Quantifying the controls on evapotranspiration partitioning in the highest alpine meadow ecosystem

Quantifying the transpiration fraction of evapotranspiration (T/ET) is crucial for understanding plant functionality in ecosystem water cycles, land‐atmosphere interactions, and the global water budget. However, the controls and mechanisms underlying the temporal change of T/ET remain poorly understood in arid and semiarid areas, especially for remote regions with sparse observations such as the Tibetan Plateau (TP). In this study, we used combined high‐frequency laser spectroscopy and chamber methods to constrain estimates of T/ET for an alpine meadow ecosystem in the central TP. The three isotopic end members in ET (δET), soil evaporation (δE), and plant transpiration (δT) were directly determined by three newly customized chambers. Results showed that the seasonal variations of δET, δE, and δT were strongly affected by the precipitation isotope (R2 = 0.53). The δ18O‐based T/ET agreed with that of δ2H. Isotope‐based T/ET ranged from 0.15 to 0.73 during the periods of observation, with an average of 0.43. This mean result was supported by T/ET derived from a two‐source model and eddy covariance observations. Our overarching finding is that at the seasonal timescale, surface soil water content (θ) dominated the change of T/ET, with leaf area index playing only a secondary role. Our study confirms the critical impact of soil water on the temporal change of T/ET in water‐limited regions such as the TP. This knowledge sheds light on diverse land‐surface processes, global hydrological cycles, and their modeling

Tour-Route-Recommendation Algorithm Based on the Improved AGNES Spatial Clustering and Space-Time Deduction Model

This study designed a tour-route-planning and recommendation algorithm that was based on an improved AGNES spatial clustering and space-time deduction model. First, the improved AGNES tourist attraction spatial clustering algorithm was created. Based on the features and spatial attributes, city tourist attraction clusters were formed, in which the tourist attractions with a high degree of correlation among attributes were gathered into the same cluster. It formed the precondition for searching tourist attractions that would match tourist interests. Using tourist attraction clusters, this study also developed a tourist attraction reachability model that was based on tourist-interest data and geospatial relationships to confirm each tourist attraction’s degree of correlation to tourist interests. A dynamic space-time deduction algorithm that was based on travel time and cost allowances was designed in which the transportation mode, time, and costs were set as the key factors. To verify the proposed algorithm, two control algorithms were chosen and tested against the proposed algorithm. Our results showed that the proposed algorithm had better results for tour-route planning under different transportation modes as compared to the controls. The proposed algorithm not only considered time and cost allowances, but it also considered the shortest traveling distance between tourist attractions. Therefore, the tourist attractions and tour routes that were suggested not only met tourist interests, but they also conformed to the constraint conditions and lowered the overall total costs

Tour-Route-Recommendation Algorithm Based on the Improved AGNES Spatial Clustering and Space-Time Deduction Model

This study designed a tour-route-planning and recommendation algorithm that was based on an improved AGNES spatial clustering and space-time deduction model. First, the improved AGNES tourist attraction spatial clustering algorithm was created. Based on the features and spatial attributes, city tourist attraction clusters were formed, in which the tourist attractions with a high degree of correlation among attributes were gathered into the same cluster. It formed the precondition for searching tourist attractions that would match tourist interests. Using tourist attraction clusters, this study also developed a tourist attraction reachability model that was based on tourist-interest data and geospatial relationships to confirm each tourist attraction’s degree of correlation to tourist interests. A dynamic space-time deduction algorithm that was based on travel time and cost allowances was designed in which the transportation mode, time, and costs were set as the key factors. To verify the proposed algorithm, two control algorithms were chosen and tested against the proposed algorithm. Our results showed that the proposed algorithm had better results for tour-route planning under different transportation modes as compared to the controls. The proposed algorithm not only considered time and cost allowances, but it also considered the shortest traveling distance between tourist attractions. Therefore, the tourist attractions and tour routes that were suggested not only met tourist interests, but they also conformed to the constraint conditions and lowered the overall total costs

A Low-Carbon Decision-Making Algorithm for Water-Spot Tourists, Based on the k-NN Spatial-Accessibility Optimization Model

