Multi-objective Land Use Allocation Research Articles

An improved non-dominated sorting biogeography-based optimization algorithm for multi-objective land-use allocation: a case study in Kigali-Rwanda

ABSTRACT With the continuous increase of rapid urbanization and population growth, sustainable urban land-use planning is becoming a more complex and challenging task for urban planners and decision-makers. Multi-objective land-use allocation can be regarded as a complex spatial optimization problem that aims to achieve the possible trade-offs among multiple and conflicting objectives. This paper proposes an improved Non-dominated Sorting Biogeography-Based Optimization (NSBBO) algorithm for solving the multi-objective land-use allocation problem, in which maximum accessibility, maximum compactness, and maximum spatial integration were formulated as spatial objectives; and space syntax analysis was used to analyze the potential movement patterns in the new urban planning area of the city of Kigali, Rwanda. Efficient Non-dominated Sorting (ENS) algorithm and crossover operator were integrated into classical NSBBO to improve the quality of non-dominated solutions, and local search ability, and to accelerate the convergence speed of the algorithm. The results showed that the proposed NSBBO exhibited good optimal solutions with a high hypervolume index compared to the classical NSBBO. Furthermore, the proposed algorithm could generate optimal land use scenarios according to the preferred objectives, thus having the potential to support the decision-making of urban planners and stockholders in revising and updating the existing detailed master plan of land use.

Exploring trade-offs in agro-ecological landscapes: Using a multi-objective land-use allocation model to support agroforestry research

Finding the optimal land allocation for providing ecosystem services, conserving biodiversity and maintaining rural livelihoods is a key challenge of agricultural management and land-use planning. Agroforestry has been widely discussed as a sustainable land-use solution and as one strategy to improve the provision of multiple ecological and economic functions in agricultural landscapes. In this study, we use the backdrop of agroforestry research to evaluate a method from the multi-criteria decision analysis toolbox: robust multi-objective optimization. The key feature of this modelling approach is its capacity to integrate uncertain ecological and socio-economic data. We illustrate the optimization model with a case study from eastern Panama, showing how the model can bring together scientific and practical knowledge to provide potentially desirable landscape compositions from the perspective of farmers, a public perspective, and a compromise solution. Example results of our case study show how to assess whether agroforestry is a desirable component in a landscape composition to satisfy multiple objectives of different interest groups. Furthermore, we use the model to demonstrate how different objectives influence the optimal area share and type of agroforestry. Due to its parsimonious nature, the model could be used as a starting point of an interactive co-learning process with decision-makers, researchers and other stakeholders. The model, however, is not yet suitable for an exact prediction of future land-use dynamics, for questions of spatially explicit land-use configuration, studies going beyond the regional scale or for socio-economic interactions of agents. Therefore, we outline future research needs and recommendations for other types of models or hybrid approaches.

Accuracy and validity assessment of application algorithms in land use allocation into comparison LP, SA, MOLA and MDCHOICE

The main viewpoint of land use planning is to resolve the conflict between competing land uses in one place that is carried out based on their economic, social and environmental suitability. Many different approaches have been designed to resolve this conflict but any of them have done. This study aimed to assess accuracy and validity compared four optimization algorithms, including linear programming (LP), simulated annealing (SA), multi-objective land use allocation (MOLA) and multidimensional choice (MDCHOICE) for land use planning in Gorgan Township. At first, land use allocation was done through the LP algorithm. Then, using achieved pixels quantity from LP optimization, the land uses was optimized in term of spatial suitability by other algorithms. The allocation maps generated through these algorithms were compared using statistical methods and landscape metrics. The map provided through MDCHOICE was used as a reference map and that generated through MOLA, SA and LP as a comparative one in a validation process. The similarities and agreements of the two maps were evaluated with various algorithms in IDRISI and MCK software. The results demonstrate that: (1) It follows that the LP algorithm to determine the optimal number of pixels to lead MDCHOICE has helped in the spatial allocation properly. (2) MOLA had a better performance for development in terms of all landscape metrics and SA algorithm was better in forestry land use allocation. Therefore the quality of spatial allocation both of them needs to be improved. (3) Although the LP algorithm is not superior to the algorithms based on the dimensions studied in this research, in terms of achieving the defined goals and the degree of deviation from the number of cells defined for each land use has the least violation. (4) The superiority of MDCHOICE over MOLA, SA and LP and applicability of various kappa statistics for comparing the maps for a more in depth analysis of agreements and disagreements.

