Accurate estimation of evapotranspiration(ET)is crucial for efficient water resource management,particularly in the face of climate change and increasing water scarcity.This study performs a bibliometric analysis of 3...Accurate estimation of evapotranspiration(ET)is crucial for efficient water resource management,particularly in the face of climate change and increasing water scarcity.This study performs a bibliometric analysis of 352 articles and a systematic review of 35 peer-reviewed papers,selected according to PRISMA guidelines,to evaluate the performance of Hybrid Artificial Neural Networks(HANNs)in ET estimation.The findings demonstrate that HANNs,particularly those combining Multilayer Perceptrons(MLPs),Recurrent Neural Networks(RNNs),and Convolutional Neural Networks(CNNs),are highly effective in capturing the complex nonlinear relationships and tem-poral dependencies characteristic of hydrological processes.These hybrid models,often integrated with optimization algorithms and fuzzy logic frameworks,significantly improve the predictive accuracy and generalization capabilities of ET estimation.The growing adoption of advanced evaluation metrics,such as Kling-Gupta Efficiency(KGE)and Taylor Diagrams,highlights the increasing demand for more robust performance assessments beyond traditional methods.Despite the promising results,challenges remain,particularly regarding model interpretability,computational efficiency,and data scarcity.Future research should prioritize the integration of interpretability techniques,such as attention mechanisms,Local Interpretable Model-Agnostic Explanations(LIME),and feature importance analysis,to enhance model transparency and foster stakeholder trust.Additionally,improving HANN models’scalability and computational efficiency is crucial,especially for large-scale,real-world applications.Approaches such as transfer learning,parallel processing,and hyperparameter optimization will be essential in overcoming these challenges.This study underscores the transformative potential of HANN models for precise ET estimation,particularly in water-scarce and climate-vulnerable regions.By integrating CNNs for automatic feature extraction and leveraging hybrid architectures,HANNs offer considerable advantages for optimizing water management,particularly agriculture.Addressing challenges related to interpretability and scalability will be vital to ensuring the widespread deployment and operational success of HANNs in global water resource management.展开更多
As a result of rapid development in electronics and communication technology,large-scale unmanned aerial vehicles(UAVs)are harnessed for various promising applications in a coordinated manner.Although it poses numerou...As a result of rapid development in electronics and communication technology,large-scale unmanned aerial vehicles(UAVs)are harnessed for various promising applications in a coordinated manner.Although it poses numerous advantages,resource management among various domains in large-scale UAV communication networks is the key challenge to be solved urgently.Specifically,due to the inherent requirements and future development trend,distributed resource management is suitable.In this article,we investigate the resource management problem for large-scale UAV communication networks from game-theoretic perspective which are exactly coincident with the distributed and autonomous manner.By exploring the inherent features,the distinctive challenges are discussed.Then,we explore several gametheoretic models that not only combat the challenges but also have broad application prospects.We provide the basics of each game-theoretic model and discuss the potential applications for resource management in large-scale UAV communication networks.Specifically,mean-field game,graphical game,Stackelberg game,coalition game and potential game are included.After that,we propose two innovative case studies to highlight the feasibility of such novel game-theoretic models.Finally,we give some future research directions to shed light on future opportunities and applications.展开更多
Zarrineh River is located in the northwest of Iran,providing more than 40%of the total inflow into the Lake Urmia that is one of the largest saltwater lakes on the earth.Lake Urmia is a highly endangered ecosystem on ...Zarrineh River is located in the northwest of Iran,providing more than 40%of the total inflow into the Lake Urmia that is one of the largest saltwater lakes on the earth.Lake Urmia is a highly endangered ecosystem on the brink of desiccation.This paper studied the impacts of climate change on the streamflow of Zarrineh River.The streamflow was simulated and projected for the period 1992-2050 through seven CMIP5(coupled model intercomparison project phase 5)data series(namely,BCC-CSM1-1,BNU-ESM,CSIRO-Mk3-6-0,GFDL-ESM2G,IPSL-CM5A-LR,MIROC-ESM and MIROC-ESM-CHEM)under RCP2.6(RCP,representative concentration pathways)and RCP8.5.The model data series were statistically downscaled and bias corrected using an artificial neural network(ANN)technique and a Gamma based quantile mapping bias correction method.The best model(CSIRO-Mk3-6-0)was chosen by the TOPSIS(technique for order of preference by similarity to ideal solution)method from seven CMIP5 models based on statistical indices.For simulation of streamflow,a rainfall-runoff model,the hydrologiska byrans vattenavdelning(HBV-Light)model,was utilized.Results on hydro-climatological changes in Zarrineh River basin showed that the mean daily precipitation is expected to decrease from 0.94 and 0.96 mm in 2015 to 0.65 and 0.68 mm in 2050 under RCP2.6 and RCP8.5,respectively.In the case of temperature,the numbers change from 12.33℃ and 12.37℃ in 2015 to 14.28℃ and 14.32℃ in 2050.Corresponding to these climate scenarios,this study projected a decrease of the annual streamflow of Zarrineh River by half from 2015 to 2050 as the results of climatic changes will lead to a decrease in the annual streamflow of Zarrineh River from 59.49 m^(3)/s in 2015 to 22.61 and 23.19 m^(3)/s in 2050.The finding is of important meaning for water resources planning purposes,management programs and strategies of the Lake's endangered ecosystem.展开更多
