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Landslides represent a growing threat among the various morphological processes that cause damage to territories. To address this problem and prevent the associated risks, it is essential to quickly find adequate methodologies capable of predicting these phenomena in advance. The following study focuses on the implementation of an experimental WebGIS infrastructure designed and built to predict the susceptibility index of a specific presumably at-risk area in real time (using specific input data) and in response to extreme weather events (such as heavy rain). The climate data values are calculated through an innovative and experimental atmospheric simulator developed by the authors, which is capable of providing data on meteorological variables with high spatial precision. To this end, the terrain is represented through cellular automata, implementing a suitable neural network useful for producing the desired output. The effectiveness of this methodology was tested on two debris flow events that occurred in the Calabria region, specifically in the province of Reggio Calabria, in 2001 and 2005, which caused extensive damage. The (forecast) results obtained with the proposed methodology were compared with the (known) historical data, confirming the effectiveness of the method in predicting (and therefore signaling the possibility of an imminent landslide event) a higher susceptibility index than the known one and one provided (to date) by the Higher Institute for Environmental Protection and Research (ISPRA), validating the result obtained through the actual subsequent occurrence of a landslide event in the area under investigation. Therefore, the method proposed today is not aimed at predicting the local movement of a small landslide area, but is primarily aimed at predicting the change or improving the variation of the landslide susceptibility index to compare the predicted value with the current one provided by the relevant bodies (ISPRA), thus signaling an alert for the entire area under investigation.

Animal skin patterns are increasingly explained using the Turing pattern model proposed by Alan Turing. The Turing model, a self-organizing model, can produce spotted or striped patterns. However, several animal patterns exist that do not correspond to these patterns. For example, the body patterns of the ornamental carp Nishiki goi produced in Japan vary randomly among individuals. Therefore, predicting the pattern of offspring is difficult based on the parent fish. Such a randomly formed pattern could be explained using a majority voting model. This model is a type of cellular automaton model that counts the surrounding states and transitions to high-number states. Nevertheless, the utility of these two models in explaining fish patterns remains unclear. Interestingly, the patterns generated by these two models can be detected among very closely related species. It is difficult to think that completely different epidermal formation mechanisms are used among species of the same family. Therefore, there may be a basic model that can produce both patterns. Herein, the Turing pattern and majority voting method are represented using cellular automata, and the possibility of integrating these two methods is examined. This integrated model is equivalent to both models when the parameters are adjusted. Although this integrated model is extremely simple, it can produce more varied patterns than either one of the individual models. However, further research is warranted to determine whether this model is consistent with the mechanisms involved in the formation of animal fish patterns from a biological perspective.

Lifespan of high temperature services could seriously be affected by cavitation and thus, prediction of this phenomenon is considered as a significant task. A two-dimensional cellular automata approach coupled with governing equations for cavity nucleation and growth was developed to predict the size and distribution of cavities. Different growth mechanisms including diffusion and strain-controlled processes were taken into account and also, the coalescence phenomenon and the corresponding growth rate were considered in the model. The creep experiments were conducted on AA5052 under different temperatures and applied stresses such as 15 MPa at 300 °C, 20 MPa at 300 °C and 30 MPa at 270 °C for providing essential data points. Afterwards, the optical metallography and the scanning electron microscopy were performed to characterize the cavity size and distribution. The obtained results were then used to determine material constants and validate the simulation outcomes and a reasonable consistency was found between the predictions and experimental data. It was found that the strain-controlled growth is the dominant mechanism for strains larger than 0.04 while the coalescence of cavities might be significant when cavity volume fraction is higher than 0.5%. The simulations showed that the large cavity radius was observed when growth rate due to coalescence was implemented in the model.

Abstract Background Malaria, a deadly disease caused by Plasmodium protozoa parasite and transmitted through bites of infected female Anopheles mosquitoes, remains a significant public health challenge in sub-Saharan Africa. Efforts to eliminate malaria have increasingly focused on vector control using insecticides. However, the emergence of insecticide resistance (IR) in malaria vectors pose a formidable obstacle, and the current IR mapping models remain static, relying on fixed coefficients. This study introduces a dynamic spatio-temporal approach to characterize phenotypic resistance in Anopheles gambiae complex and Anopheles arabiensis. We developed a cellular automata (CA) model and applied it to data collected from Ethiopia, Nigeria, Cameroon, Chad, and Burkina Faso. The data encompasses georeferenced records detailing IR levels in mosquito vector populations across various classes of insecticides. In characterizing the dynamic patterns of confirmed resistance, we identified key driving factors through correlation analysis, chi-square tests, and extensive literature review. Results The CA model demonstrated robustness in capturing the spatio-temporal dynamics of confirmed IR states in the vector populations. In our model, the key driving factors included insecticide usage, agricultural activities, human population density, Land Use and Land Cover (LULC) characteristics, and environmental variables. Conclusions The CA model developed offers a robust tool for countries that have limited data on confirmed IR in malaria vectors. The embrace of a dynamical modeling approach and accounting for evolving conditions and influences, contribute to deeper understanding of IR dynamics, and can inform effective strategies for malaria vector control, and prevention in regions facing this critical health challenge.