This study presents a low-carbon decision-making algorithm for water-spot tourists, based on the k-NN spatial-accessibility optimization model, to address the problems of water-spot tourism spatial decision-making. The attributes of scenic water spots previously visited by the tourists were knowledge-mined, to ascertain the tourists’ interest-tendencies. A scenic water-spot classification model was constructed, to classify scenic water spots in tourist cities. Then, a scenic water spot spatial-accessibility optimization model was set up, to sequence the scenic spots. Based on the tourists’ interest-tendencies, and the spatial accessibility of the scenic water spots, a spatial-decision algorithm was constructed for water-spot tourists, to make decisions for the tourists, in regard to the tour routes with optimal accessibility and lowest cost. An experiment was performed, in which the tourist city of Leshan was chosen as the research object. The scenic water spots were classified, and the spatial accessibility for each scenic spot was calculated; then, the optimal tour routes with optimal spatial accessibility and the lowest cost were output. The experiment verified that the tour routes that were output via the proposed algorithm had stronger spatial accessibility, and cost less than the sub-optimal ones, and were thus more environmentally friendly

Water Ecotourism Route Recommendation Model Based on an Improved Cockroach Optimization Algorithm

Aiming to address the problems of the current research on water ecotourism routes, a water ecotourism route recommendation model based on an improved cockroach optimization algorithm is proposed. The aim is to recommend the tour routes with the lowest exhaust emissions. Firstly, depending on tourists’ once-visited water scenic spots, a scenic spot recommendation model based on the improved item-based collaborative filtering algorithm is set up. Then, by combining the recommended scenic spots and integrating the random transportation modes selected by tourists, a tour route recommendation model based on an improved cockroach optimization algorithm is constructed, which can output the tour route that produces the lowest exhaust emissions. Finally, The sample experiment shows that, on the basis of combining with the multivariate random transportation modes, the proposed algorithm has greater advantages than the tour routes planned by the traditional electronic maps, as it can output the tour routes with the lowest exhaust emissions, reduce the damage exhaust emissions cause in the urban water environments and to water resources, and effectively protect the urban water ecological environments

Low-Carbon Tour Route Algorithm of Urban Scenic Water Spots Based on an Improved DIANA Clustering Model

Aiming at the problems in current research into low-carbon and water scenery tourism, this paper brings forward a low-carbon tour route algorithm of urban scenic water spots based on an improved Divisive Analysis clustering model. Based on the ecological attributes of scenic water spots, the clustering model is set up to create scenic spot clusters. Via the clusters, the low-carbon tour route algorithm of urban scenic water spots based on the optimal energy conservation and emission reduction mode is proposed, and it provides the optimal scenic water spots and low-carbon tour routes for tourists. The model can thus realize the optimization of vehicle exhaust emission in urban travel and reduce exhaust emission damage to urban water bodies and natural environments. In order to verify the advantages of the proposed algorithm, this paper performs an experiment to compare the proposed algorithm with the frequently used route planning methods by tourists. The experimental results show that the proposed algorithm has great advantages in energy conservation, emission reduction and low-carbon travel and can reduce the exhaust emission and the damage to the urban water bodies and the natural environment, realizing low-carbon tourism. The main findings and contributions of the proposed work are as follows. First, an improved clustering algorithm is set up, and the urban scenic water spots are clustered according to attribute data, which could optimize the scenic spot recommendation spatial model. Second, combining with the specific characteristics of scenic water spots, the scenic spot mining and matching algorithm is set up to satisfy tourists’ needs. Third, a method that could reduce emission exhaust by optimizing self-driving tour routes is proposed, which could control and reduce the damage to urban environments and protect water ecosystems. The proposed algorithm could be used as the embedded algorithm of tour recommendation systems or the reference algorithm for planning urban tourism transportation. Especially in peak tourism season, it could be used as an effective method for tourism and traffic management departments to direct traffic flow

Low-Carbon Tour Route Algorithm of Urban Scenic Water Spots Based on an Improved DIANA Clustering Model

