Intercomparison of Machine Learning Models for Spatial Downscaling of Daily Mean Temperature in Complex Terrain by Sudheer Bhakare, Sara Dal Gesso, Marco Venturini, Dino Zardi, Laura Trentini, Michael Matiu, Marcello Petitta

“…We compare three machine learning models—artificial neural network (ANN), random forest (RF), and convolutional neural network (CNN)—for spatial downscaling of temperature at 2 m above ground (T2M) from a 9 km ERA5-Land reanalysis to 1 km in a complex terrain area, including the Non Valley and the Adige Valley in the Italian Alps. …”
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Modelling Salmo trutta Complex Spatial Distribution in Central Italy: A Random Forest Approach Revealing Underrepresented Lowland Populations Based on Spatially‐Explicit Predictors by Lorenzo Talarico, Elena Catucci, Marco Martinoli, Michele Scardi, Lorenzo Tancioni

“…ABSTRACT Species distribution models are powerful tools to infer ecology and support management of conservation and socio‐economic valuable taxa, such as brown trout (Salmo trutta complex). Using a random forest approach, we modelled its distribution in central Italy watercourses, using recent presences/absences and eight environmental/bioclimatic predictors. …”
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Autonomous modal analysis method for industrial robots considering dynamic spatial sensitivity and excitation randomness by Yongkang Jiao, Xubing Chen, Yili Peng, Xinyong Mao, Qiushuang Guo

“…To address these challenges, this paper proposes an autonomous modal analysis method that considers the dynamic spatial sensitivity of robots and the randomness of the excitation frequency band and direction. …”
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Definition of Spatial Copula Based Dependence Using a Family of Non‐Gaussian Spatial Random Fields by András Bárdossy, Sebastian Hörning

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Emergence of a complex network structure on a Spatial Prisoner's Dilemma. by Tomoko Sakiyama, Akihiro Takahara

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ОPTIMAL ALGORITHM OF COMPLEX PROCESSING OF INFORMATION ABOUT THE SPATIAL POSITION OF AEROLOGICAL RADIOSONDE by E. A. Bolelov, Yu. M. Ermoshenko

“…The problem of optimal algorithm synthesis of complex signal processing of satellite navigation systems GLONASS/GPS, relayed from the board and aerological radiosonde output aerological radar using the methods of Markov random processes, estimation theory is considered in this article. …”
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Assessment of spatial autocorrelation and scalability in fine-scale wildfire random forest prediction models by Madeleine Pascolini-Campbell, Joshua B. Fisher, Kerry Cawse-Nicholson, Christine M. Lee, Natasha Stavros

“…Machine learning methods such as random forests provide an empirical framework that are high-accuracy, computationally efficient, interpretable and able to model complex ecological relationships. …”
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Improving crop rotation classification using a random forest model incorporating spatial heterogeneity by Xiaomi Wang, Qi Tang, Kang Yang

“…To overcome this limitation, an improved method named random forest based on rotation zoning strategy (RF_RZS) that classifies crop rotations under the consideration of spatial heterogeneity is proposed. …”
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A population spatialization method based on the integration of feature selection and an improved random forest model. by Zhen Zhao, Hongmei Guo, Xueli Jiang, Ying Zhang, Changjiang Lu, Can Zhang, Zonghang He

“…The random forest (RF) model is widely used in population spatialization studies. …”
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The T-DBSCAN Algorithm for Stopover Site Identification of Migration Birds Based on Satellite Positioning Data by Xinwu He, Xiqun Liu, Jiajia Liu, Youwen Li, Zhenggang Xu, Ping Mo, Tian Huang

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Spatial autocorrelation in machine learning for modelling soil organic carbon by Alexander Kmoch, Clay Taylor Harrison, Jeonghwan Choi, Evelyn Uuemaa

“…Spatial autocorrelation, the relationship between nearby samples of a spatial random variable, is often overlooked in machine learning models, leading to biased results. …”
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Out of randomness: How evolution benefits from modularity by Chunxiuzi Liu, Shaohua Tang, Jingxi Liu, Jiashuo Ye, Lanxin Ma, Bingning Liu, Lu Peng, Jiaxin Dong, Linjie Que, Binbin Hong, Yu Liu

“…This study employs genetic algorithms (GAs) to quantitatively explore how evolution-like processes, marked by mutation and crossover, search for complex solutions. Compared to random search, GAs significantly improve the probability of finding solutions, especially complex ones. …”
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Mastering data complexity beyond traditional computation by Ruedi Stoop

“…Applied to ‘symbolic dynamics data’ (sequences composed of a finite number of symbols representing distinct system states), excess entropies enable the distinction of various degrees of periodic, unstable-nonchaotic, chaotic, and random dynamics, in an convenient manner even for systems of high complexity. …”
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Random Cross-Validation Produces Biased Assessment of Machine Learning Performance in Regional Landslide Susceptibility Prediction by Chandan Kumar, Gabriel Walton, Paul Santi, Carlos Luza

“…The performance statistics of these models are vital for assessing their reliability, which is typically obtained using the random cross-validation (R-CV) method. However, R-CV has a major drawback, i.e., it ignores the spatial autocorrelation (SAC) inherent in spatial datasets when partitioning the training and testing sets. …”
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OＲB-feature Based Augmented Ｒeality under Complex Scenes by WANG Ai-li, HU Chang-yu, HAN Chuang

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Mean limiting pressure factors determination in contiguous pile walls using RAFELA and nonlinear regression models in spatially random soil by Divesh Ranjan Kumar, Sittha Kaorapapong, Warit Wipulanusat, Suraparb Keawsawasvong

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Choosing blocks for spatial cross-validation: lessons from a marine remote sensing case study by Andy Stock, Andy Stock

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Landslide spatial prediction using data-driven based statistical and hybrid computational intelligence algorithms by Xia Zhao, Wei Chen, Paraskevas Tsangaratos, Ioanna Ilia, Qingfeng He

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Dual-Scale Complementary Spatial-Spectral Joint Model for Hyperspectral Image Classification by Huayue Chen, Yue Sun, Xiang Li, Bochuan Zheng, Tao Chen

“…In the postprocessing stage, a sub-Markov random walk-based spatial probability optimization method is proposed, which models the spatial association of neighboring pixels, retaining complex textures as well as weak edge information to optimize the classification probability. …”
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Constructing an Ecological Spatial Network Optimization Framework from the Pattern–Process–Function Perspective: A Case Study in Wuhan by An Tong, Yan Zhou, Tao Chen, Zihan Qu

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