Forecasting Lattice and Point Spatial Data: Comparison of Unilateral and Multilateral SAR Models by Carlo Grillenzoni

“…Spatial auto-regressive (SAR) models are widely used in geosciences for data analysis; their main feature is the presence of weight (W) matrices, which define the neighboring relationships between the spatial units. …”
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The Impact of Radiosounding Observations on Numerical Weather Prediction Analyses in the Arctic by T. Naakka, T. Nygård, M. Tjernström, T. Vihma, R. Pirazzini, I. M. Brooks

“…Abstract The radiosounding network in the Arctic, despite being sparse, is a crucial part of the atmospheric observing system for weather prediction and reanalysis. The spatial coverage of the network was evaluated using a numerical weather prediction model, comparing radiosonde observations from Arctic land stations and expeditions in the central Arctic Ocean with operational analyses and background fields (12‐hr forecasts) from European Centre for Medium‐Range Weather Forecasts for January 2016 to September 2018. …”
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Integration of geospatial techniques and machine learning in land parcel prediction by Nekkanti Haripavan, Subhashish Dey, Chimakurthi Harika Mani Chandana

“…Researchers and practitioners can customize their models by choosing the most pertinent variables for each land parcel forecasts from a wide range of spatial features. …”
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Examination of analytical shear stress predictions for coastal dune evolution by O. Cecil, N. Cohn, M. Farthing, S. Dutta, S. Dutta, A. Trautz

“…<p>Existing process-based models for simulating coastal foredune evolution largely use the same analytical approach for estimating wind-induced surface shear stress distributions over spatially variable topography. …”
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Impact of Thermospheric Mass Density on the Orbit Prediction of LEO Satellites by Changyong He, Yang Yang, Brett Carter, Kefei Zhang, Andong Hu, Wang Li, Florent Deleflie, Robert Norman, Suqin Wu

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Bayesian Adaptive Lasso for the Partial Functional Linear Spatial Autoregressive Model by Dengke Xu, Ruiqin Tian, Ying Lu

“…This study introduces a partial functional linear spatial autoregressive model which can explore the relationship between a scalar spatially dependent response variable and predictive variables containing both multiple scalar covariates and a functional covariate. …”
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Prediction of Landslide Susceptibility in the Karakorum under the Context of Climate Change by Yanqian Pei, Haijun Qiu, Yaru Zhu

“…In this work, we focused on static and dynamic environment factors and utilized the certainty factor-logistic regression (CF-LR) model to assess and predict landslide susceptibility in Taxkorgan County, located in the Karakorum. …”
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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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Change in Fractional Vegetation Cover and Its Prediction during the Growing Season Based on Machine Learning in Southwest China by Xiehui Li, Yuting Liu, Lei Wang

“…The predicted spatial change trends were consistent with the MODIS-MOD13A3-FVC and FY3D-MERSI-FVC, although the predicted FVC values were slightly higher but closer to the MODIS-MOD13A3-FVC. …”
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Crop Yield Prediction: Data Structure and Ai-Powered Methods by V. K. Kalichkin, K. Yu. Maksimovich, O. A. Aleshchenko, V. V. Aleshchenko

“…(Results and discussion) The study presents the core data structure and methods for data acquisition, along with a typical workflow for implementing predictive analytics models for crop yield prediction. …”
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Penalized Composite Likelihood Estimation for Spatial Generalized Linear Mixed Models by Mohsen Mohammadzadeh, Leyla Salehi

“…When discussing non-Gaussian spatially correlated variables, generalized linear mixed models have enough flexibility for modeling various data types. …”
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A novel approach to skin disease segmentation using a visual selective state spatial model with integrated spatial constraints by Yu Bai, Hai Zhou, Hongjie Zhu, Shimin Wen, Binbin Hu, Haotian Li, Huazhang Wang, Daji Ergu, Fangyao Liu

“…Additionally, we introduce a spatially-constrained loss function that mitigates gradient stability issues by considering the distance between label and prediction boundaries. …”
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Ecological epidemiology insights into clonorchiosis endemicity in Guangxi, China and Vietnam: a comprehensive machine learning analysis by Jin-Xin Zheng, Hui‐Hui Zhu, Shang Xia, Men‐Bao Qian, Robert Bergquist, Hung Manh Nguyen, Xiao‐Nong Zhou

“…Logistic regression achieved the highest predictive accuracy (AUC = 0.941). Climatic comparisons showed that Vietnam had a higher annual mean temperature (Bio1: 23.37 °C vs. 20.86 °C), greater temperature seasonality (Bio4: 609.33 vs. 464.92), and higher annual precipitation (Bio12: 1731.64 mm vs. 1607.56 mm) than Guangxi, contributing to spatial differences in endemicity. …”
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Evaluation of Eurasian Snow Cover Fraction Prediction Based on BCC-CSM1.1m by Cheng Fei, Li Qiaoping, Shen Xinyong, Liu Yanju, Wang Jing