文摘Accurate estimation of evapotranspiration(ET)is crucial for efficient water resource management,particularly in the face of climate change and increasing water scarcity.This study performs a bibliometric analysis of 352 articles and a systematic review of 35 peer-reviewed papers,selected according to PRISMA guidelines,to evaluate the performance of Hybrid Artificial Neural Networks(HANNs)in ET estimation.The findings demonstrate that HANNs,particularly those combining Multilayer Perceptrons(MLPs),Recurrent Neural Networks(RNNs),and Convolutional Neural Networks(CNNs),are highly effective in capturing the complex nonlinear relationships and tem-poral dependencies characteristic of hydrological processes.These hybrid models,often integrated with optimization algorithms and fuzzy logic frameworks,significantly improve the predictive accuracy and generalization capabilities of ET estimation.The growing adoption of advanced evaluation metrics,such as Kling-Gupta Efficiency(KGE)and Taylor Diagrams,highlights the increasing demand for more robust performance assessments beyond traditional methods.Despite the promising results,challenges remain,particularly regarding model interpretability,computational efficiency,and data scarcity.Future research should prioritize the integration of interpretability techniques,such as attention mechanisms,Local Interpretable Model-Agnostic Explanations(LIME),and feature importance analysis,to enhance model transparency and foster stakeholder trust.Additionally,improving HANN models’scalability and computational efficiency is crucial,especially for large-scale,real-world applications.Approaches such as transfer learning,parallel processing,and hyperparameter optimization will be essential in overcoming these challenges.This study underscores the transformative potential of HANN models for precise ET estimation,particularly in water-scarce and climate-vulnerable regions.By integrating CNNs for automatic feature extraction and leveraging hybrid architectures,HANNs offer considerable advantages for optimizing water management,particularly agriculture.Addressing challenges related to interpretability and scalability will be vital to ensuring the widespread deployment and operational success of HANNs in global water resource management.
基金This work was supported by National Key R&D Program of China under Grant 2018YFB1800802in part by the National Natural Science Foundation of China under Grant No.61771488,No.61631020 and No.61827801+1 种基金in part by State Key Laboratory of Air Traffic Management System and Technology under Grant No.SKLATM201808in part by Postgraduate Research and Practice Innovation Program of Jiangsu Province under No.KYCX190188.
文摘As a result of rapid development in electronics and communication technology,large-scale unmanned aerial vehicles(UAVs)are harnessed for various promising applications in a coordinated manner.Although it poses numerous advantages,resource management among various domains in large-scale UAV communication networks is the key challenge to be solved urgently.Specifically,due to the inherent requirements and future development trend,distributed resource management is suitable.In this article,we investigate the resource management problem for large-scale UAV communication networks from game-theoretic perspective which are exactly coincident with the distributed and autonomous manner.By exploring the inherent features,the distinctive challenges are discussed.Then,we explore several gametheoretic models that not only combat the challenges but also have broad application prospects.We provide the basics of each game-theoretic model and discuss the potential applications for resource management in large-scale UAV communication networks.Specifically,mean-field game,graphical game,Stackelberg game,coalition game and potential game are included.After that,we propose two innovative case studies to highlight the feasibility of such novel game-theoretic models.Finally,we give some future research directions to shed light on future opportunities and applications.
文摘Zarrineh River is located in the northwest of Iran,providing more than 40%of the total inflow into the Lake Urmia that is one of the largest saltwater lakes on the earth.Lake Urmia is a highly endangered ecosystem on the brink of desiccation.This paper studied the impacts of climate change on the streamflow of Zarrineh River.The streamflow was simulated and projected for the period 1992-2050 through seven CMIP5(coupled model intercomparison project phase 5)data series(namely,BCC-CSM1-1,BNU-ESM,CSIRO-Mk3-6-0,GFDL-ESM2G,IPSL-CM5A-LR,MIROC-ESM and MIROC-ESM-CHEM)under RCP2.6(RCP,representative concentration pathways)and RCP8.5.The model data series were statistically downscaled and bias corrected using an artificial neural network(ANN)technique and a Gamma based quantile mapping bias correction method.The best model(CSIRO-Mk3-6-0)was chosen by the TOPSIS(technique for order of preference by similarity to ideal solution)method from seven CMIP5 models based on statistical indices.For simulation of streamflow,a rainfall-runoff model,the hydrologiska byrans vattenavdelning(HBV-Light)model,was utilized.Results on hydro-climatological changes in Zarrineh River basin showed that the mean daily precipitation is expected to decrease from 0.94 and 0.96 mm in 2015 to 0.65 and 0.68 mm in 2050 under RCP2.6 and RCP8.5,respectively.In the case of temperature,the numbers change from 12.33℃ and 12.37℃ in 2015 to 14.28℃ and 14.32℃ in 2050.Corresponding to these climate scenarios,this study projected a decrease of the annual streamflow of Zarrineh River by half from 2015 to 2050 as the results of climatic changes will lead to a decrease in the annual streamflow of Zarrineh River from 59.49 m^(3)/s in 2015 to 22.61 and 23.19 m^(3)/s in 2050.The finding is of important meaning for water resources planning purposes,management programs and strategies of the Lake's endangered ecosystem.
