Construction land is the leading carrier of human activities such as production and living.Evaluating the construction land suitability(CLS)on the Qinghai-Tibet Plateau(QTP)holds significant implications for harmonizi...Construction land is the leading carrier of human activities such as production and living.Evaluating the construction land suitability(CLS)on the Qinghai-Tibet Plateau(QTP)holds significant implications for harmonizing the relationship between ecological protection and human activity and promoting population and industry layout optimization.However,no relevant studies provide a complete CLS assessment of the QTP.In this study,we developed a model-based CLS evaluation framework coupling of pattern and process to calculate the global CLS on the QTP based on a previously developed CLS evaluation model.Then,using the land-use data of 1990,2000,2010,and 2020,we examined the adaptability of existing construction land(ECL)to the CLS assessment result through the adaptability index and vertical gradient index and further analyzed the limitations of maladaptive construction land.Finally,we calculated the potential area of reserve suitable construction land.This article includes four conclusions:(1)The highly suitable,suitable,moderately suitable,marginally suitable,and unsuitable CLS classes cover areas of 0.33×10^(4)km^(2),10.42×10^(4)km^(2),18.06×10^(4)km^(2),24.12×10^(4)km^(2),and 205.29×10^(4)km^(2),respectively.Only approximately 11%of the study area on the QTP is suitable for large-scale permanent construction land,and approximately 79.50%of the area is unsuitable under current economic and technological conditions.(2)The ECL adaptability index is 85.16%,85.93%,85.18%,and 78.01%during 1990–2020,respectively,with an average adaptability index exceeding 80%on the QTP.The ECL distribution generally conforms to construction land suitable space characteristics but with a significant spatial difference.(3)From 1990 to 2020,the maladaptive ECL was dominated by rural settlement land,transport land,and special land,with a rapidly increasing proportion of urban and other construction land.The maladaptive ECL is constrained by both elevation and slope in the southern Qinghai Plateau,the Hengduan Mountains,and the Qilian Mountains.In contrast,elevation is significantly more limiting than slope in the northern Tibet Plateau,the Gangdis Mountains,and the Himalayan Mountains.(4)The potential area of reserve suitable construction land is 12.41×10^(4)km^(2),accounting for 4.81%of the total land area of the QTP,and the per capita area is 9928 m^(2).Regions of Qaidam Basin,Gonghe Basin,and Lhasa-Shannan Valley have the richest and most concentrated land resource of reserve suitable construction land.The research results provide spatial decision support for urban and rural settlement planning and ecological migration on the QTP.展开更多
In recent years,aquaculture has developed rapidly,especially in coastal and open ocean areas.In practice,water quality prediction is of critical importance.However,traditional water quality prediction models face limi...In recent years,aquaculture has developed rapidly,especially in coastal and open ocean areas.In practice,water quality prediction is of critical importance.However,traditional water quality prediction models face limitations in handling complex spatiotemporal patterns.To address this challenge,a prediction model was proposed for water quality,namely an adaptive multi-channel temporal graph convolutional network(AMTGCN).The AMTGCN integrates adaptive graph construction,multi-channel spatiotemporal graph convolutional network,and fusion layers,and can comprehensively capture the spatial relationships and spatiotemporal patterns in aquaculture water quality data.Onsite aquaculture water quality data and the metrics MAE,RMSE,MAPE,and R^(2) were collected to validate the AMTGCN.The results show that the AMTGCN presents an average improvement of 34.01%,34.59%,36.05%,and 17.71%compared to LSTM,respectively;an average improvement of 64.84%,56.78%,64.82%,and 153.16%compared to the STGCN,respectively;an average improvement of 55.25%,48.67%,57.01%,and 209.00%compared to GCN-LSTM,respectively;and an average improvement of 7.05%,5.66%,7.42%,and 2.47%compared to TCN,respectively.This indicates that the AMTGCN,integrating the innovative structure of adaptive graph construction and multi-channel spatiotemporal graph convolutional network,could provide an efficient solution for water quality prediction in aquaculture.展开更多