“…The model ability to predict Eurasian snow cover fraction (SCF) is evaluated by using the hindcast data during 1984-2019 from the Beijing Climate Center (BCC) Climate Prediction System version 2 (CPSv2), developed based on Climate System Model BCC-CSM1.1m. …”
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Evidential deep learning-based drug-target interaction prediction by Yanpeng Zhao, Yuting Xing, Yixin Zhang, Yifei Wang, Mengxuan Wan, Duoyun Yi, Chengkun Wu, Shangze Li, Huiyan Xu, Hongyang Zhang, Ziyi Liu, Guowei Zhou, Mengfan Li, Xuanze Wang, Zhengshan Chen, Ruijiang Li, Lianlian Wu, Dongsheng Zhao, Peng Zan, Song He, Xiaochen Bo

“…Through EDL, EviDTI provides uncertainty estimates for its predictions. Experimental results on three benchmark datasets demonstrate the competitiveness of EviDTI against 11 baseline models. …”
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Prediction of Nitrogen Responses of Corn by Soil Nitrogen Mineralization Indicators by R.R. Simard, N. Ziadi, M.C. Nolin, A.N. Cambouris

“…Soil nitrogen mineralization potential (Nmin) has to be spatially quantified to enable farmers to vary N fertilizer rates, optimize crop yields, and minimize N transfer from soils to the environment. …”
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Predicting future evapotranspiration based on remote sensing and deep learning by Xin Zheng, Sha Zhang, Shanshan Yang, Jiaojiao Huang, Xianye Meng, Jiahua Zhang, Yun Bai

“…Study focus: This study validates the efficiency of Convolutional Long Short-Term Memory Network (ConvLSTM) models for site-scale ETa prediction. We enhanced the ConvLSTM model by adding a Spatial Pyramid Pooling module (SPPM) and a Multi-head Self-Attention Module (MSA-Module), creating the Multi-head Self-Attention ConvLSTM (MSA-ConvLSTM) model, which we applied to predicting regional-scale actual evapotranspiration (ETa). …”
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A Simple Predictive Enhancer Syntax for Hindbrain Patterning Is Conserved in Vertebrate Genomes. by Joseph Grice, Boris Noyvert, Laura Doglio, Greg Elgar