Assessing synergies and trade-offs between ecosystem services in forest landscape management

Human induced land use changes affect the provisioning of ecosystem services and may follow some economic rationale. Allocation of limited natural resources to different land utilization forms is the ultimate management problem for sustainable development. This study attempts to analyses the current land-use allocation systems in the Zagros area of western Iran suffering from soil erosion and water yielding problems and providing crucial economic benefits for local communities. Therefore, a comprehensive study using multiple advanced methods is needed to examine the ways land-use allocation can be enhanced with respect to multiple ecosystem services. For this, we have considered four ecosystem services, namely water yield, prevention of soil erosion, carbon sequestration and marketable products (wood, food). The economic efficiency (marketable production) of four different and competing land-uses, i.e. forest, pasture, orchard, and agriculture, were calculated applying a direct market valuation. To estimate the provision of ecosystem services, natural capital project tools InVEST and MPSIAC were used to model water production and total carbon budget (aerial biomass, underground, litter and soil), respectively. A Land-Use Conflict Identification Strategy model was used to classify Synergies and Trade-offs between the ecosystem services. A map was then developed to illustrate the spatial distribution of services, their provision levels, and to better understand trade-offs and synergies among a bundle of ecosystem services. To select sample points, a systematic random network was implemented on the map of the region and 533 sample points were selected. We used these sample points to assess the provision of ecosystem services and their synergies and trade-offs. To account for decision-makers’ preferences about the ecosystem services, a non-linear programming method was used to optimize multi-objective land-use allocation by weighing multi-objective benefits from ecosystem services at the landscape level. The results showed that 75% of the studied sample points had synergies, at least between two services, and 25% of the sample points showed trade-offs between the ES. The highest synergies were achieved between water production and prevention of soil erosion. The highest maximum target function was found for a diversity of scenarios aiming for 0–100% provision of the four services. In superior options, forest area level has increased by 75% and the level of orchards has increased by 258% (Relative to the initial area before optimization). The pasture area decreased by 45% and the agricultural land-use area was 65% lower than the pre-optimal level. The erosion rate of the region was reduced about 3736 tons/ha/year relative to pre-optimized conditions (100,514 tones/ha/year) and the water yield capacity increased by 663,100 cubic meters in the whole area, which is of great value due to the importance of water availability in the region. In addition, 76,905 tons /whole area have been added to the carbon storage function. Finally, 172,597 million Rials revenue generation (80%) could be realized for the entire region of 13484 ha.

Trade-Offs between Agricultural Production, GHG Emissions and Income in a Changing Climate, Technology, and Food Demand Scenario

Climate-smart agriculture targets integrated adaptation and mitigation strategies for delivering food security and greenhouse gas emissions reduction. This study outlines a methodology to identify the trade-offs between food production, emissions, and income under technology and food demand-shift scenario and climate change. The methodology uses Climate Smart Agricultural Prioritization (CSAP) toolkit a multi-objective land-use allocation model, and detailed databases, characterizing the agricultural production processes at the land-unit scale. A case study has also been demonstrated for Bihar, a state in India. The quantification of trade-offs demonstrates that under different technology growth pathways alone the food self-sufficiency for Bihar cannot be achieved whilst the reduction in emission intensity targets are achievable up to 2040. However, both food self-sufficiency and reduction in emission intensity can be achieved if we relax constraints on dietary demand and focus on kilo-calories maximization targets. The district-level analysis shows that food self-sufficiency and reduction in emission intensity targets can be achieved at a local scale through efficient crop-technology portfolios.

Aligning Agent-Based Modeling With Multi-Objective Land-Use Allocation: Identification of Policy Gaps and Feasible Pathways to Biophysically Optimal Landscapes

It is increasingly recognized in science and policy that landscapes need to be managed for multifunctionality. Multi-objective land-use allocation and agent-based modelling are two potent tools to explore the potential of landscapes to provide multiple ecosystem services. However, in the case of the former, the real-world feasibility of the biophysically optimal land-use configurations remains unclear. Meanwhile, agent-based models are not well-suited to recognize the biophysical potential of landscapes to provide multiple ecosystem services. In this paper, we propose an approach to align multi-objective optimization with agent-based modelling in order to investigate the economic, institutional and social feasibility of biophysically optimal landscapes. It especially allows to contrast biophysically optimized land-use patterns with the option space circumscribed by relevant policy frameworks. We argue that a structured comparison of biophysical optimization with an exploration of the parameter space of an agent-based model can be used to identify the real-world feasibility and the barriers to reaching multifunctional landscapes. We demonstrate the applicability of our approach by using it on a virtual landscape, which allows us to detect the importance of various economic, institutional and behavioural factors that facilitate or hamper moving the social–ecological system towards its biophysical potential. Particularly, we demonstrate the essential role of tailored policy instruments. Our approach can be useful in informing land-use policy with respect to its effectiveness and efficiency in achieving multifunctional landscapes.

A Knowledge-Informed and Pareto-Based Artificial Bee Colony Optimization Algorithm for Multi-Objective Land-Use Allocation

Land-use allocation is of great significance in urban development. This type of allocation is usually considered to be a complex multi-objective spatial optimization problem, whose optimized result is a set of Pareto-optimal solutions (Pareto front) reflecting different tradeoffs in several objectives. However, obtaining a Pareto front is a challenging task, and the Pareto front obtained by state-of-the-art algorithms is still not sufficient. To achieve better Pareto solutions, taking the grid-representative land-use allocation problem with two objectives as an example, an artificial bee colony optimization algorithm for multi-objective land-use allocation (ABC-MOLA) is proposed. In this algorithm, the traditional ABC’s search direction guiding scheme and solution maintaining process are modified. In addition, a knowledge-informed neighborhood search strategy, which utilizes the auxiliary knowledge of natural geography and spatial structures to facilitate the neighborhood spatial search around each solution, is developed to further improve the Pareto front’s quality. A series of comparison experiments (a simulated experiment with small data volume and a real-world data experiment for a large area) shows that all the Pareto fronts obtained by ABC-MOLA totally dominate the Pareto fronts by other algorithms, which demonstrates ABC-MOLA’s effectiveness in achieving Pareto fronts of high quality.

