A multi-objective evolutionary programming approach to the ‘object location’ spatial analysis and optimisation problem within the urban water management domain

Facility allocation plays a crucial role in modern urban planning and design. However, the allocation of various facilities in a reasonable manner is a complex problem involving high-dimensional multi-peak combination optimization. Existing heuristic methods for facility configuration, including traditional genetic algorithms and their improved variants, often suffer from premature convergence and limited search diversity when dealing with large-scale spatial optimization problems under geographical correlation and heterogeneity constraints in urban coupled environments. These limitations result in suboptimal solutions and reduced effectiveness of planning schemes. This paper proposes an improved quantum genetic algorithm with adaptive dynamic rotation angle (IQGA-ADA) for spatial optimization of facility configuration. The proposed IQGA-ADA method addresses the limitations of existing approaches through two key innovations: (1) an ADA mechanism integrated into the population renewal strategy to prevent early-stage local convergence and enhance global search capabilities; (2) the application of S-transform for processing multi-modal spatial data (including population distribution, traffic network, and facility location information) to extract characteristic quantities (root mean square and mean value) that enhance the identification degree of facility configuration patterns. By conducting a case analysis on the spatial optimization of first aid facilities, the proposed algorithm demonstrates its capability to enhance the fairness of repositioning optimization for such facilities. Quantitative results show that IQGA-ADA achieves a 68% improvement in fitness value over the real-coded genetic algorithm (RCGA), with the average fitness converging at approximately 5 iterations compared to 125 iterations for RCGA. Experimental results indicate that the proposed algorithm exhibits stronger global search capabilities and performs better in handling high-dimensional multi-peak spatial optimization problems. Moreover, the research highlights the potential application of quantum evolution mechanisms in addressing geospatial optimization problems.

Abstract. In the last few years, the 3D GIS domain has developed rapidly, and has become increasingly accessible to different disciplines. 3D Spatial analysis of Built-up areas seems to be one of the most challenging topics in the communities currently dealing with spatial data. One of the most basic problems in spatial analysis is related to visibility computation in such an environment. Visibility calculation methods aim to identify the parts visible from a single point, or multiple points, of objects in the environment. In this work, we present a unique method combining visibility analysis in 3D environments with dynamic motion planning algorithm, named Visibility Velocity Obstacles (VVO) with Markov process defined as spatial visibility analysis for routes in 3D dense city environment. Based on our VVO analysis, we use Reinforcement Learning (RL) method in order to find an optimal action policy in dense 3D city environment described as Markov decision process, navigating in the most visible routes. As far as we know, we present for the first time a Reinforcement Learning (RL) solution to the visibility analysis in 3D dense environment problem, generating a sequence of viewpoints that allows an optimal visibility in different routes in urban city. Our analysis is based on fast and unique solution for visibility boundaries, formulating the problem with RL methods.

To achieve net-zero in the transportation sector, there is a need to deploy public electric vehicle (EV) fast chargers to boost customer confidence. Currently, charge point operators and EV manufacturers focus on deploying chargers to cover highway networks and the progress in urban environments is inadequate. This is becoming an obstacle for potential EV buyers who do not have access to dedicated chargers or cannot afford expensive EVs with larger battery packs. Since mainstream combustion engine vehicle drivers are accustomed to using petrol stations, this paper examines the suitability of such sites as candidate locations to deploy fast chargers. Spatial analysis is carried out by comparing their coverage performance with existing locations of the fast chargers. More specifically, the location problem is modelled as a maximum coverage location problem (MCLP) and solved using a geographic information system (GIS) based platform. The spatial optimization problem is solved using a linear-programming relaxation based MCLP algorithm developed in Python. Five cities with growing populations, namely San Clara, CA, Salt Lake City, UT, Raleigh, NC, Denver, CO, and Los Angeles, CA, are chosen as case studies. The location analysis is carried out with two demand metrics (population and road traffic) using actual GIS data collected from public authorities. The results show that deploying fast chargers at existing fuel stations significantly increases the coverage needed for EVs. This study will provide useful insights into EV fast charging station planning in urban cities, as the related research is still in its infancy.

Genetic algorithm (GA), evolutionary programming (EP) and evolutionary strategy (ES) are called the three kinds of evolutionary computation methods. They have been widely used in many engineering fields. However, selecting individuals directly and random search lead to produce premature problem, and requirement for high precision decreases the search efficiency, these become the obstructs of application in engineering practice. This paper proposes a new algorithm of evolutionary computation, it is called bio-simulated optimization algorithm (BSO). BSO reproduces new generation through asexual propagation and sexual propagation. Here, the evolutionary operators effectively solve the problem of premature convergence. Furthermore, performance of global search and convergence are proved theoretically. Finally, Compared BSO with GA and EP in searching the optimal solution of a continuous multi-peaks function, three kinds of computation procedures are run in Matlab, the result shows that performance of BSO is superior to GA and EP.