“…These sequences tend to be located near developmental transcription factors and are enriched in known hindbrain activating elements, demonstrating the predictive power of this simple model.<h4>Conclusion</h4>Our findings support the theory that hundreds of CNEs, and perhaps thousands of regions across the human genome, function to coordinate gene expression in the developing hindbrain. …”
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Predicting the environmental suitability for onchocerciasis in Africa as an aid to elimination planning. by Elizabeth A Cromwell, Joshua C P Osborne, Thomas R Unnasch, Maria-Gloria Basáñez, Katherine M Gass, Kira A Barbre, Elex Hill, Kimberly B Johnson, Katie M Donkers, Shreya Shirude, Chris A Schmidt, Victor Adekanmbi, Olatunji O Adetokunboh, Mohsen Afarideh, Ehsan Ahmadpour, Muktar Beshir Ahmed, Temesgen Yihunie Akalu, Ziyad Al-Aly, Fahad Mashhour Alanezi, Turki M Alanzi, Vahid Alipour, Catalina Liliana Andrei, Fereshteh Ansari, Mustafa Geleto Ansha, Davood Anvari, Seth Christopher Yaw Appiah, Jalal Arabloo, Benjamin F Arnold, Marcel Ausloos, Martin Amogre Ayanore, Atif Amin Baig, Maciej Banach, Aleksandra Barac, Till Winfried Bärnighausen, Mohsen Bayati, Krittika Bhattacharyya, Zulfiqar A Bhutta, Sadia Bibi, Ali Bijani, Somayeh Bohlouli, Mahdi Bohluli, Oliver J Brady, Nicola Luigi Bragazzi, Zahid A Butt, Felix Carvalho, Souranshu Chatterjee, Vijay Kumar Chattu, Soosanna Kumary Chattu, Natalie Maria Cormier, Saad M A Dahlawi, Giovanni Damiani, Farah Daoud, Aso Mohammad Darwesh, Ahmad Daryani, Kebede Deribe, Samath Dhamminda Dharmaratne, Daniel Diaz, Hoa Thi Do, Maysaa El Sayed Zaki, Maha El Tantawi, Demelash Abewa Elemineh, Anwar Faraj, Majid Fasihi Harandi, Yousef Fatahi, Valery L Feigin, Eduarda Fernandes, Nataliya A Foigt, Masoud Foroutan, Richard Charles Franklin, Mohammed Ibrahim Mohialdeen Gubari, Davide Guido, Yuming Guo, Arvin Haj-Mirzaian, Kanaan Hamagharib Abdullah, Samer Hamidi, Claudiu Herteliu, Hagos Degefa de Hidru, Tarig B Higazi, Naznin Hossain, Mehdi Hosseinzadeh, Mowafa Househ, Olayinka Stephen Ilesanmi, Milena D Ilic, Irena M Ilic, Usman Iqbal, Seyed Sina Naghibi Irvani, Ravi Prakash Jha, Farahnaz Joukar, Jacek Jerzy Jozwiak, Zubair Kabir, Leila R Kalankesh, Rohollah Kalhor, Behzad Karami Matin, Salah Eddin Karimi, Amir Kasaeian, Taras Kavetskyy, Gbenga A Kayode, Ali Kazemi Karyani, Abraham Getachew Kelbore, Maryam Keramati, Rovshan Khalilov, Ejaz Ahmad Khan, Md Nuruzzaman Nuruzzaman Khan, Khaled Khatab, Mona M Khater, Neda Kianipour, Kelemu Tilahun Kibret, Yun Jin Kim, Soewarta Kosen, Kris J Krohn, Dian Kusuma, Carlo La Vecchia, Van Charles Lansingh, Paul H Lee, Kate E LeGrand, Shanshan Li, Joshua Longbottom, Hassan Magdy Abd El Razek, Muhammed Magdy Abd El Razek, Afshin Maleki, Abdullah A Mamun, Ali Manafi, Navid Manafi, Mohammad Ali Mansournia, Francisco Rogerlândio Martins-Melo, Mohsen Mazidi, Colm McAlinden, Birhanu Geta Meharie, Walter Mendoza, Endalkachew Worku Mengesha, Desalegn Tadese Mengistu, Seid Tiku Mereta, Tomislav Mestrovic, Ted R Miller, Mohammad Miri, Masoud Moghadaszadeh, Abdollah Mohammadian-Hafshejani, Reza Mohammadpourhodki, Shafiu Mohammed, Salahuddin Mohammed, Masoud Moradi, Rahmatollah Moradzadeh, Paula Moraga, Jonathan F Mosser, Mehdi Naderi, Ahamarshan Jayaraman Nagarajan, Gurudatta Naik, Ionut Negoi, Cuong Tat Nguyen, Huong Lan Thi Nguyen, Trang Huyen Nguyen, Rajan Nikbakhsh, Bogdan Oancea, Tinuke O Olagunju, Andrew T Olagunju, Ahmed Omar Bali, Obinna E Onwujekwe, Adrian Pana, Hadi Pourjafar, Fakher Rahim, Mohammad Hifz Ur Rahman, Priya Rathi, Salman Rawaf, David Laith Rawaf, Reza Rawassizadeh, Serge Resnikoff, Melese Abate Reta, Aziz Rezapour, Enrico Rubagotti, Salvatore Rubino, Ehsan Sadeghi, Abedin Saghafipour, S Mohammad Sajadi, Abdallah M Samy, Rodrigo Sarmiento-Suárez, Monika Sawhney, Megan F Schipp, Amira A Shaheen, Masood Ali Shaikh, Morteza Shamsizadeh, Kiomars Sharafi, Aziz Sheikh, B Suresh Kumar Shetty, B Suresh Kumar Shetty, Jae Il Shin, K M Shivakumar, Biagio Simonetti, Jasvinder A Singh, Eirini Skiadaresi, Amin Soheili, Shahin Soltani, Emma Elizabeth Spurlock, Mu'awiyyah Babale Sufiyan, Takahiro Tabuchi, Leili Tapak, Robert L Thompson, Alan J Thomson, Eugenio Traini, Bach Xuan Tran, Irfan Ullah, Saif Ullah, Chigozie Jesse Uneke, Bhaskaran Unnikrishnan, Olalekan A Uthman, Natalie V S Vinkeles Melchers, Francesco S Violante, Haileab Fekadu Wolde, Tewodros Eshete Wonde, Tomohide Yamada, Sanni Yaya, Vahid Yazdi-Feyzabadi, Paul Yip, Naohiro Yonemoto, Hebat-Allah Salah A Yousof, Chuanhua Yu, Yong Yu, Hasan Yusefzadeh, Leila Zaki, Sojib Bin Zaman, Maryam Zamanian, Zhi-Jiang Zhang, Yunquan Zhang, Arash Ziapour, Simon I Hay, David M Pigott

“…The ROC analysis identified a mean environmental suitability index of 0·71 as a threshold to classify based on the location with the largest mean prediction within the IU. Of the IUs considered for mapping surveys, 50·2% exceed this threshold for suitability in at least one 5 × 5-km location. …”
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Multiphysics property prediction from hyperspectral drill core data by A. V. Kamath, S. T. Thiele, M. Kirsch, R. Gloaguen

“…These findings lay the groundwork for building deep learning models that predict physical and mechanical rock properties from hyperspectral data. …”
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