Abstract Rainfall serves as a significant factor contributing to slope stability challenges in mountainous areas, and simulating the process of slope rainwater movement is a crucial approach for analyzing the stability of slopes triggered by rainfall. By combining computer numerical simulation technology with traditional hydraulic and hydrological calculation theories, it is possible to create an efficient and precise rainwater movement model that can simulate and analyze the process of rainwater movement on slopes. Utilizing natural slopes as the focal point of our research, the cellular automaton method was applied to simulate rainfall runoff on slopes, and a Cellular Automata (CA) based model for rainwater movement process was developed. This model modified the Green-Ampt (G-A) infiltration model by adopting an elliptical water content curve and introducing a coefficient that quantifies the ratio of saturated to unsaturated depth. Additionally, we refined the rules governing runoff generation and convergence within the slope and on its surface, enabling a comprehensive simulation of the entire rainwater movement process on the slope. Furthermore, the effectiveness of the model was validated through analytical solutions derived from simplified assumptions, laboratory experiments on infiltration and runoff in the flume, and a case study of a natural slope. The results show that the infiltration calculation results of the rainwater movement model are closer to the experimental values, and their overall values are slightly higher than the measured values, which are basically consistent with the model test results; The runoff calculation results show a phenomenon of initially increasing and gradually approaching the measured values compared to the measured values. When applying the model to an actual slope, it was found that the model comprehensively accounts for the influence of slope seepage, infiltration and runoff process, has better performance compared to G-A modified model. The model can be used to describe the spatial distribution and temporal variation of infiltration and runoff processes.

Urban growth has accelerated significantly in recent decades, categorized into two spatial patterns: adjacent and outlying growth. Traditional Cellular Automata (CA)-based models excel at simulating adjacent growth but exhibit limitations in modeling outlying growth, which can be summarized into two issues: 1) the overestimation of neighborhood effects caused by models’ excessive dependence on neighboring cell states; and 2) the ignorance of spatial heterogeneity in the relative importance of land suitability and neighborhood effects on urban growth. To address these problems, a novel CA model with Separate Extraction and Adaptive Fusion of land suitability and neighborhood effects (SEAF-CA) is proposed. In this model, a dual-path convolution structure is employed to extract spatial features from driving factors and cell states; geographical coordinates of each cell then input into a multilayer perceptron to derive spatially varying weights for feature fusion. Finally, the derived conversion probability is integrated with CA to simulate urban growth. Land use data collected from 2000 to 2020 in Wuhan are selected to evaluate the proposed model. Experimental results illustrate that SEAF-CA outperforms three typical CA models, achieving the closest outlying growth proportion to reality and the highest simulation accuracy. In addition, the source code of SEAF-CA is now available at GitHub (https://github.com/ohXu/SEAF-CA).

Abstract Human LULCC is the many driver of environmental changes. Accurate and up-to-date current and predicted information on LULCC is important in land use planning and natural resource management; however, in Zambia, detailed information on LULCC is insufficient. Therefore, this study assessed the dynamics of LULC change (2000–2020) and future projections (2020–2030) for Zambia. The ESA CCI land cover maps, which have been developed from Sentinel-2 images were used in this study. This dataset has a grid spatial resolution of 300 m for the 2000, 2010 and 2020. The 31 ESA CCI Classification were reclassified into ten (10) local Classifications using the r.class module in QGIS 2.18.14. The 2000 and 2010 LULC maps were used to simulate the 2020 LULC scenario using Artificial Neural Network (Multi-layer Perception) algorithms in Modules for Land Use Change Evaluation (MOLUSCE) plugin in QGIS 2.18.14. The 2010 and 2020 maps were used to predict the 2030 LULC classes. The reference 2020 and predicted 2020 LULC maps were used to validate the model. Predicted against observed 2020 LULC map, Kappa (loc) statistic was 0.9869. The 2020 LULC patterns was successfully simulated using ANN-MLP with accuracy level of 95%. LULC classes were predicted for 2030 using the 2010–2020 calibration period. The predicted 2030 LULC types shows an increase in built-up (71.44%) and decrease in cropland (0.73%) with reference to 2020 LULC map. Dense forest (0.19%), grassland (0.85%) and bare land (1.37%) will reduce from 2020–2030. However, seasonally flooded, sparse forest, shrub land, wetland and water body will increase marginally. The largest LULC change is from forest into other LULC types. The insights from this study show that ANN-MLP can be used to predict LULCC, and that the generated information can be employed in land use planning and National Adaptation Plans at regional and national scale.

