Genetic Algorithms for dynamic land-use optimization

In response to the problems of traditional genetic algorithms in evaluating English compositions, the stability of automatic grading of English compositions has been further enhanced. This article evaluates the teaching effectiveness of automatic grading of English compositions using an optimization fusion algorithm combined with genetic optimization algorithm and CNN model. By analyzing genetic content and optimization algorithms, a corresponding fusion optimization model was obtained, and the automatic evaluation of English compositions was analyzed and predicted through experimental verification. The results indicate that the curves corresponding to different parameters exhibit typical segmentation features through the variation curves of individual numbers under different scale factors. And through quantitative description and analysis of the curve, it can be seen that the change in proportion factor has an absolute advantage in the impact of genetic algorithm on the number of children. As the number of samples increases, the performance of genetic optimization algorithms under the f function shows an upward trend. Research has shown that the writing content index has the greatest impact on English writing, while the corresponding grammar errors have the smallest impact on English writing. Finally, the accuracy of the optimized model was verified by comparing the model curve with experimental data. This study provides theoretical support for the use of genetic optimization algorithms and CNN models in English, and provides ideas for the use of optimization algorithms in other fields.

Environmental and socio-economic developments induce land-use changes with potentially negative impacts on human well-being. To counteract undesired developments, a profound understanding of the complex relationships between drivers, land use, and ecosystem services is needed. Yet, national studies examining extended time periods are still rare. Based on the Special Report on land use, land management and climate change by the Austrian Panel on Climate Change (APCC), we use the Driver-Pressure-State-Impact-Response (DPSIR) framework to (1) identify the main drivers of land-use change, (2) describe past and future land-use changes in Austria between 1950 and 2100, (3) report related impacts on ecosystem services, and (4) discuss management responses. Our findings indicate that socio-economic drivers (e.g., economic growth, political systems, and technological developments) have influenced past land-use changes the most. The intensification of agricultural land use and urban sprawl have primarily led to declining ecosystem services in the lowlands. In mountain regions, the abandonment of mountain grassland has prompted a shift from provisioning to regulating services. However, simulations indicate that accelerating climate change will surpass socio-economic drivers in significance towards the end of this century, particularly in intensively used agricultural areas. Although climate change-induced impacts on ecosystem services remain uncertain, it can be expected that the range of land-use management options will be restricted in the future. Consequently, policymaking should prioritize the development of integrated land-use planning to safeguard ecosystem services, accounting for future environmental and socio-economic uncertainties.

The study assessed agricultural land use change and food security linkage in Nigeria from 1981 to 2023. The study adopted a historical survey design using time series data spanning the period of 43 years from 1981 to 2023.Data on the variables were collected from the archives of World Bank and Food and Agriculture Organization database and analyzed using descriptive statistics such as mean, maximum and minimum with trend graphs, Autoregressive distributed lag model (ARDL) after testing for unit root, granger causality test and Autoregressive Moving Average (ARIMA) model. The Augmented Dickey-Fuller (ADF) test for unit roots indicated that agricultural land use change is integrated of order one, I(1), while food security is integrated of order two, I(2). This suggests that both variables follow a random walk, meaning their future values do not revert to their past values. The results from the Bound test for a long-run relationship between agricultural land use change and food security showed that the F-statistic value of 4.676068 exceeds the upper critical bound of 4.35 at the 5% significance level, confirming a long-run relationship between the two variables. Additionally, the pairwise Granger causality test revealed a significant causal link between agricultural land use change and food security. The trend forecast indicated that over the next decade, agricultural land use change is expected to follow a downward trend, while food security is projected to increase, assuming other factors remain constant. Based on these findings, the study suggests that the government should implement comprehensive food security monitoring systems to track changes in land use and food security indicators. Early warning systems could prove valuable in predicting and addressing potential food security challenges, particularly in light of declining agricultural productivity.

As one of the last agricultural frontiers of the humid tropics, Amazonia is the largest area of the world currently undergoing frontier settlement. Although the earliest intrusions of foreign populations into Amazonia date from pre-Hispanic times, the large-scale entrance of peasant colonists into the vast region is a recent phenomenon. Much of this movement represents the spontaneous migration of peoples, but governments in the region have also become increasingly interested in opening up and integrating Amazonia to national and international economies. These actions are frequently seen as potential solutions to a number of national problems, including the need to increase agricultural production, correct spatial imbalances in the distribution of population, exploit frontier lands for reasons of national security, and defuse potentially serious political problems resulting from the existing agrarian structure, landlessness, and unemployment. The upper basin of the Amazon in Ecuador, bordering on the eastern slopes of the Andes, is one such area of frontier settlement. Recent decades have witnessed the rapid conversion of these Amazonian forests to agricultural uses through a series of schemes bearing such labels as land development and colonization. Most forest intervention in the region has come at the hands of colonist farmers attempting to establish land claims along transport routes originally constructed to aid in petroleum exploration and exploitation. These are farmers who formerly have made a living in long-established farmlands and who, for various reasons (population pressures, pervasive poverty, maldistribution of farmland, lack of inputs for intensive cultivation, lack of nonagrarian livelihood opportunities, and generally inadequate rural development) have been increasingly squeezed out of their homelands. A marginal person by virtue of his low socioeconomic and political status, the farmer often perceives

