Understanding forest fragmentation dynamics and identifying drivers for forest cover loss using random forest models to develop effective forest management strategies in North-East... by Soumik Mahapatra, Bishal Kumar Majhi, Mriganka Shekhar Sarkar, Debajit Datta, Arun Pratap Mishra, Upaka Rathnayake

“…Furthermore, spatial and non-spatial Random Forest regression techniques were employed to identify key drivers of forest loss within the landscape. …”
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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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Citizen-led initiatives and hydro-meteorological risks reduction: Who is implementing nature-based solutions? by Yaw Agyeman Boafo, Kirk B. Enu, Kofi Dua Agyei, Jude Dokbila Kolog

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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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The contributions of natural and anthropogenic climate change on water resources reduction in Zarrinehroud basin of Lake Urmia by Sahar Esmaeili, Jamil Bahrami, Bahareh Kamali

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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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Study on the Application of Determinant-MTMD(TMDD) Vibration Reduction in Cable-Supported Pedestrian Suspension Bridge by Zhen Li, Dejian Li, Jincheng Li, Chenxi Zhao, Xuezhi Cheng, Yao Lu

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