Data sharing and GRA weight optimization for power prediction of distributed photovoltaic power plant considering missing NWP information by YANG Xiyun, YANG Yan, MENG Lingzhuochao, PENG Yan, WANG Chenxu

“…On this basis, this paper proposes a power prediction model for distributed photovoltaic power plant based on data sharing and grey relation analysis (GRA) weight optimization. …”
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Monitoring land-use changes and predicting their spatio-temporal trends in Hamedan City by Naser Shafiei Sabet, Faranak Feyzbabaei cheshmeh sefidi

“…After land use detection and its changes, the trend of these changes was predicted in 2050 using the automatic cell model and Markov chain due to its high ability to detect spatial-spatial component changes.Results and discussion: Results indicated that the growth and development of urbanization in this metropolis have led to the city's expansion in this area. …”
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Prevalence, associated risk factors and satellite imagery analysis in predicting soil-transmitted helminth infection in Nakhon Si Thammarat Province, Thailand by Jarawadee Muenjak, Jutarat Thongrod, Chanakan Choodamdee, Pongphan Pongpanitanont, Manachai Yingklang, Tongjit Thanchomnang, Sakhone Laymanivong, Penchom Janwan

“…We developed an innovative predictive model by integrating convolutional neural networks (CNNs) for land-use classification of satellite imagery with artificial neural networks (ANNs) following dimensionality reduction through principal component analysis (PCA). …”
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Modeling Canopy Height of Forest–Savanna Mosaics in Togo Using ICESat-2 and GEDI Spaceborne LiDAR and Multisource Satellite Data by Arifou Kombate, Guy Armel Fotso Kamga, Kalifa Goïta

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Assessing conservation priorities for seahorses in Brazil reveals gaps in current protected areas by Anna Karolina Martins Borges, Vanessa M. Adams, Rômulo Romeu Nóbrega Alves, Tacyana Pereira Ribeiro Oliveira

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Using the SWAT model to characterise the water regime of soils in agrolandscapes by E. V. Shein, A. G. Bolotov, A. V. Dembovetskiy, D. Yu. Usenko, N. A. Kharkhardinov, Yu. I. Vernyuk

“…Numerical methods for representing the hydrological regimes of soils in the agricultural landscape are based on physically validated mathematical models of the soil water movement and spatial GIS information, which together allow to calculate, analyze and predict soil water regime, runoff in the scale of watersheds, substance transport in the soil profile, leaching processes, as well as the content of available soil moisture reserves in the agrolandscape structure – which is important for practice. …”
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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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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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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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Comprehensive propagation of errors for the prediction of woody biomass by Stephen H. Roxburgh, Keryn I. Paul

“…Recommendations for reducing errors in predicted biomass include increasing field survey sample size, adopting field survey designs that ensure spatial representativeness and improving moisture content measurement protocols and increasing the moisture content sample size during allometric model development. …”
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Improving spatial resolution of Aqua MODIS and GCOM-C chlorophyll-a data for Cyprus coastal waters monitoring by Sofiia Drozd, Nataliia Kussul, Andrii Shelestov

“…Four images from spring-summer 2024 were selected for analysis, with Sentinel-3 spectral bands used as predictors and both multiple linear regression and random forest models applied. The results indicate that linear regression predicts higher coastal Chl-a values, while random forest smooths spatial gradients. …”
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ELM2.1-XGBfire1.0: improving wildfire prediction by integrating a machine learning fire model in a land surface model by Y. Liu, H. Huang, S.-C. Wang, T. Zhang, D. Xu, Y. Chen

“…Evaluated against the historical burned area from Global Fire Emissions Database 5 from 2001–2019, the ELM2.1-XGBFire1.0 outperforms process-based fire models in terms of spatial distribution and seasonal variations. …”
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Urban Traffic Travel Time Short-Term Prediction Model Based on Spatio-Temporal Feature Extraction by Leilei Kang, Guojing Hu, Hao Huang, Weike Lu, Lan Liu

“…In order to improve the accuracy of short-term travel time prediction in an urban road network, a hybrid model for spatio-temporal feature extraction and prediction of urban road network travel time is proposed in this research, which combines empirical dynamic modeling (EDM) and complex networks (CN) with an XGBoost prediction model. …”
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A prediction model of soil organic carbon into river and its driving mechanism in red soil region by Yanhu He, Yuyin Yang, Daoguo Xu, Zirui Wang

“…This study integrates the Soil and Water Assessment Tool (SWAT) for sediment yield simulation and a Soil Organic Carbon Content (SOCC) model to quantify SOCR at the basin scale. A Random Forest-based prediction model was developed to explore the spatial-temporal variability and driving mechanisms of SOCR in the Dongjiang River Basin (DRB), a representative red soil region in southern China. …”
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Modeling Porosity Distribution Strategies in PEM Water Electrolyzers: A Comparative Analytical and Numerical Study by Ali Bayat, Prodip K. Das, Suvash C. Saha

“…Despite this, conventional models often oversimplify key components, such as porous transport and catalyst layers, by assuming constant porosity and neglecting the spatial heterogeneity found in real electrodes. …”
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<strong>Hybrid neural network with genetic algorithms for predicting distribution pattern of <em>Tetranychus urticae</em> (Acari: Tetranychidae) in cucumbers field of Ramhormoz, Ir... by Alireza Shabaninejad, Bahram Tafaghodinia, Nooshin Zandi Sohani

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Attention-Enhanced CNN-LSTM Model for Exercise Oxygen Consumption Prediction with Multi-Source Temporal Features by Zhen Wang, Yingzhe Song, Lei Pang, Shanjun Li, Gang Sun

“…Across all models, prediction errors grew during high-intensity bouts, highlighting a bottleneck in capturing non-linear physiological responses under heavy load. …”
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Drone Attitude and Position Prediction via Stacked Hybrid Deep Learning Model for Massive MIMO Applications by Abdullah Al-Ahmadi

“…This paper presents a novel stacked hybrid deep learning model for real-time prediction of drone attitude and position, specifically designed to support applications in massive Multiple-Input Multiple-Output (MIMO) systems. …”
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Evaluating PSA dynamics for predicting androgen deprivation failure with a patient specific prostate cancer model by Shengchao Zhao, Evan T. Keller, Tyler Robinson, Jinlu Dai, Alyssa Ghose, Ajjai Alva, Trachette Jackson, Harsh Vardhan Jain

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A Review of Wind Power Prediction Methods Based on Multi-Time Scales by Fan Li, Hongzhen Wang, Dan Wang, Dong Liu, Ke Sun

“…Common classification angles of wind power prediction methods are outlined. By synthesizing existing approaches through multi-time scales, from the ultra-short term and short term to mid-long term, the review further deconstructs methods by model characteristics, input data types, spatial scales, and evaluation metrics. …”
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