General-purpose optimization methods for parallelization of digital terrain analysis based on cellular automata

Research on water scarcity and water management in Europe has accelerated significantly in the past two decades, mainly as a result of growing water demand for agricultural, industrial, and municipal uses. Most research studies in the field evaluate water use and management by means of static indicators that depict a one-time value for a given time period (e.g., one year). This paper suggests a dynamic indicator measuring product (here: water) generational dematerialization. The indicator presents a comprehensive approach for evaluating water resources and water management strategies, as it represents a function of both resource use changes and population changes occurring simultaneously and over time. To accentuate the benefits of this dynamic indicator over static indicators as well as its practical applicability for decision-making support, the paper evaluates water management in Europe based on the total water use between 2001 and 2013. The results show that water management estimated cumulatively for the analyzed European countries has been effective for the last 13 years, though significant regional variations have been found. The research and the dynamic water generational dematerialization indicator can be helpful with addressing regional and national water deficit problems and designing sustainable water management strategies in the mid- and long-run.

With the development of science and technology, there has been growing the grid size, the growing demand for electricity and the extent of increasing the electricity market.In this paper, a mathematical morphology and genetic algorithm based dynamic reactive power optimization method for distribution network is proposed.By means of constructing mathematical morphology filter, the binary image composed by allelic genes of chromosome is filtered and the problem that deals with the action times of voltage regulating devices for compensation is turned into filtering problem of discrete binary image.With further automate scheduling, distribution automation and unmanned substation reactive power optimization of distribution line running made an urgent request.Reactive power optimization can improve the voltage passing rate, increasing the reliability of the system, the power quality, and the system runs the security and economy of the perfect combination, very broad application prospects.

Abstract. Temporal and spatial changes to the water quality of Dianchi Lake in southwest China were investigated using monthly monitoring data from 2005 to 2012. Dianchi Lake is divided into two parts, Caohai Lake and Waihai Lake, by a man-made dike. Caohai Lake lies at the north of Dianchi Lake, while Waihai Lake is the main water body of Dianchi Lake and accounts for 96.7% of the whole area of the lake. Based on the analysis of total phosphorus (TP), total nitrogen (TN), and chlorophyll a (Chl a) concentrations, it was determined that, in Caohai Lake, the annual concentrations of these variables ranged from 0.19–1.46 mg L−1, 6.11–16.79 mg L−1, 0.06–0.14 mg L−1, respectively. In addition, the annual concentrations of TP, TN and Chl a in Waihai Lake ranged between 0.13 and 0.20 mg L−1, 1.82 and 3.01 mg L−1, and 0.04 and 0.09 mg L−1, respectively. Cluster analysis (CA) classified the 10 monitoring sites into two clusters (cluster A and cluster B) based on similarities of water quality characteristics. Our data revealed that the current status of water quality within Caohai Lake was much worse than that of Waihai Lake. Water quality was seriously degraded during the economic boom near the period of the "Eleventh Five-Year Plan" (2005–2010), and gradually improved from 2010 to 2012 because of the "standard emission directive to industry". The main factors that influenced the spatial and temporal changes to water quality were natural factors including lake evolution and regional characteristic as well as human factors such as pollution load into the lake and management strategies that were already adopted. Some activities and regulations were implemented to enhance the lake environment by controlling wastewater emissions and establishing regulations to protect the lakes in the Yunnan Province. However, problems with institutional fragmentation (horizontal and vertical), simple treatment methods, low-intensity investment in pollution control, and lack of meaningful endogenous pollution control strategies were still present in the lake management strategy. To solve these problems, suitable control measures are needed, especially considering the current old-age status of Dianchi Lake. The fundamental improvement of the water quality within Caohai Lake was dependent on the measures taken in the upper reaches of the Caohai Watershed, including further recovery of submerged plants, resource utilization by floating plants and the reinforcement of sediment disposal. Management strategies for endogenous pollution in Waihai Lake were mainly dependent on restocking algae-eating fish and the ecological restoration of macrophytes. In this way, the swamping trend and the ageing process that is occurring in Dianchi Lake can be stunted. And the management strategies would be a contribution to the management of water conflicts between mankind and ecosystems in similar lakes.

In searching for a track record of earth formation, people tend to interpret field data in a way that limits the potential of history and explanation, which is often less influenced by theory and available facts, this requires valid data gathered intellectually. Geocomputing, which is the digital processing of geographic data, is a relatively recent field that reflects a wide range of research methodologies and their application to help solve problems in machine learning, geostatistics, image processing, and spatial analysis, among other areas. The purpose of this study was to assess the effectiveness of using geocomputational methods with Avenza Map and ArcGIS. This study uses geocomputation and spatial analysis to carry out geological mapping at a scale of 1:25,000 located in the Battang area, West Wara District, Palopo City, South Sulawesi. Two aspects are the main focus in implementing this research method, namely when mapping by relying on Avenza with a base map sourced from the Rupa Bumi Indonesia (RBI) map and on data management using the ArcGIS application to become a 3D map. Lithological of the study area showed basalt, porphyry basalt, and phyllite. The geological structure that develops in the research area is joint and fault. Avenza Map work with spatial analysis in ArcGIS is obtained cognitively, involving the relationship between individual conditions, tools, and terrain conditions. It can be stated that the use of avenza on smartphones in this modernization condition is more effective. This research allows for a review of the practical level of use of computer methods ranging from mapping to data management. Regarding implementing cognitive mapping, Avenza Map and ArcGIS can be recommended tools in geo-computation techniques as more effective support for recording and managing data while still paying attention to noise that may occur as a barrier to data accuracy.

