Using Temporal Deep Learning Models to Estimate Daily Snow Water Equivalent Over the Rocky Mountains by Shiheng Duan, Paul Ullrich, Mark Risser, Alan Rhoades

“…To train the DL models, Snow Telemetry (SNOTEL) station‐based SWE observations are used as the prediction target. …”
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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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Spatial modeling and ecological suitability of Ebola virus disease in Africa. by Lombo Baluma Didier, Lukusa Lumu Jude, Esuka Igabuchia Franck, HaoNing Wang, Xiao-Long Wang

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Using System‐Inspired Metrics to Improve Water Quality Prediction in Stratified Lakes by Kamilla Kurucz, Cayelan C. Carey, Peisheng Huang, Eduardo R. De Sousa, Jeremy T. White, Matthew R. Hipsey

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Generation of Spatial Structure of Urban Parks Based on Spatial Analysis of Agent-Based Models by Huizi KONG, Tianming LIU, Jiajie LIAO, Liu CUI

“…By adjusting model parameters, the foraging paths of slime molds in the designed site are simulated, and their spatial function distribution is analyzed. …”
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Hybrid CNN-LSTM Model with Custom Activation and Loss Functions for Predicting Fan Actuator States in Smart Greenhouses by Gregorius Airlangga, Julius Bata, Oskar Ika Adi Nugroho, Boby Hartanto Pramudita Lim

“…The hybrid model integrates CNNs for spatial feature extraction and LSTMs for temporal dependency modeling, enhanced by a custom activation function and loss function tailored for the problem’s characteristics. …”
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Spatiotemporal Dynamics and Prediction of Habitat Quality Based on Land Use and Cover Change in Jiangsu, China by Ge Shi, Chuang Chen, Qingci Cao, Jingran Zhang, Jinghai Xu, Yu Chen, Yutong Wang, Jiahang Liu

“…This study utilizes the land use data of Jiangsu Province for the years 2000, 2010, and 2020, applying the FLUS model to investigate the driving force behind land expansion and to simulate a prediction for the land use of 2030. …”
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Spatial heterogeneity and spatial bias analyses in hedonic price models: some practical considerations by Khalid Haniza

“…Estimation of a hedonic price function using Malaysian dataset of agricultural land sale values indicates spatial disaggregation and spatial dependence. However, diagnostic tests and actual estimation of spatial models do not always provide unambiguous conclusions while predicted errors do not vary all that much from those generated by simpler models. …”
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SPATIALLY INFORMED INSIGHTS: MODELING PERCENTAGE POVERTY IN EAST JAVA PROVINCE USING SEM WITH SPATIAL WEIGHT VARIATIONS by Ashabul Akbar Maulana, Achmad Fauzan

“…Diverse weighting schemes are applied based on both distance (1) and contiguity (2). The optimal predictive model utilized is the Spatial Error Model (SEM) incorporating a Distance Band Weighing (DBW) mechanism with a designated maximum distance ( ) of 75000 meters. …”
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Assessing the ecological risk and its driving forces on Islands using the Pressure-State-Response model by Meina Xu, Xiaochen Huang, Jiajun Li, Shaojun Qi, Ye Zhang, Xiaojie Zhang

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Statistical Analysis of Atmospheric Delay Gradient and Rainfall Prediction in a Tropical Region by Anik Naha Biswas, Yee Hui Lee, Wei Tao Yeo, Wai Chong Low, Ding Yu Heh, Shilpa Manandhar

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Expanding cryospheric landform inventories – quantitative approaches for underestimated periglacial block- and talus slopes in the Dry Andes of Argentina by Tamara Köhler, Anna Schoch-Baumann, Rainer Bell, Johannes Buckel, Diana Agostina Ortiz, Dario Trombotto Liaudat, Lothar Schrott

“…Random forest models produce robust and transferable predictions of both target landforms, demonstrating a high predictive power (mean AUROC values ≥0.95 using non-spatial validation and ≥0.83 using spatial validation). …”
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Orthogonal intercellular signaling for programmed spatial behavior by Paul K Grant, Neil Dalchau, James R Brown, Fernan Federici, Timothy J Rudge, Boyan Yordanov, Om Patange, Andrew Phillips, Jim Haseloff

“…We used this model to predict optimal expression levels for receiver proteins, to create an effective two‐channel cell communication device. …”
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Enhancing environmental monitoring of harmful algal blooms with ConvLSTM image prediction by Sung Jae Kim, Yongbok Cho

“…Using 3D universal kriging, the study interpolates missing HAB concentration values, transforming geospatial point data into spatially continuous grid images that serve as the foundation for predictive modeling. …”
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Spatial Compression Methods for Latent Diffusion Models by Vladimir Abramov, Mikhail Gromov

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Spatiotemporal evolution and trend prediction of coupled coordination between digital technology and manufacturing green transformation from provinces in China by Xin Huang, Xin Huang, Hongbing Deng, Hongbing Deng

“…Based on this, this paper adopts the coupling coordination model, kernel density estimation, Dagum Gini coefficient decomposition, and spatial autocorrelation to conduct a spatiotemporal evolution analysis of the coupling coordination degree (the D‐G system) of digital technology and MGT in 30 provinces (municipalities, autonomous regions) of mainland China from 2011 to 2020, and adopting the spatial Markov chain to predict its evolutionary trend. …”
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Bayesian spatio-temporal modeling and prediction of malaria cases in Tanzania mainland (2016-2023): unveiling associations with climate and intervention factors by Lembris Laanyuni Njotto, Wilfred Senyoni, Ottmar Cronie, Anna-Sofie Stensgaard

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Revisiting the "satisfaction of spatial restraints" approach of MODELLER for protein homology modeling. by Giacomo Janson, Alessandro Grottesi, Marco Pietrosanto, Gabriele Ausiello, Giulia Guarguaglini, Alessandro Paiardini

“…The most frequently used approach for protein structure prediction is currently homology modeling. The 3D model building phase of this methodology is critical for obtaining an accurate and biologically useful prediction. …”
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Flash flood prediction modeling in the hilly regions of Southeastern Bangladesh: A machine learning attempt on present and future climate scenarios by Arifur Rahman Rifath, Md Golam Muktadir, Mahmudul Hasan, Md Ashraful Islam

“…This study thus investigated flash flood susceptibility (FFS) by applying machine learning algorithms and climate projection to predict both present and future hazard scenarios in the southeastern hilly regions of Bangladesh. …”
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Optimizing ensemble learning for satellite-based multi-hazard monitoring and susceptibility assessment of landslides, land subsidence, floods, and wildfires by Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi-Niaraki, Farman Ali, Biswajeet Pradhan, Soo-Mi Choi

“…Past studies have relied mainly on traditional machine learning models, but these models do not perform well for complex spatial patterns. …”
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