Deep-learning-based canopy height model generation from sub-meter resolution panchromatic satellite imagery by Charles J Abolt, Javier E Santos, Adam L Atchley, Lucas Wells, Daithi Martin, Russell A Parsons, Rodman R Linn

“…This result underscores the value of forest structural information derived from our workflow for improving the fidelity of wildland fire simulations, among other ecological applications.…”
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Evaluating climate-induced changes in the suitable distribution and ecological niche of Prunus mira Koehne using ensemble modeling and high-performance liquid chromatography analys... by Shuning Zhang, Minglong Gao, Junxing Chen, Ao Dun, Lin Wang, Wuyun Tana, Yu-e Bai, Wenquan Bao

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Muted Amazon Rainfall Response to Deforestation in a Global Storm‐Resolving Model by Arim Yoon, Cathy Hohenegger

“…Abstract Ongoing Amazon deforestation has raised concerns about forest dieback via induced precipitation changes. …”
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Assessment of the Impact of Climate Change on Dam Hydrological Safety by Using a Stochastic Rainfall Generator by Enrique Soriano, Luis Mediero, Andrea Petroselli, Davide Luciano De Luca, Ciro Apollonio, Salvatore Grimaldi

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Joint Assessment of the Behavior of Nitrate and Saltwater Intrusion Within Negative Hydraulic Barrier Setups by Shaobo Gao, Tianyuan Zheng, Jian Luo, Xilai Zheng, Yunhai Fang, Marc Walther

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Spatio-temporal dynamics of urbanization and environmental sustainability: A predictive modelling approach to forecasting land use transitions in Vellore, India by Sai Saraswathi Vijayaraghavalu, Kumaraguru Arumugam, Sakshi Dange

“…Demographic analysis shows that the district’s population has more than doubled since 1990, rising from 3.07 million to a projected 6.26 million by 2025, further intensifying land and resource pressure. Future simulations using Cellular Automata and Artificial Neural Networks project that, by 2050, urban areas will constitute 27.1 %, whereas forest and agricultural land will decline further to 48.91 % and 9.15 %, respectively. …”
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The significance of the leaf area index for evapotranspiration estimation in SWAT-T for characteristic land cover types of West Africa by F. Merk, T. Schaffhauser, F. Anwar, Y. Tuo, J.-M. Cohard, M. Disse

“…Our findings demonstrate that the significance of detailed LAI modeling for the AET estimation is more pronounced in the forested than in the grassland region.</p>…”
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Community structure of habitat microorganisms and endophytes of narrow-ranged species Corybas fanjingshanensis at different ecological niche specificities by Huakai Zou, Jian Xu, Mingtai An, Haibo Li, Li Tian, Yuhang Ma

“…It has a highly restricted distribution and limited population size, grows primarily in mosses within alpine dwarf forests, and is highly sensitive to environmental changes, facing a high risk of extinction. …”
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Improving Wildfire Resilience in the Mediterranean Central-South Regions of Chile by Fernando Veloso, Pablo Souza-Alonso, Gustavo Saiz

“…Compared to other Mediterranean countries, Chile is clearly below in terms of investment in forest fire prevention, both in global (public investment) and specific terms ($ ha<sup data-eusoft-scrollable-element="1">−1</sup>, GDP per capita). …”
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Predicting mechanical properties of low-alloy steels using features extracted from Electron Backscatter Diffraction characterization by Yu Li, Jingxiao Zhao, Xiucheng Li, Zhao Xing, Qiqiang Duan, Xiaojun Liang, Xuemin Wang

“…Several ML methods, including Random Forest (RF), Gradient Boosting Decision Trees (GBDT), and Extreme Gradient Boosting (XGBoost), were utilized to predict yield strength (YS) and ultimate tensile strength (UTS) using the aforementioned microstructural features. …”
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Spatial-temporal heterogeneity of vegetation reduces concentration of atmospheric pollution particles in the East China Metropolitan Area by Tong Liu, Jiaqi Yao, Yongqiang Cao, Tianling Qin, Qingyang Wu, Fan Mo, Haoran Zhai, Haiying Gong, Zihua Liu