ABSTRACTFailure to detect change in an auditory scene (i.e., change deafness) has been found to occur with alterations to object identities and the location of objects. Event-related potential (ERP) correlates of change deafness for object identity have been identified, but to our knowledge, ERP comparisons between spatial and identity change deafness are non-existent. We examined whether ERP differences exist between spatial and identity (ID) changes, and among levels of change detection accuracy (correct change identified vs. wrong change identified vs. change deaf). Within a trial, listeners were presented with two consecutive auditory scenes, each composed of four environmental sounds played simultaneously at separate azimuths on the horizontal plane. Scene 2 was either identical to the first (“no change”), had one sound replaced (“ID change”), or contained the same sounds at different locations (“space change”). Accuracy was similar for ID and space change trials (~60% correct), but error patterns differed. On space change trials, listeners made few ID change responses. However, on ID change trials listeners were nearly equal in their tendencies to make a no change and space change response. N1-P2 complexes locked to scene onsets were largest on trials where a change was indicated, but the wrong change type was chosen. P3b amplitudes locked to Scene 2 onset were monotonically related to accuracy (larger for correct and wrong change trials than change deaf trials). Effects were similar for space and ID changes, but scalp topography of P3b differed for change types (larger amplitudes for ID change trials at left temporal electrodes). Data indicate that P3b amplitude is a reliable indicator of successful change detection across different types of scene changes, showing different topography reflecting different “what” and “where” processing streams. N1-P2 complex effects suggest better encoding of scenes in relation to change detection, but unknown change type.

Outlier detection is an important problem in spatial analysis which involves finding a region of spatial locations with features significantly different from the rest of the population. In this paper, we used fast fourier transform to highlight the areas with high frequency change. The spatial points identified by the fourier transform are then reconfirmed with Z-value test and outlier regions are identified. We performed several experiments to highlight the accuracy and efficiency of the approach and compared it with some other existing approaches.

A central problem in spatial analysis is the mapping of data for complex spatial fields using relatively simple data structures, such as those of a conventional GIS. This complexity can be measured using such indices as multi-scale variance, which reflects spatial autocorrelation, and multi-fractal dimension, which characterizes the values of fields. These indices are computed for three spatial processes: Gaussian noise, a simple mathematical function, and data for a random walk. Fractal analysis is then used to produce a vegetation map of the central region of California based on a satellite image. This analysis suggests that real world data lie on a continuum between the simple and the random, and that a major GIS challenge is the scientific representation and understanding of rapidly changing multi-scale fields.

Human activities exert both beneficial and detrimental impacts on the ecosystem. In recent years, greenhouse gas emissions have significantly increased due to global climate change, causing profound alterations in ecosystem distribution and productivity. The synergistic interplay between climatic shifts and anthropogenic activities is intensifying ecological transformations and disturbances, and accelerating biodiversity depletion. The Plantago genus (Plantaginaceae family) includes 14 herbaceous species among China's flora. This study was conducted to elucidate the spatial distribution of Plantago species patterns across China and evaluate their differential responses to impending climate change and human interventions. In this study, we projected the potential distributions of Plantago species under three climate scenarios (SSP126, SSP245, and SSP585) across current and future temporal intervals (2021-2040, 2041-2060, 2061-2080, and 2081-2100) using the MaxEnt model integrated with ArcGIS V10.8 spatial analysis. A spatial trend analyses was also conducted to assess habitat suitability dynamics by incorporating anthropogenic influence parameters. The model validation yielded AUC values exceeding 0.9, demonstrating excellent model performance and predictive reliability. Precipitation variability and anthropogenic pressure emerged as the most predominant determinants shaping Plantago distributions. Centroid migration analyses further indicated the progressive northward displacement of optimal habitats under the projected climate scenarios. These findings significantly advance our understanding of Plantago species' adaptive responses to environmental changes. This study also offers an invaluable scientific foundation for sustainable resource management and ecological conservation strategies.

A Spatially Adjusted ANOVA Model

Urban spaces, as the material representatives of past capital accumulation strategies, survive or get replaced on the basis of actors' strategies and partnerships. Periodization allows conceptualising these decisions and subsequent spatial change with respect to evolving local and national settings. The study focuses on expanding such approaches to Türkiye's experience beyond the two primate cities Istanbul and Ankara by the example of a secondary city, Bursa. The study uses maps, plans, official documents and newspapers corresponding to the periods and literature to form the connections between the actors' decisions and the spatial changes. The results highlight that while the previous periods' urban fabric and heritage is being replaced during the neoliberal era in parallel with the national experience, the problems faced as a secondary centre in close proximity to a primate city reduces local agency much more severely, leaving the urban space more vulnerable to exogenous influences.