Mapanget District is one of the administrative parts of Manado City which has service functions, namely housing and new settlements, tourism, and trade and services. The Mapanget District is part of the new city planning in the 2015–2019 RPJMN to account for unequal development in the city core. Predicting the development of residential land use is crucial to avoid uncontrollably detrimental effects on suburban communities. To forecast land development over 20 years, from 2022 to 2042, this study will employ spatial analysis based on the Geography Information System-Cellular Automata and weighting analysis using the Analytic Hierarchy Process (AHP) technique to analyze potential deviations in the spatial pattern development of Manado City settlements, particularly Mapanget District. The software used for this research's simulation procedure is LanduseSim 2.3.1, QGIS, and Archmap 10.8. According to the projection results, residential land use will vary during the following 20 years, starting in 1956.75 Ha, or a 201% increase, resulting from the most important driving factor, which is closeness to existing towns. 1106.79 Ha is the possible deviation depending on the direction of the spatial pattern plan for residential structures. It is advised to plan tall buildings, particularly for settlements, as the current residential land in Mapanget District exceeds both the carrying capacity and the land capacity as of 2042, according to the prediction results.

Land use modelling is essential to understanding future land use and change dynamics. Analyses of land use and change using Cellular Automata (CA) have been frequent in the literature since the latter were introduced to geography in the 1970s. In this paper, the statistical subsections – city blocks – of a previous analysis of Lisbon's commercial structure are used as the cells of a CA. A multi-layer perceptron (MLP) models the transition potential of those cells between 1995 and 2020, considering the impact of street centrality, subway development and other proxies of urban growth. The results confirm that commercial growth and change can be simulated as a function of street centrality and that using Local integration and Choice as drivers of a CA may help improve predictions of commercial growth and change. Moreover, the CA results reinforce that street centrality positively affects street commerce, especially when combined with access to transit and amenities. While these results can already inform planning practice and policymaking, the framework presented in this paper may be replicated to simulate ​future commercial land use, thus helping planners and policymakers prepare for change.

In Canada, the periodic circulation of West Nile Virus (WNV) is difficult to predict and, beyond climatic factors, appears to be related to the migratory movements of infected birds from the southern United States. This hypothesis has not yet been explored in a spatially distributed model. The main objective of this work was to develop a spatially explicit dynamic model for the transmission of WNV in Canada, that allows us to explore non-climate related hypotheses associated with WNV transmission. A Cellular Automata (CA) approach for multiple hosts (birds and humans) is used for a test region in eastern Ontario, Canada. The tool is designed to explore the role of host and vector spatial heterogeneity, host migration, and vector feeding preferences.We developed a spatialized compartmental SEIRDS-SEI model for WNV transmission with a study region divided into 4 km2 rectangular cells. We used 2010–2021 bird data from the eBird project and 2010–2019 mosquito data collected by Ontario Public Health to mimic bird and mosquito seasonal variation. We considered heterogeneous bird densities (high and low suitability areas) and homogeneous mosquito and human densities. In high suitability areas for birds, we identified 5 entry points for WNV-infected birds. We compared our simulations with pools of WNV-infected field collected mosquitoes. Simulations and sensitivity analyses were performed using MATLAB software.The results showed good correspondence between simulated and observed epidemics, supporting the validity of our model assumptions and calibration. Sensitivity analysis showed that a 5% increase or decrease in each parameter of our model except for the biting rate of bird by mosquito (c(B,M)) and mosquito natural mortality rate (dM), had a very limited effect on the total number of cases (newly infected birds and humans), prevalence peak, or date of occurrence. We demonstrate the utility of the CA approach for studying WNV transmission in a heterogeneous landscape with multiple hosts.