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Estimation of Reference Crop Evapotranspiration in the Yellow River Basin Based on Machine Learning and Its Regional and Drought Adaptability Analysis by Jun Zhao, Huayu Zhong, Congfeng Wang

“…The study constructed four machine learning models—random forest (RF), a Support Vector Machine (SVM), Gradient Boosting (GB), and Ridge Regression (Ridge)—using the meteorological variables required by the Priestley–Taylor (PT) and Hargreaves (HG) equations as inputs. …”
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A New Grazing–Vegetation Tradeoff and Coordination Indicator: The Grazing Intensity and Vegetation Cover Harmonization Index (GVCI) by Qinyi Huang, Jianjun Chen, Xinhong Li, Hucheng Li, Zizhen Chen, Yanping Lan, Ming Ling, Haotian You, Xiaowen Han

“…In addition, the Random Forest (RF) model was used to simulate the GVCI development trend of various PLCs from 2015 to 2040. …”
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Inferring Parameters in a Complex Land Surface Model by Combining Data Assimilation and Machine Learning by L. T. Keetz, K. Aalstad, R. A. Fisher, C. Poppe Terán, B. Naz, N. Pirk, Y. A. Yilmaz, O. Skarpaas

“…Finally, this emulator replaces CLM‐FATES simulations in an adaptive Markov Chain Monte Carlo approach enabling computationally feasible posterior sampling with enhanced uncertainty quantification (“sample”). …”
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Local Emissions Drive Summer PM_2.5 Pollution Under Adverse Meteorological Conditions: A Quantitative Case Study in Suzhou, Yangtze River Delta by Minyan Wu, Ningning Cai, Jiong Fang, Ling Huang, Xurong Shi, Yezheng Wu, Li Li, Hongbing Qin

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Rooting depth projections of global plant functional types and driving factors analysis based on a hybrid modeling framework by Qinggong Han, Bo Liu, Yunuo Liu, Jielin Zhang, Yongxia Ding, Shouzhang Peng

“…In this study, we developed a hybrid model that combines random forest algorithm with ecosystem data simulated by LPJ-GUESS model and environmental variables to predict global rooting depth, based on 1,184 integrated rooting depth observations. …”
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Multiple-model based prediction of weekly discharge of the Brahmaputra-Jamuna by assimilating antecedent hydrological regime by Md. Abdur Rahim, Shuang Liu, Kaiheng Hu, Hao Li, Md. Anwarul Abedin, Fatima Akter

“…For peak discharge simulation, the ensemble model shows the best performance (R2 = 0.94, NSE = 0.94, RMSE = 4013.11 m3/s and MAE = 2843.60 m3/s). …”
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Deep Residual Transfer Ensemble Model for mRNA Gene-Expression-Based Breast Cancer by Job Prasanth Kumar Chinta Kunta, Vijayalakshmi A. Lepakshi

“…The E2E ensemble learning method used bagging, AdaBoost, Random Forest, Extra Tree Classifier and XGBoost algorithms as base classifier to perform maximum voting-based prediction. …”
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A machine-learning-based approach for active monitoring of blade pitch misalignment in wind turbines by S. Milani, J. Leoni, S. Cacciola, A. Croce, M. Tanelli

“…</p> <p>To tackle this challenge, this paper introduces a novel machine-learning-based approach that relies on the combination of random forest classifier instances and linear regression for automatic pitch misalignment detection and localization. …”
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Impact of short-term soil disturbance on cadmium remobilization and associated risk in vulnerable regions by Zhong Zhuang, Hao Qi, Siyu Huang, Qiqi Wang, Yanan Wan, Huafen Li

“…Monte Carlo simulation, integrating Michaelis-Menten reaction kinetics model, further accessed the potential risk of Cd remobilization. …”
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