Urban growth and land-use change are a few of many puzzling factors affecting our future cities. Creating a precise simulation for future land change is a challenging process that requires temporal and spatial modeling. Many recent studies developed and trained models to predict urban expansion patterns using Artificial Intelligence (AI). This study aims to enhance the simulation capability of Cellular Automata Markov Chain (CA-MC) model in predicting changes in land-use. This study integrates the Artificial Neural Network (ANN) into CA-MC to incorporate several driving forces that highly impact land-use change. The research utilizes different socio-economic, spatial, and environmental variables (slope, distance to road, distance to urban centers, distance to commercial, density, elevation, and land fertility) to generate potential transition maps using ANN Data-driven model. The generated maps are fed to CA-MC as additional inputs. We calibrated the original CA-MC and our models for 2015 cross-comparing simulated maps and actual maps obtained for Irbid city, Jordan in 2015. Validation of our model was assessed and compared to the CA-MC model using Kappa indices including the agreement in terms of quantity and location. The results elucidated that our model with an accuracy of 90.04% substantially outperforms CA-MC (86.29%) model. The improvement we obtained from integrating ANN with CA-MC suggested that the influence imposed by the driving force was necessary to be taken into account for more accurate prediction. In addition to the improved model prediction, the predicted maps of Irbid for the years 2021 and 2027 will guide local authorities in the development of management strategies that balance urban expansion and protect agricultural regions. This will play a vital role in sustaining Jordan's food security.

Rural–urban immigration, regional wars, refugees, and natural disasters all bring to prominence the importance of studying urban growth. Increased urban growth rates are becoming a global phenomenon creating stress on agricultural land, spreading pollution, accelerating global warming, and increasing water run-off, which adds exponentially to pressure on natural resources and impacts climate change. Based on the integration of machine learning (ML) and geographic information system (GIS), we employed a framework to delineate future urban boundaries for future expansion and urban agglomerations. We developed it based on a Time Delay Neural Network (TDNN) that depends on equal time intervals of urban growth. Such an approach is used for the first time in urban growth as a predictive tool and is coupled with Land Suitability Analysis, which incorporates both qualitative and quantitative data to propose evaluated urban growth in the Greater Irbid Municipality, Jordan. The results show the recommended future spatial expansion and proposed results for the year 2025. The results show that urban growth is more prevalent in the eastern, northern, and southern areas and less in the west. The urban growth boundary map illustrates that the continuation of urban growth in these areas will slowly further encroach upon and diminish agricultural land. By means of suitability analysis, the results showed that 51% of the region is unsuitable for growth, 43% is moderately suitable and only 6% is suitable for growth. Based on TDNN methodology, which is an ML framework that is dependent on the growth of urban boundaries, we can track and predict the trend of urban spatial expansion and thus develop policies for protecting ecological and agricultural lands and optimizing and directing urban growth.

The character of Amman, Jordan, as the "City of Waters"—referring to the abundance of water flowing in its known stream—has faded away because of the municipal policy to cover the stream in the 1960s which gradually changed the ecological character. This paper traces and explores the impacts of stream-coverage policy on the city character, morphology and land use changes. The purpose is to understand how an engineered problem-solving policy changed physical and perceptive factors and affected the character of the city. It also explores future attitudes towards reversing the non-nature-friendly conditions. The methods depend upon monitoring morphological changes in aerial photographs and in land use maps from municipality archives, conducting interviews with the elderly who witnessed change, one-to-one questionnaires with stakeholders and online questionnaires with residents and visitors. The results show that covering the stream is depriving the city of its historical/ecological character. The policy failed to promote affluent business, to mitigate flood impacts, or to decrease traffic congestion in the Central Business District (CBD). Most age groups believe the stream can improve the image and economy, despite the fact of their unawareness of its historical presence. In conclusion, engineered problem-solving should not stay in the hands of decision makers (technocrats) alone, but rather be considered with the public, sustainable character experts, and ecologists.

Wind-farm planning optimization is important for decision-making concerning regional energy planning in developing countries. This process is governed by restrictions on site selection based on land suitability metric variables, wind turbine technology variables, and land-use governing criteria. This study aims to create a framework for land appropriation strategies for locating optimum sites suitable for wind farms. It is using Jordan as an Area of Interest (AOI), where the scope is to illustrate how this framework will employ wind turbine energy to positively enhance the national Gross Domestic Product (GDP). The methodology employs thirteen GIS thematic layers with a 250-m spatial resolution to substantiate how site-specific criteria, turbine type, and turbine hub height variables are determining factors in the optimal solution. This method involves selecting relevant factors, database construction, data layer generation and preparation, numerical ranking and weighting of each factor, and computation of the potential wind farm locations map by overlaying all the thematic GIS layers. The results showed that the establishment of wind farms would not only meet the AOI’s growing energy needs, rather exceed them to generating income for the developing nation. The results of the feasibility study will boost the national GDP by 3.4%; where, for example, one governorate alone could produce 274.3% of the total required national consumption at a turbine hub height of 50 m. The study attests to a valuable framework that can be implemented elsewhere to establish regional power sustainability and feasibility for other nations. The results show that an added land-use layer indicating the potential value of land in terms of its suitability for establishing wind farms should be considered in future sustainable regional planning studies when considering networks for smart cities, industrial cities, smart agriculture, and new agglomerations.

Land-use allocation (LUA) is a spatial optimization problem for urban planning in the future. The solution to this problem could be enhancing the effectiveness of optimization algorithms that create a balance between urban needs and efficient LUAs. This study will improve the performance of conventional genetic algorithms (cGAs) to address future LUAs. The enhancements will include improved initialization and mutation operators and a modification in the fitness function by the introduction of the area size deviation (ASD). The study will consider four objective functions to be maximized that include suitability, compatibility, conversion cost, and spatial compactness. The improved algorithm will be tested and applied to Taiz, Taiz Governorate, Yemen, to acquire the optimal LUA based on their 2035 plan. The findings produced three scenarios that were objectively weighted. The experimental results revealed that the improved genetic algorithms (GAs) were superior to the cGA for convergence speed, solution efficiency, optimality, and fulfillment of all constraints that included the LUA area size. The study exhibited the impact of weight variations on optimum LUAs for decision makers. This optimization resulted in a significant radial plan that featured green wedges and a smart arrangement of land-uses (LUs).