The impact of land use- and climate change on the managed eco-geomorphic balance in the Alps

The main concern of all economic companies is the resources equipping and allocating them in different economic sectors with the aim of maximizing profit and minimizing risk. Decentralization is one of the important factors that reduce investment risk. The investors plan to create investment by carefully planning and collecting sufficient information on the economic situation and analyzing the situation of various industries. As an economic enterprise, banks are looking for short- and long-term investments in a types of loans ,such as bailment of a capital , civil participation, reward, etc, which guarantees the return of their capital. In this paper, considering the condition of a bank as an economic enterprise, a model is presented which not only increases profit but also reduces risk. Two objective functions have been defined that the first objective is to minimize the risk and the second objective function is to maximize the of the bank profit, which is used by robust programming and Malvi Sim model. In this paper, we have investigated the Risky and non-Risky Partfolio and the optimal portfolio of bank assets from scenario based solution of the model and by using PSO and Genetic Optimization Algorithm. At all levels of confidence and optimal values of risk based on the estimation of SPP-CVAR method by Particle Swarm Algorithm (PSA) is less than genetic algorithm, which indicates better performance of Particle Swarm Algorithm (PSA) than Genetic Algorithm (GA). Also, the optimum wealth obtained from PSA solution is higher at all levels of confidence than the corresponding value of Genetic Algorithm (GA), and this is another reason to confirm the performance of PSO algorithm compared to the Genetic Algorithm (GA). The values of the first goal function, obtained from the PSO algorithm, for all confidence levels are lower than those of the genetic algorithm. The optimum wealth obtained from PSA is higher than genetic algorithm. At 0.9 level, the value of LR of kupiec statistics for the SPP-CVAR method was less than the Chi-square statistics (Critical value) which was assumed to be acceptable.

Abstract: The earthquake that occurred on 30 September 2009 had an impact on patterns of use of agricultural land and human settlements. Many agricultural lands are turned into residential land, especially the city of Padang. The purpose of this research ; (1). To see patterns and changes in the use of agricultural land in the city of Padang in the aftermath of the 2009 Padang earthquake; and (2) To see the population distribution pattern after the earthquake in Padang City. The method used is spatial and temporal analysis of changes in land use and population distribution before the earthquake and after the earthquake. The results of the study indicate that there has been a change in land use in the City of Padang from 2008-2012. Upland land that is widely used for agricultural business has decreased by 6%. Furthermore, there has been a change in land use in the city of Padang from 20012-2018. The decrease occurred on forest land by 38% and paddy fields by 41%. This decrease occurred due to the conversion of land into settlements which increased to 188%. In 2012, there was a decrease in population, especially in the districts of South Padang, North Padang, West Padang, East Padang and Bungus Teluk Kabung. In other Districts there was an increase, especially those that led eastward. Shifting land cover and the population of the city of Padang affect the Padang City Spatial Pattern. Keywords: Earthquake, Land Use, Spatial Analysis

Deli Serdang Regency is a district that in its development is experiencing quite rapid development. Changes in agricultural land use from 2009 - 2019 in Deli Serdang Regency are quite significant. The purpose of this study was to analyze changes in the use of agricultural land between 2009 - 2019, analyzing the economic value of the land and factors that affect the economic value of agricultural land in Deli Serdang Regency. The analysis is based on Geographic Information System (GIS), land economic analysis, and multiple linear regression analysis.

This paper firstly proposes an improved genetic algorithm (GA) for optimization in adaptive bus signal priority control at signalized intersections. Unlike conventional genetic algorithms with slow convergence speed, this algorithm can increase the convergence speed by utilizing the compensation rule between consecutive signal cycles to narrow new possible generated population spaces. Secondly, the paper would like to present a way to apply the algorithm to a simple adaptive bus signal priority control as well as compare how much the computation time is saved when applying the improved algorithm. Then the research thirdly investigates the efficiency of the proposed algorithm under various flow rate situations. The results show that the improved genetic algorithm can reduce the computation time considerably, by up to 48.39% for the studied case. With high saturation degrees on the cross street, the convergence rate performance of the improved genetic algorithm is significantly good. The figure can be up to 36.2% when compared with the convergence rate of the conventional GA.

