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Based on deep learning, precipitation prediction has been a hot topic in hydrological research in recent years. Monthly precipitation data is a typical small sample data that cannot meet the demands of deep learning for large datasets. Therefore, this paper proposes an improved method for predicting monthly precipitation using a Long Short-term Memory Network (LSTM) by integrating the ideas of signal decomposition and boundary correction. First, to address the " end effect" of sequence decomposition, this paper uses the waveform feature matching extension method to expand the boundaries of the original sequence. Then, the original sequence is decomposed twice by using Extreme-point Symmetric Mode Decomposition (ESMD) and Variational Mode Decomposition (VMD). ESMD is used to extract different sorts of scale information of the monthly precipitation sequence, obtaining several model components with decreasing frequencies and one residual component. After cutting off the most severe extension parts that "pollute" the internal data of each sub-sequence, VMD further smooths the high-frequency components. Finally, LSTM is applied to each sub-sequence for prediction, and the predicted results are reconstructed to obtain the final prediction. The monthly precipitation in Badong County, Hubei Province is selected as an example for verification. Through model comparison analysis, the results show that compared to traditional single SVM and LSTM models, the prediction model combining signal decomposition algorithm has more advantages in monthly precipitation prediction; The method of boundary correction is incorporated into the combined model by using the ESMD algorithm improves the overall prediction accuracy of the model. The prediction effect of high-frequency components is a key factor determining the accuracy of combined model prediction. The method proposed in this paper not only performs the best on the selected evaluation indicators but also significantly improves the fitting effect of extreme data points. In particular, even when facing a small sample dataset, the Nash-Sutcliffe Efficiency coefficient (NSE) of monthly precipitation prediction can reach 0.960 0 by using this model. [ABSTRACT FROM AUTHOR]

Rapeseed is an essential oil crop in China. Hubei, Hunan and Jiangxi provinces in the middle reaches of the Yangtze River are the main planting areas, which account for more than 42% of the total planted area of rapeseed in China. The area has more than 3467 ha of winter fallow field, with massive potential for utilization. The total mechanization rate of rapeseed tillage, seeding and harvesting in the 3 provinces is higher than national average. Still, the uneven development, insufficient utilization of idle fields in winter, low levels of mechanization and intelligence, are technical bottlenecks for improving whole mechanization. This paper analyzes the basic situation and existing problems of the rapeseed industry in the middle reaches of the Yangtze River, combing tillage, seeding, harvesting key aspects of mechanization and intelligent technology, and gives the technical routes of the whole process of rapeseed production that could be promoted. Meanwhile, a comparative analysis of the technical characteristics and difficulties in promoting the application of the "tillage, seeding and harvesting" aspects of rapeseed production was carried out. Development trend of rapeseed production technology mode is semi-mechanized, mechanized to intelligent in the winter fallow fields. The technical development paths of each link are discribed as "anti-blocking, anti-sticky, anti-tangle, and high-speed, efficient, low consumption" mechanized tillage. Integrated tillage and seeding technology to complete multiple working procedures simultaneously. High efficiency, low consumption and low loss of rapeseed mechanized harvesting technology should be combined with harvesting and two-stage harvesting synergistic development. The mechanization technology of feeding, vegetable and fertilizer and the critical technology of intelligent rapeseed production should also be developed and applied simultaneously. To achieve high yield, several suggestions for promoting full mechanization technology model for rapeseed in the mid-winter fields of the Yangtze River are proposed. [ABSTRACT FROM AUTHOR]

Copyright of Carsologica Sinica is the property of Institute of Karst Geology, CAGS and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Based on 694 farmer survey data in Jiangsu Province and Hubei Province, this paper uses hierarchical regression and Bootstrap method to empirically analyze the influence path of farmer s participation on ecological policy gains and social policy gains. The results show that: * The participation of farmers has significantly improved the sense of ecological and social gains, and part of the mediating effect on ecological gains and social gains through government trust, accounting for 29.89% and 18.97% of the total utility, respectively. * Formal systems play a positive role in regulating the influence of government trust on social gains, and informal systems have a positive regulating role in the influence of government trust on ecological and social gains. Therefore, this study proposes that rural environmental governance should strengthen the trust of farmers in the government, require institutions to empower farmers to participate in and empower farmers, to activate the endogenous motivation of farmers to participate, and moreover, it is necessary to use informal systems to reach a consensus on collective governance in the village, strengthen the network of farmers and the government, and enhance farmers sense of policy gains. [ABSTRACT FROM AUTHOR]

Copyright of Guihaia is the property of Guihaia Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)