SPFDNet: Water Extraction Method Based on Spatial Partition and Feature Decoupling by Xuejun Cheng, Kuikui Han, Jian Xu, Guozhong Li, Xiao Xiao, Wengang Zhao, Xianjun Gao

“…Therefore, this paper proposes a water feature extraction network with spatial partitioning and feature decoupling. To ensure that the water features are extracted with deep semantic features and stable spatial information before decoupling, we first design a chunked multi-scale feature aggregation module (CMFAM) to construct a context path for obtaining deep semantic information. …”
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Prediction of Urban Construction Land Carbon Effects (UCLCE) Using BP Neural Network Model: A Case Study of Changxing, Zhejiang Province, China by Qinghua Liao, Xiaoping Zhang, Zixuan Cui, Xunxi Yin

“…The results demonstrate that the BP neural network model effectively predicts the different types of UCLCE, with an average error rate of 30.10%. (1) The total effect and intensity effect exhibit different trends in the study area, and a carbon effect table for different types of UCL is established. (2) The spatial distribution characteristics of UCLCE reveal a distinct reverse-L pattern (“┙”-shaped layout) with positive spatial correlation (Moran’s I = 0.11, <i>p</i> < 0.001). (3) The model’s core practical value lies in enabling forward-looking assessment of carbon effects in urban planning schemes and precise quantification of emissions reduction benefits. …”
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Multidimensional assessment of the spatiotemporal evolution, driving mechanisms, and future predictions of urban heat islands in Jinan, China by Lingye Tan, Tiong Lee Kong Robert, Yan Zhang, Siyi Huang, Ziyang Zhang

“…By analyzing the evolution of LST in Jinan city, China, from 2002 to 2022, and forecasts future trends using advanced spatial analysis and predictive modeling techniques. …”
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GIS-based calculation method to predict mining subsidence in flat and inclined mining: A comparative case study by Ibrahim Djamaluddin, Poppy Indrayani, Yue Cai, Yujing Jiang

“…All subsidence computations are implemented within GIS, where spatial components are used to conduct the subsidence prediction analysis. …”
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Fire Intensity and spRead forecAst (FIRA): A Machine Learning Based Fire Spread Prediction Model for Air Quality Forecasting Application by Wei‐Ting Hung, Barry Baker, Patrick C. Campbell, Youhua Tang, Ravan Ahmadov, Johana Romero‐Alvarez, Haiqin Li, Jordan Schnell

“…FIRA aims to improve the performance of AQF models by providing realistic, dynamic fire characteristics including the spatial distribution and intensity of fire radiative power (FRP). …”
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Integrating Systematic Surveys With Historical Data to Model the Distribution of Ornithodoros turicata americanus, a Vector of Epidemiological Concern in North America by Sebastian Botero‐Cañola, Carson Torhorst, Nicholas Canino, Lorenza Beati, Kathleen C. O'Hara, Angela M. James, Samantha M. Wisely

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A Deep Learning-Based Trajectory and Collision Prediction Framework for Safe Urban Air Mobility by Junghoon Kim, Hyewon Yoon, Seungwon Yoon, Yongmin Kwon, Kyuchul Lee

“…To unify spatial dimensions, the model uses Earth-Centered Earth-Fixed (ECEF) coordinates, enabling efficient Euclidean distance calculations. …”
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Predicting the first seasonal occurrence of <i>Lobesia botrana</i> and <i>Eupoecilia ambiguella</i> in Austria using new multiple linear regression models by Kerstin Kolkmann, Sylvia Blümel, Josef Eitzinger

“…The validation results showed high prediction accuracy for all six newly generated MLR models for L. botrana and for two out of six newly generated MLR models for E. ambiguella (R2 > 0.6; RMSE < 4.0; | BIAS | < 2.5). …”
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A Review of Predictive Software for the Design of Community Microgrids by Mina Rahimian, Lisa D. Iulo, Jose M. Pinto Duarte

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Satellite-Based Prediction of Water Turbidity Using Surface Reflectance and Field Spectral Data in a Dynamic Tropical Lake by Elsa Pereyra-Laguna, Valeria Ojeda-Castillo, Enrique J. Herrera-López, Jorge del Real-Olvera, Leonel Hernández-Mena, Ramiro Vallejo-Rodríguez, Jesús Díaz

