Recent research has showed increasing interest at the vital role of irrigation ponds that plays at biodiversity conservation,and provides ecological functions at a wide range.However,many irrigation ponds were abolish...Recent research has showed increasing interest at the vital role of irrigation ponds that plays at biodiversity conservation,and provides ecological functions at a wide range.However,many irrigation ponds were abolished due to the economic and societal transformation in the rural.In particular,small-scale ponds were abolished and rebuilt to other public uses based on the consensus building process among the community.At the same time,civil organizations also launched initiatives to conserve irrigation ponds for its ecological significance or landscape scenery.However,study pertinent to the small scale ponds in the rural setting is largely neglected.This research aims at revealing the current situation of the utilization and management of small irrigation ponds using a case study of Noto Island in Ishikawa Prefecture.It was found that irrigation ponds are still under the traditional co-management of rural community.The most important finding in this study is that the traditional management of pond water use largely contributes to mitigate the harvest loss from natural disasters such as drought in the face of extreme climate.However,irrigation ponds are facing the threat of degradation due to the sharp decrease of farm population and the existing large number of part time farmers.Therefore,the small scale irrigation ponds and pertinent management and water use allotment should be revalued for its functions at a wide range from the biological and ecological functions and human knowledge system to mitigate disaster threats.展开更多
In this study,a machine learning method,i.e.genetic programming(GP),is employed to obtain a simplified statistical model to describe the variation of soil suction in drying cycles using five selected influential param...In this study,a machine learning method,i.e.genetic programming(GP),is employed to obtain a simplified statistical model to describe the variation of soil suction in drying cycles using five selected influential parameters.The data used for model development was recorded by an in-situ experiment.The image processing technology is used to quantify several tree canopy parameters.Based on four accuracy metrics,i.e.root mean square error(RMSE),mean absolute percentage error(MAPE),coefficient of determination(R2),and relative error,the performance of the proposed GP model was evaluated.The results indicate that the model can give a reasonable estimation for the spatiotemporal variations of soil suction around a tree with acceptable errors.Global sensitivity analysis for the statistical model obtained using limited data of a specific region demonstrates the drying time as the most influential variable and the initial soil suction as the second most influential variable for the soil suction variations.A case study was conducted using a set of assumed input variable values and validated that the simplified GP model can be used to estimate and predict the spatiotemporal variations of soil suction in rooted soil at a certain range.展开更多
文摘Recent research has showed increasing interest at the vital role of irrigation ponds that plays at biodiversity conservation,and provides ecological functions at a wide range.However,many irrigation ponds were abolished due to the economic and societal transformation in the rural.In particular,small-scale ponds were abolished and rebuilt to other public uses based on the consensus building process among the community.At the same time,civil organizations also launched initiatives to conserve irrigation ponds for its ecological significance or landscape scenery.However,study pertinent to the small scale ponds in the rural setting is largely neglected.This research aims at revealing the current situation of the utilization and management of small irrigation ponds using a case study of Noto Island in Ishikawa Prefecture.It was found that irrigation ponds are still under the traditional co-management of rural community.The most important finding in this study is that the traditional management of pond water use largely contributes to mitigate the harvest loss from natural disasters such as drought in the face of extreme climate.However,irrigation ponds are facing the threat of degradation due to the sharp decrease of farm population and the existing large number of part time farmers.Therefore,the small scale irrigation ponds and pertinent management and water use allotment should be revalued for its functions at a wide range from the biological and ecological functions and human knowledge system to mitigate disaster threats.
基金the National Key R&D Program of China(No.2019YFB1600700)the Science and Technology Development Fund of Macao(Nos.SKL-IOTSC-2018-2020 and 0193/2017/A3)the University of Macao Research Fund(No.MYRG2018-00173-FST),China。
文摘In this study,a machine learning method,i.e.genetic programming(GP),is employed to obtain a simplified statistical model to describe the variation of soil suction in drying cycles using five selected influential parameters.The data used for model development was recorded by an in-situ experiment.The image processing technology is used to quantify several tree canopy parameters.Based on four accuracy metrics,i.e.root mean square error(RMSE),mean absolute percentage error(MAPE),coefficient of determination(R2),and relative error,the performance of the proposed GP model was evaluated.The results indicate that the model can give a reasonable estimation for the spatiotemporal variations of soil suction around a tree with acceptable errors.Global sensitivity analysis for the statistical model obtained using limited data of a specific region demonstrates the drying time as the most influential variable and the initial soil suction as the second most influential variable for the soil suction variations.A case study was conducted using a set of assumed input variable values and validated that the simplified GP model can be used to estimate and predict the spatiotemporal variations of soil suction in rooted soil at a certain range.
