used for comparative experiments. The results show that the R-CNN method has great potential in the tea plantation extraction task, with an F1 score and IoU of 0.885 and 0.793 on the test dataset, respectively. The overall classification accuracy and kappa coefficient for the whole region are 0.953 and 0.904, respectively, indicating that this method possesses higher extraction accuracy than the other four methods. In addition, we found that the distribution index of tea plantations in mountainous areas with gentle slopes is the highest in Xinchang County. This study can provide a reference basis for the fine mapping of tea plantation distributions

Distributed modeling of monthly air temperatures over the rugged terrain of the Yellow River Basin

Our analyses of the monthly mean air temperature of meteorological stations show that altitude, global solar radiation and surface effective radiation have a significant impact on air temperature. We set up a physically-based empirical model for monthly air temperature simulation. Combined the proposed model with the distributed modeling results of global solar radiation and routine meteorological observation data, we also developed a method for the distributed simulation of monthly air temperatures over rugged terrain. Spatial distribution maps are generated at a resolution of 1 km×1 km for the monthly mean, the monthly mean maximum and the monthly mean minimum air temperatures for the Yellow River Basin. Analysis shows that the simulation results reflect to a considerable extent the macro and local distribution characteristics of air temperature. Cross-validation shows that the proposed model displays good stability with mean absolute bias errors of 0.19°C-0.35°C. Tests carried out on local meteorological station data and case year data show that the model has good spatial and temporal simulation capacity. The proposed model solely uses routine meteorological data and can be applied easily to other regions. © Science in China Press and Springer-Verlag GmbH 2009

Risk Assessment and Application of Tea Frost Hazard in Hangzhou City Based on the Random Forest Algorithm

Using traditional tea frost hazard risk assessment results as sample data, the four indicators of minimum temperature, altitude, tea planting area, and tea yield were selected to consider the risk of hazard-causing factors, the exposure of hazard-bearing bodies, and the vulnerability of hazard-bearing bodies. The random forest algorithm was used to construct the frost hazard risk assessment model of Hangzhou tea, and hazard risk assessment was carried out on tea with different cold resistances in Hangzhou. The model’s accuracy reached 93% after training, and the interpretation reached more than 0.937. According to the risk assessment results of tea with different cold resistance, the high-risk areas of weak cold resistance tea were the most, followed by medium cold resistance and the least strong cold resistance. Compared with the traditional method, the prediction result of the random forest model has a deviation of only 1.57%. Using the random forest model to replace the artificial setting of the weight factor in the traditional method has the advantages of simple operation, high time efficiency, and high result accuracy. The prediction results have been verified by the existing hazard data. The model conforms to the actual situation and has certain guiding for local agricultural production and early warning of hazards

Risk Assessment and Application of Tea Frost Hazard in Hangzhou City Based on the Random Forest Algorithm

Using traditional tea frost hazard risk assessment results as sample data, the four indicators of minimum temperature, altitude, tea planting area, and tea yield were selected to consider the risk of hazard-causing factors, the exposure of hazard-bearing bodies, and the vulnerability of hazard-bearing bodies. The random forest algorithm was used to construct the frost hazard risk assessment model of Hangzhou tea, and hazard risk assessment was carried out on tea with different cold resistances in Hangzhou. The model’s accuracy reached 93% after training, and the interpretation reached more than 0.937. According to the risk assessment results of tea with different cold resistance, the high-risk areas of weak cold resistance tea were the most, followed by medium cold resistance and the least strong cold resistance. Compared with the traditional method, the prediction result of the random forest model has a deviation of only 1.57%. Using the random forest model to replace the artificial setting of the weight factor in the traditional method has the advantages of simple operation, high time efficiency, and high result accuracy. The prediction results have been verified by the existing hazard data. The model conforms to the actual situation and has certain guiding for local agricultural production and early warning of hazards