基金The Second Tibetan Plateau Scientific Expedition and Research Program,No.2019QZKK0406。
文摘Construction land is the leading carrier of human activities such as production and living.Evaluating the construction land suitability(CLS)on the Qinghai-Tibet Plateau(QTP)holds significant implications for harmonizing the relationship between ecological protection and human activity and promoting population and industry layout optimization.However,no relevant studies provide a complete CLS assessment of the QTP.In this study,we developed a model-based CLS evaluation framework coupling of pattern and process to calculate the global CLS on the QTP based on a previously developed CLS evaluation model.Then,using the land-use data of 1990,2000,2010,and 2020,we examined the adaptability of existing construction land(ECL)to the CLS assessment result through the adaptability index and vertical gradient index and further analyzed the limitations of maladaptive construction land.Finally,we calculated the potential area of reserve suitable construction land.This article includes four conclusions:(1)The highly suitable,suitable,moderately suitable,marginally suitable,and unsuitable CLS classes cover areas of 0.33×10^(4)km^(2),10.42×10^(4)km^(2),18.06×10^(4)km^(2),24.12×10^(4)km^(2),and 205.29×10^(4)km^(2),respectively.Only approximately 11%of the study area on the QTP is suitable for large-scale permanent construction land,and approximately 79.50%of the area is unsuitable under current economic and technological conditions.(2)The ECL adaptability index is 85.16%,85.93%,85.18%,and 78.01%during 1990–2020,respectively,with an average adaptability index exceeding 80%on the QTP.The ECL distribution generally conforms to construction land suitable space characteristics but with a significant spatial difference.(3)From 1990 to 2020,the maladaptive ECL was dominated by rural settlement land,transport land,and special land,with a rapidly increasing proportion of urban and other construction land.The maladaptive ECL is constrained by both elevation and slope in the southern Qinghai Plateau,the Hengduan Mountains,and the Qilian Mountains.In contrast,elevation is significantly more limiting than slope in the northern Tibet Plateau,the Gangdis Mountains,and the Himalayan Mountains.(4)The potential area of reserve suitable construction land is 12.41×10^(4)km^(2),accounting for 4.81%of the total land area of the QTP,and the per capita area is 9928 m^(2).Regions of Qaidam Basin,Gonghe Basin,and Lhasa-Shannan Valley have the richest and most concentrated land resource of reserve suitable construction land.The research results provide spatial decision support for urban and rural settlement planning and ecological migration on the QTP.
基金funded by the National Key Research and Development Program of China:Sino-Malta Fund 2022“Autonomous Biomimetic Underwater Vehicle for Digital Cage Monitoring”(Grant No.2022YFE0107100).
文摘In recent years,aquaculture has developed rapidly,especially in coastal and open ocean areas.In practice,water quality prediction is of critical importance.However,traditional water quality prediction models face limitations in handling complex spatiotemporal patterns.To address this challenge,a prediction model was proposed for water quality,namely an adaptive multi-channel temporal graph convolutional network(AMTGCN).The AMTGCN integrates adaptive graph construction,multi-channel spatiotemporal graph convolutional network,and fusion layers,and can comprehensively capture the spatial relationships and spatiotemporal patterns in aquaculture water quality data.Onsite aquaculture water quality data and the metrics MAE,RMSE,MAPE,and R^(2) were collected to validate the AMTGCN.The results show that the AMTGCN presents an average improvement of 34.01%,34.59%,36.05%,and 17.71%compared to LSTM,respectively;an average improvement of 64.84%,56.78%,64.82%,and 153.16%compared to the STGCN,respectively;an average improvement of 55.25%,48.67%,57.01%,and 209.00%compared to GCN-LSTM,respectively;and an average improvement of 7.05%,5.66%,7.42%,and 2.47%compared to TCN,respectively.This indicates that the AMTGCN,integrating the innovative structure of adaptive graph construction and multi-channel spatiotemporal graph convolutional network,could provide an efficient solution for water quality prediction in aquaculture.