Aiming at the problems in current research into low-carbon and water scenery tourism, this paper brings forward a low-carbon tour route algorithm of urban scenic water spots based on an improved Divisive Analysis clustering model. Based on the ecological attributes of scenic water spots, the clustering model is set up to create scenic spot clusters. Via the clusters, the low-carbon tour route algorithm of urban scenic water spots based on the optimal energy conservation and emission reduction mode is proposed, and it provides the optimal scenic water spots and low-carbon tour routes for tourists. The model can thus realize the optimization of vehicle exhaust emission in urban travel and reduce exhaust emission damage to urban water bodies and natural environments. In order to verify the advantages of the proposed algorithm, this paper performs an experiment to compare the proposed algorithm with the frequently used route planning methods by tourists. The experimental results show that the proposed algorithm has great advantages in energy conservation, emission reduction and low-carbon travel and can reduce the exhaust emission and the damage to the urban water bodies and the natural environment, realizing low-carbon tourism. The main findings and contributions of the proposed work are as follows. First, an improved clustering algorithm is set up, and the urban scenic water spots are clustered according to attribute data, which could optimize the scenic spot recommendation spatial model. Second, combining with the specific characteristics of scenic water spots, the scenic spot mining and matching algorithm is set up to satisfy tourists’ needs. Third, a method that could reduce emission exhaust by optimizing self-driving tour routes is proposed, which could control and reduce the damage to urban environments and protect water ecosystems. The proposed algorithm could be used as the embedded algorithm of tour recommendation systems or the reference algorithm for planning urban tourism transportation. Especially in peak tourism season, it could be used as an effective method for tourism and traffic management departments to direct traffic flow

A Smart Tourism Recommendation Algorithm Based on Cellular Geospatial Clustering and Multivariate Weighted Collaborative Filtering

Tourist attraction and tour route recommendation are the key research highlights in the field of smart tourism. Currently, the existing recommendation algorithms encounter certain problems when making decisions regarding tourist attractions and tour routes. This paper presents a smart tourism recommendation algorithm based on a cellular geospatial clustering and weighted collaborative filtering. The problems are analyzed and concluded, and then the research ideas and methods to solve the problems are introduced. Aimed at solving the problems, the tourist attraction recommendation model is set up based on a cellular geographic space generating model and a weighted collaborative filtering model. According to the matching degree between the tourists’ interest needs and tourist attraction feature attributes, a precise tourist attraction recommendation is obtained. In combination with the geospatial attributes of the tourist destination, the spatial adjacency clustering model based on the cellular space generating algorithm is set up, and then the weighted model is introduced for the collaborative filtering recommendation algorithm, which ensures that the recommendation result precisely matches the tourists’ needs. Providing precise results, the optimal tour route recommendation model based on the precise tourist attraction approach vector algorithm is set up. The approach vector algorithm is used to search the optimal route between two POIs under the condition of multivariate traffic modes to provide the tourists with the best motive benefits. To verify the feasibility and advantages of the algorithm, this paper designs a sample experiment and analyzes the resulting data to obtain the relevant conclusion

Cognitive Semantic Analysis and Dynamic Generation of Cartographic Symbols

Location-based services (LBS) requires generating real-time personalized and dynamic cartographic symbols. In order to tackle this problem, this paper proposes a structural description model and a dynamic generation method of cartographic symbols based on cognitive analysis. Following the cognitive semantics principle of‘reality-cognition-symbol’, the description model elaborates the mapping mechanism between symbol graphics and symbol semantics, which uses symbol morphemes as atomic units and semantic structure as description framework. The generation method is composed of a context-free grammar model which uses rules to generate cartographic symbols on the basis of morphemes. Through the modelling of graphic morphemes, structural morphemes and rule-based generation system, a grammar compiler for dynamic generation of cartographic symbols is redesigned. Lastly, experiments of dynamic generation of cartographic symbols in different contexts and semantics are performed to verify the feasibility of the proposed method. Therefore, this study leads to a further understanding of cognition and structure principles of cartographic symbols, and also pushes forward dynamic generation of cartographic symbols for related industries and applications