Future permafrost degradation under climate change in a headwater catchment of central Siberia: quantitative assessment with a mechanistic modelling approach by T. Xavier, L. Orgogozo, A. S. Prokushkin, E. Alonso-González, S. Gascoin, O. S. Pokrovsky, O. S. Pokrovsky

“…To this end, numerical simulation can be used to explore the response of soil thermal and hydrological regimes to changes in climatic conditions. …”
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Artificial intelligence aided microwave coagulation therapy: Analysis of heat transfer to tumor tissue via hybrid modeling by Zheng Yang, KeWei Dai, Wujun Zhang, Rui Zhou, QingBin Wu, Liang Liu, HuaiRong Qu

“…The simulations were carried out for cancer treatment via hyperthermia utilizing antenna for electromagnetic heating. …”
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Multitemporal Sentinel and GEDI data integration for overstory and understory fuel type classification by Pegah Mohammadpour, Domingos Xavier Viegas, Alcides Pereira, Emilio Chuvieco

“…Cropland, urban, non-fuel, and various forest classes, particularly evergreen needle-leaved forests, demonstrated outstanding performance, achieving F1 scores ranging from 83% to 100%. …”
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Gradient diffusion entropy corrected ALNS optimization for vegetation topology interaction networks by Shengwei Wang, Hongquan Chen, Yulin Guo, Wenjing Su, Yurong Xu, Shuohao Cui, Zhiqiang Zhou

“…All types of ecological nodes showed an increasing trend. the number of ecological nodes in cropland, grassland and forest in 2020 was 27, 8 and 19 respectively. The interaction structure is dominated by competition for cropland and forest in the middle and lower reaches. …”
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Land Surface Modeling in the Himalayas: On the Importance of Evaporative Fluxes for the Water Balance of a High‐Elevation Catchment by Pascal Buri, Simone Fatichi, Thomas E. Shaw, Evan S. Miles, Michael J. McCarthy, Catriona L. Fyffe, Stefan Fugger, Shaoting Ren, Marin Kneib, Achille Jouberton, Jakob Steiner, Koji Fujita, Francesca Pellicciotti

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Ghost hunting in the nonlinear dynamic machine. by Jonathan E Butner, Ascher K Munion, Brian R W Baucom, Alexander Wong

“…Applying dynamical systems theory to the machine learning solution further provides a pathway to interpret the results. Using random forest models as an illustrative example, these models were able to recover the temporal dynamics of time series data simulated using a modified Cusp Catastrophe Monte Carlo. …”
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Deployment and Operation of Battery Swapping Stations for Electric Two-Wheelers Based on Machine Learning by Yu Feng, Xiaochun Lu

“…Then, on a 3000 m grid scale, a prediction model of BSS quantity with random forest, support vector regression, and gradient-boosting decision tree algorithm was built. …”
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Mathematical and computational modeling for organic and insect frass fertilizer production: A systematic review. by Malontema Katchali, Edward Richard, Henri E Z Tonnang, Chrysantus M Tanga, Dennis Beesigamukama, Kennedy Senagi

“…Mathematical models such as simulation, regression, dynamics, and kinetics have been applied while computational data driven machine learning models such as random forest, support vector machines, gradient boosting, and artificial neural networks have also been applied as well. …”
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Numerical evaluation of design factors for effective debris flow mitigation of closed-type check dams by Seungjun Lee, Hyunuk An, Minseok Kim, Jinho Lee

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A Copula-Driven CNN-LSTM Framework for Estimating Heterogeneous Treatment Effects in Multivariate Outcomes by Jong-Min Kim

“…We compare this method with a baseline CNN-LSTM model lacking copula preprocessing and a nonparametric tree-based approach, the Causal Forest, grounded in generalized random forests for HTE estimation. …”
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Parametric optimization of the slot waveguide characteristics using a machine-learning approach by Yadvendra Singh, Suraj Jena, Harish Subbaraman

“…Three different ML techniques, such as artificial neural network (ANN), support vector regression (SVR), and random forest (RF), were tested to compute performance parameters for the slot waveguide. …”
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Spot the bot: the inverse problems of NLP by Vasilii A. Gromov, Quynh Nhu Dang, Alexandra S. Kogan, Assel Yerbolova

“…We then deliberately use the simplest of classifiers (support vector machine, decision tree, random forest) and the derived characteristics to identify whether the text is human-written or not. …”
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A Novel Fault Diagnosis of Induction Motor by Using Various Soft Computation Techniques: BESO-RDFA by Kapu V. Sri Ram Prasad, K. Dhananjay Rao, Guruvulu Naidu Ponnada, Umit Cali, Taha Selim Ustun

“…The established hybrid forecast scheme signifies the combined execution of Bald-Eagle- Search-Optimization (BESO) and Random-Decision-Forest-Algorithm (RDFA), called as BESO-RDFA prediction scheme. …”
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A Method for the 3D Reconstruction of Landscape Trees in the Leafless Stage by Jiaqi Li, Qingqing Huang, Xin Wang, Benye Xi, Jie Duan, Hang Yin, Lingya Li

“…Three-dimensional models of trees can help simulate forest resource management, field surveys, and urban landscape design. …”
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Response of streamflow components and evapotranspiration to changes in tree species composition in a subboreal permafrost watershed in the Greater Khingan Mountains of Northeastern... by Peng Hu, Zhipeng Xu, Xiuling Man, Liangliang Duan, Tijiu Cai

“…Changes in watershed water resources are often linked to land use changes, but the influence of forest structure, especially the composition of tree species, plays a crucial role in hydrological processes. …”
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LightGBM-Based Human Action Recognition Using Sensors by Yinuo Liu, Ziwei Chen

“…Compared with classical machine learning algorithms such as random forest (version 1.5.2) and XGBoost (version 2.1.3), the LightGBM algorithm shows improved performance in terms of the accuracy rate, which reaches 94.98%. …”
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The Influence of mangrove arrangement on wave transmission using smoothed particle hydrodynamics by Trimulyono Andi, Ardi Setiawan Dikky, Samuel, Arswendo Adietya Berlian, Budi Santosa Ari Wibawa, Tuswan

“…The wave transmission value produced by the mangrove forest is relatively small. Additionally, the wave energy result decreased significantly after passing through the mangroves.…”
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Prediction of the packaging chemical migration into food and water by cutting-edge machine learning techniques by Behzad Vaferi, Mohsen Dehbashi, Reza Yousefzadeh, Ali Hosin Alibak

“…This research uses five renowned AI-based techniques (namely, long short-term memory, gradient boosting regressor, multi-layer perceptron, Random Forest, and convolutional neural networks) to anticipate chemical migration from packaging materials to the food/water structure, considering variables such as temperature, chemical characteristics, and packaging/food types. …”
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Experimental-Numerical Study of Indexation of Scenic Road Vertical Alignment in China by Ronghua Wang, Xingliang Liu, Zhe Yuan

“…MLS and LSL values in scenic roads are obtained based on this model through numerical simulation, considering typical EFV, maximum velocity loss (MVL), and ideal velocity loss (IVL). …”
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Graph-based two-level indicator system construction method for smart city information security risk assessment by Li Yang, Kai Zou, Yuxuan Zou

“…In this study, we proposed a graph-based two-level indicator system construction method. First, a random forest was used to extract the indicators' dependency graph from missing data. …”
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