Predicting large-scale spatial patterns of marine meiofauna: implications for environmental monitoring by Fabiane Gallucci, Gustavo Fonseca, Danilo C Vieira, Luciana Erika Yaginuma, Paula Foltran Gheller, Simone Brito, Thais Navajas Corbisier

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Investigating Durban’s Morphological Dynamics and Spatial Prediction Techniques for Urban Geography Pedagogy by Tolulope Ayodeji Olatoye, Raymond Nkwenti Fru

“…This study highlighted the transformative potential of geospatial analysis and spatial prediction techniques in using the dynamic morphology of Durban Metropolis as a model for fostering innovative, data-driven learning experiences. …”
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Predicting large-scale spatial patterns of marine meiofauna: implications for environmental monitoring by Fabiane Gallucci, Gustavo Fonseca, Danilo C Vieira, Luciana Erika Yaginuma, Paula Foltran Gheller, Simone Brito, Thais Navajas Corbisier

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A Spatially Informed Machine Learning Method for Predicting Sound Field Uncertainty by Xiangmei Chen, Chao Li, Haibin Wang, Yupeng Tai, Jun Wang, Cyrille Migniot

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Flood Classification and Improved Loss Function by Combining Deep Learning Models to Improve Water Level Prediction in a Small Mountain Watershed by Rukai Wang, Ximin Yuan, Fuchang Tian, Minghui Liu, Xiujie Wang, Xiaobin Li, Minrui Wu

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Modelling the Current and Future Pollutant Emission from Non-Road Machinery: A Case Study in Shanghai by Rui Chen, Xuerui Yang, Lei Xi, Huajun Zhen, Guangli Xiu

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Predicting communities with high tuberculosis case-finding efficiency to optimise resource allocation in Pakistan: comparing the performance of a negative binomial spatial lag model with a Bayesian machine-learning model by Hasan Tahir, Frank Cobelens, Christina Mergenthaler, Mirjam I Bakker, Tanveer Ahmed, Jake D Mathewson, Daniella Brals, Abdullah Latif, Stephanie Lako, Andreas Werle van der Merwe, Matthys Potgieter, Vincent Meurrens, Zia Samad, Ente Rood

“…A predictive negative binomial regression (NBR) model was created, and the presence of spatial autocorrelation was examined to account for spatial dependencies in the outcome variable. …”
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CALFUSE-KAN: A Multi-Scale Feature Fusion Network for Aircraft Trajectory Prediction by Yalan Zhang, Ruxin Sun, Fang Liu, Yongchao Wei

Subjects: “…Trajectory prediction…”
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Solar and Wind 24 H Sequenced Prediction Using L-Transform Component and Deep LSTM Learning in Representation of Spatial Pattern Correlation by Ladislav Zjavka

“…Spatiotemporal correlations between meteo-inputs and wind–solar outputs in an optimal regional scale are crucial for developing robust models, reliable in mid-term prediction time horizons. …”
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Spatial Modeling of Douglas‐Fir Plantations in Italy After 120 Years of Experimentation by Maurizio Marchi, Martina Cocozza, Gabriele Bucci, Paolo Iovieno

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Comparative Analysis of Different Interpolation Methods in Modeling Spatial Distribution of Monthly Precipitation by Yılmaz İçağa, Emin Taş

“…It is the main objective of the study that Geographic Information Systems (GIS) techniques are used to compare widely preferred interpolation methods and to model the spatial distribution of monthly precipitation values for prediction in ungauged areas in Akarcay Sinanpasa and Suhut sub-basins, Turkey. …”
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Exploring and Modeling the Spatial Variability of Soil Erosion in Tana Basin, Northwestern Ethiopia by Derege Tsegaye Meshesha, Anteneh Wubet Belay, Gizachew Ayalew Tiruneh, Enyew Adgo, Tiringo Yilak Alemayehu, M. Chandrakala, José Miguel Reichert

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Predicting high-quality ecologically suitable areas of Astragalus mongholicus Bunge based on secondary metabolites content using Biomod2 model by Jingyi Zhou, Xiaoting Zhang, Tongtong Huo, Huizhong Xu, Fanyun Meng, Ning Xu, Cheng Peng

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Unravelling the importance of spatial and temporal resolutions in modeling urban air pollution using a machine learning approach by Alireza Zhalehdoost, Mohammad Taleai

“…In the spatial phase, emission inventory data are aggregated at three spatial resolutions (500 m, 750 m, and 1000 m) to evaluate their effect on model performance in predicting PM and NOx concentrations. …”
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Corrigendum: Spatial-temporal evolution characteristics and driving factors of carbon emission prediction in China-research on ARIMA-BP neural network algorithm by Sanglin Zhao, Zhetong Li, Hao Deng, Xing You, Jiaang Tong, Bingkun Yuan, Zihao Zeng

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A spatiotemporal model for urban taxi Origin–Destination prediction based on Multi-hop GCN and Hierarchical LSTM by Jiang Rong, Wangtu Xu, Yanjie Wen

“…On the PEMS08, the single-path version of DBSTNet outperforms state-of-the-art demand prediction models with an 8.29% increase in SMAPE, underscoring the scalability of the proposed MS-HT for spatial–temporal computation. …”
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Machine learning-driven framework for realtime air quality assessment and predictive environmental health risk mapping by M. Rajesh, R. Ganesh Babu, Usha Moorthy, Sathishkumar Veerappampalayam Easwaramoorthy

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Predicting fine-scale downstream migratory movement of Atlantic salmon smolt (Salmo salar) in front of a hydropower plant by Kim M. Bærum, Ana T. Silva, Henrik Baktoft, Karl Ø. Gjelland, Finn Økland, Torbjørn Forseth

“…We present a spatially explicit individual-based model for predicting the movement of Atlantic salmon smolts in regulated rivers in Norway, parameterised for smolt movements in the River Mandal and the River Orkla. …”
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MEA‐seqX: High‐Resolution Profiling of Large‐Scale Electrophysiological and Transcriptional Network Dynamics by Brett Addison Emery, Xin Hu, Diana Klütsch, Shahrukh Khanzada, Ludvig Larsson, Ionut Dumitru, Jonas Frisén, Joakim Lundeberg, Gerd Kempermann, Hayder Amin

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Research on short-term traffic flow prediction based on the PCC-IGA-LSTM model by Junxi Zhang, Shiru Qu, Yang Bi, Lijing Ma

“…To effectively address the spatial–temporal feature mining problem in short-term traffic flow prediction for complex road networks, a new method that combined the Pearson correlation coefficient (PCC) and improved genetic algorithm to optimize the long short-term memory model (IGA-LSTM) was constructed. …”
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