Improving the performance of genetic algorithms for land-use allocation problems

ABSTRACTMulti-objective optimization can be used to solve land-use allocation problems involving multiple conflicting objectives. In this paper, we show how genetic algorithms can be improved in order to effectively and efficiently solve multi-objective land-use allocation problems. Our focus lies on improving crossover and mutation operators of the genetic algorithms. We tested a range of different approaches either based on the literature or proposed for the first time. We applied them to a land-use allocation problem in Switzerland including two conflicting objectives: ensuring compact urban development and reducing the loss of agricultural productivity. We compared all approaches by calculating hypervolumes and by analysing the spread of the produced non-dominated fronts. Our results suggest that a combination of different mutation operators, of which at least one includes spatial heuristics, can help to find well-distributed fronts of non-dominated solutions. The tested modified crossover operators did not significantly improve the results. These findings provide a benchmark for multi-objective optimization of land-use allocation problems with promising prospectives for solving complex spatial planning problems.

Visually-driven parallel solving of multi-objective land-use allocation problems: a case study in Chelan, Washington

Many geospatial optimization models can be formulated as multi-objective linear integer programming (LIP) models. Because geospatial optimization models are much more complicated than regular LIP models, solving large-scale geospatial LIP models may be facilitated through parallel computing. In this paper, we explore the possibility of applying geovisual analytics to promote the search of exact optimal solutions in parallel computing environments. By integrating the potential of visual analytics and high-performance computing, we developed a suite of interactive geovisual tools to dynamically steer the optimization search in an interactive manner. Using a sustainable land use design as a case study, we demonstrate the potential of our approach in solving multi-objective land use allocation problems.

An improved artificial immune system for seeking the Pareto front of land-use allocation problem in large areas

The Pareto front can provide valuable information on land-use planning decision by revealing the possible trade-offs among multiple, conflicting objectives. However, seeking the Pareto front of land-use allocation is much more difficult than finding a unique optimal solution, especially when dealing with large-area regions. This article proposes an improved artificial immune system for multi-objective land-use allocation (AIS-MOLA) to tackle this challenging task. The proposed AIS is equipped with three modified operators, namely (1) a heuristic hypermutation based on compromise programming, (2) a non-dominated neighbour-based proportional cloning and (3) a novel crossover operator that preserves connected patches. To validate the proposed algorithm, it was applied in a hypothetical land-use allocation problem. Compared with the Pareto Simulated Annealing (PSA) method, AIS-MOLA can generate solutions more approximate to the Pareto front, with computation time amounting to only 5.1% of PSA. In addition, AIS-MOLA was also applied in the case study of Panyu, Guangdong, PR China, a large area with cells. Experimental results indicate that this algorithm, even dealing with large-area land-use allocation problems, is capable of generating optimal alternative solutions approximate to the true Pareto front. Moreover, the distribution of these solutions can quantitatively demonstrate the complex trade-offs between the spatial suitability and the compactness in the study area. Software and supplementary materials are available at http://www.geosimulation.cn/AIS-MOLA/.

Exploring normative scenarios of land use development decisions with an agent-based simulation laboratory

Suburbia and exurbia have an undeniable appeal to many urban dwellers. At the same time, they are characterized by an ineffective and fragmented residential patchwork of developed and undeveloped tracts. This research addresses a question of whether other arrangements of land, ameliorating the negative effects of current growth in the suburban fringe, are feasible from the perspectives of planning agencies and property developers. In order to answer this research question, the study employs two loosely coupled land use models: multiobjective land use allocation (MOLA) and an exploratory agent-based modeling (ABM) of residential development. The aligned modeling methodology has a number of advantages. Firstly, it combines top-down and bottom-up modeling. Such an approach is an attempt to represent society from two standpoints: institutions on one side (like zoning regulations of local planning agencies) and individual agents on the other (like developers). Secondly, the framework combines both static form (MOLA) and dynamic process (ABM). The MOLA model is equipped with mechanisms that encourage both compact and alternative residential land use arrangements. The outcomes of this model are used as zoning regulations in the ABM to examine the impact of regional-scale top-down urban growth plans on agent disutility which reflects the competitiveness of the local property market. Selected MOLA plans are further relaxed using different distance buffers. The findings point to a complex disutility–fragmentation relationship. Under the simulated planning situation, a potentially acceptable solution for planners and developers involves a relatively high compactness of development, which could satisfy agents’ overall disutility.