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Surface water quality prediction based on BOA-BiLSTM model(基于BOA-BiLSTM模型的地表水水质预测) by 章佩丽(ZHANG Peili), 赵文雅(ZHAO Wenya), 许旭敏(XU Xumin), 包鑫磊(BAO Xinlei)

“…The results indicate that the predicted RMSE of NH3—N by the BOA-BiLSTM model for the next four hours is respectively 0.213 2, 0.368 9, 0.332 7 and 0.374 0, the predicted RMSE of TP is respectively 0.024 6, 0.032 1, 0.042 2 and 0.033 4. …”
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Estimativa da produção de sedimentos mediante uso de um modelo hidrossedimentológico acoplado a um SIG Sediment yield prediction through the use of a runoff-erosion model coupled t... by Richarde M. da Silva, Celso A. G. Santos

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Improved urban runoff prediction using high-resolution land-use, imperviousness, and stormwater infrastructure data applied to a process-based ecohydrological model. by Jonathan Halama, Robert McKane, Bradley Barnhart, Paul Pettus, Allen Brookes, Kevin Djang, Vivian Phan, Sonali Chokshi, James Graham

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Application of deep learning in cloud cover prediction using geostationary satellite images by Yeonjin Lee, Seyun Min, Jihyun Yoon, Jongsung Ha, Seungtaek Jeong, Seonghyun Ryu, Myoung-Hwan Ahn

“…We explore the effectiveness of advanced deep learning techniques – specifically 3D Convolutional Neural Networks, Long Short-Term Memory networks, and Convolutional Long Short-Term Memory (ConvLSTM) – using GK2A cloud detection data, which provides updates every 10 minutes at 2 km spatial resolution. Our model utilizes training sequences of four past hourly images to predict cloud cover up to 4 hours ahead. …”
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Optimizing Traffic Speed Prediction Using a Multi-Objective Genetic Algorithm-Enhanced RNN for Intelligent Transportation Systems by C. Swetha Priya, F. Sagayaraj Francis

“…However, developing these models involves several challenges, including understanding spatiotemporal nonlinearities, making accurate predictions, minimizing prediction time, and reducing model complexity. …”
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Spatial Downscaling of TRMM Precipitation Using Geostatistics and Fine Scale Environmental Variables by No-Wook Park

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Improving Distribution Prediction by Integrating Expert Range Maps and Opportunistic Occurrences: Evidence From Japanese Sea Cucumber by Bingqing Xiao, Songxi Yuan, Ákos Bede‐Fazekas, Jinxin Zhou, Xingyu Song, Qiang Lin, Lei Cui, Zhixin Zhang

“…We first fitted SDMs for this species based on opportunistic occurrence records via four modeling algorithms, then built two types of ensemble models using stacked generalization: an ensemble model that solely used four model predictions and an expert‐informed ensemble model that further accounted for distance to the IUCN expert range map. …”
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Numerical solution of a spatio-temporal gender-structured model for hantavirus infection in rodents by Raimund BÜrger, Gerardo Chowell, Elvis GavilÁn, Pep Mulet, Luis M. Villada

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Analysis of the spatial distribution of the Siberian silk moth outbreak area based on terrain features in the Siberian mountain southern taiga forests by Svetlana M. Sultson, Andrey A. Goroshko, Denis A. Demidko, Pavel V. Mikhaylov, Olga A. Slinkina, Nadezhda N. Kulakova

“…An improved understanding of the ecology of the pest population in mountainous terrain will facilitate the development of a more effective monitoring system and the use of a digital terrain model to predict the spread of the outbreak. This will allow the implementation of timely active forest protection measures. …”
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Epidemiological dynamics of dengue in Peru: Temporal and spatial drivers between 2000 and 2022. by Katherine Susan Rufasto Goche, María Victoria Lizarbe Castro, Glenn Alberto Lozano Zanelly, Washington Melvin Lira Camargo, Elizabeth Yovana Ascayo Velasquez, Alexis G Murillo Carrasco, Daysi Diaz-Obregón

“…The findings emphasize the urgent need for innovative approaches and provides actionable insights into regional dynamics and highlights critical areas for research, including predictive climate-disease modeling and the integration of molecular surveillance. …”
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