ABSTRACTForest frontiers worldwide reveal trade-offs that are key in mitigating global change. In the forest frontiers of northeast Madagascar, land-use changes result from decisions made by smallholder farmers. In the past, subsistence needs led to increasing shifting cultivation, resulting in forest degradation and deforestation. This study focuses on investigating the role of locally determined factors in land-use change decisions in the forest frontier context. Therefore, we developed a Bayesian network-based land-use decision model that represents the causalities between factors influencing land-use decisions and takes into account local decision-makers’ knowledge. The approach is applied in two comparative case studies in northeast Madagascar. Results show that farmers mostly aim at extending the cultivation of cash crops. These results and the causal mechanisms disentangled for the forest frontier of northeast Madagascar help understand change mechanisms and hence, support decision-making to attain the Sustainable Development Goals.

Feedback loops and types of adaptation in the modelling of land-use decisions in an agent-based simulation

This paper presents a comparative performance assessment for loss minimization of vector controlled induction motor (IM) drive based on two different efficient optimization algorithms, namely Genetic Algorithm (GA) and Golden Search (GS). Here the features of GA and GS both for estimation and recalculation of optimized flux component of current ( ), have been utilized for a better optimal efficiency operation of the IM drive. In this context, a comparative performance of IM drive based on GA and GS optimization algorithms is carried out. Both the GA and GS based algorithms greatly improve the efficiency by reducing the core loss of the drive system. Moreover, both the approaches have no effect on parameter variation and also need no additional hardware for hardware implementation. However, GA based loss minimization scheme proves its edge over GS based scheme for the IM drive. The simulation results for various speed patterns and operating conditions are presented here. Stability study of the whole drive system is also carried out utilizing both the optimizing schemes. The performance of the proposed drive is validated experimentally on dSPACE-1104 based laboratory prototype.

Landowner characteristics influence his/her willingness to change landuse practices to provide more or less environmental benefits. However, most studies of agricultural/environmental polices identify landowners as homogenous. And, the primary cause of failure of many environmental and other polices is the lack of knowledge on how humans may respond to polices based on changes in their behavior (Stern, 1993). From socioeconomic theory and empirical research, landowners can be identified as individuals who make agricultural landuse decisions independently based on their objectives. Identifying possible classes of landowners, assessing how each would potentially respond to policy alternatives, and the resulting pattern of land uses in a watershed or a riparian corridor would be very useful to policy makers as they evaluated alternatives. Agricultural landscapes are important producers of ecosystem services. The mix of ecosystem services and commodity outputs of an agricultural landscape depends on the spatial pattern of land uses emerging from individual land use decisions. However, many empirical studies show that the production of ecosystem services from agricultural landscapes is declining. This is consistent with research conducted over the last few decades showing there is a narrow range of social circumstances under which landowners are willing to make investments in the present to achieve public benefits in the future through investing in natural capital resulting in public goods which are frequently produced as ecosystem services. In this study an agent-based model within a watershed planning context is used to analyze the tradeoffs involved in producing a number of ecosystem services and agricultural commodities given price and policy scenarios while assuming three different types of agents in terms of their goals. The agents represent landowners who have been divided into a number of different groups based on their goals and the size of their farm operations. The multi-agent-based model is developed using a heuristic search and optimization technique called genetic algorithm (GA) (Holland), which belongs to a broader class of evolutionary algorithms. GAs exhibit three properties (1) they start with a population of solution, (2) they explore the solution space through recombination and mutation and (3) they evaluate individual solutions based on their appropriate fitness value(s), for example given profit maximizing agents this would be gross margin. A GA is a heuristic stochastic search and optimization method, which works by mimicking the evolutionary principles and chromosomal processing in natural genetics. The three economic agents that are modeled are based on variations in their objective functions and constraints. This study will help in identifying the tradeoffs associated with various agents in the provision of ecosystem services and agricultural commodities. The agent model developed here will help policy and decision maker identify the various agents within the watershed and assess various policy options based on that information. The study will also help to understand the interaction and feedback between the agents and their environment associated with various policy initiatives. The results of the study indicate that the agent model correctly predicts the actual landuse landcover map by 75 percent. Key words: Multifunctional agriculture, Agent based modeling, Genetic Algorithm.

The impact of biogas plants on regional dynamics of permanent grassland and maize area—The example of Hesse, Germany (2005–2010)

This paper presents a new multi-objective optimization method, which is inspired from the idea of non-dominated sorting genetic algorithm (NSGA) and genetic quantum algorithm (GQA). The GQA has been tested on well known test beds in single objective optimization and compared with the genetic algorithm (GA) in the lead author’s previous work [22]. This paper aims to apply the idea of GQA to multi-objective optimization (MOO). The developed method is called non-dominated sorting genetic quantum algorithm (NSGQA). The developed method is tested with benchmark problems collected from literature, which have characteristics representing various aspects of a MOO problem. Test results show that NSGQA has better performance on most benchmark problems than currently popular MOO methods such as the NSGA. The integration of GQA with MOO, and the systematic comparison with other MOO methods on benchmark problems, should be of general interest to researchers on MOO and to practitioners using MOO methods in design.