LSTM+MA: A Time-Series Model for Predicting Pavement IRI by Tianjie Zhang, Alex Smith, Huachun Zhai, Yang Lu

“…The performance of the LSTM+MA is compared with other state-of-the-art models, including logistic regressor (LR), support vector regressor (SVR), random forest (RF), K-nearest-neighbor regressor (KNR), fully connected neural network (FNN), XGBoost (XGB), recurrent neural network (RNN) and LSTM. …”
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Discrete Train Speed Profile Optimization for Urban Rail Transit: A Data-Driven Model and Integrated Algorithms Based on Machine Learning by Kang Huang, Jianjun Wu, Xin Yang, Ziyou Gao, Feng Liu, Yuting Zhu

“…Then, two typical machine learning algorithms, random forest regression (RFR) algorithm and support vector machine regression (SVR) algorithm, are used to identify the importance degree of velocity in the different positions of profile and calculate the traction energy consumption. …”
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Analysis of Qualitative Indicators of Norway Spruce and Silver Fir Seed Stands in the Federation of Bosnia and Herzegovina by Dalibor Ballian, MIrzeta Memišević Hodžić

“…The research aims to determine the quantity and quality of Norway spruce and silver fir seed stands in the Federation of Bosnia and Herzegovina and to recommend measures for their improvement to produce the highest quality reproduction material and achieve genetic gain in newly planted forests. For this research, four measured and 19 observed phenotypic traits were analyzed in 11 Norway spruce and 14 silver fir seed stands. …”
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Landscape diversity promotes landscape functioning in North America by Sarah Mayor, Florian Altermatt, Thomas W. Crowther, Iris Hordijk, Simon Landauer, Jacqueline Oehri, Merin Reji Chacko, Michael E. Schaepman, Bernhard Schmid, Pascal A. Niklaus

“…Abstract Biodiversity–ecosystem functioning experiments have established generally positive species richness-productivity relationships in plots of single ecosystem types, typically grassland or forest. However, it remains unclear whether these findings apply in real-world landscapes that resemble a heterogeneous mosaic of different ecosystem and plant types that interact through biotic and abiotic processes. …”
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Evolution and Attribution Analysis of Habitat Quality in China’s First Batch of National Parks by Pengyue Dai, Yanfang Wang, Jinhong Ye, Jing Chen, Runze Li, Xiping Cheng

“…This study highlights the ecological benefits of forestland restoration and the risks posed by the conversion of forest to cultivated or construction land, providing valuable insights for optimizing conservation strategies in China’s national parks.…”
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New records of the golden jackal (Canis aureus L.) in the upper Soča valley, Slovenia by Miha Mihelič, Miha Krofel

“…We also reviewed the database of the reported jackal depredations on livestock at the Bovec Forest Service. In total, we conducted 31 interviews and gathered information on 100 records of jackal presence from 2009 to 2012. …”
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Molecular Signatures of Humic Acids from Different Sources as Revealed by Ultrahigh Resolution Mass Spectrometry by Shuai Qin, Chengbin Xu, Yingzi Xu, Yingchen Bai, Fei Guo

“…Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) has performed molecular-level analysis of two standard HAs from the Suwannee River (SRHA) and leonardite (LEHA) and HA from Jiufeng forest in Beijing (JFHA), which is impossible for other conventional instruments. …”
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Groundwater and soil moisture data collected in the coastal critical zone of the Delmarva Peninsula, United StatesCUAHSI Hydroshare by Dannielle Pratt, Rachel W. McQuiggan, Eva Snell Bacmeister, Amanda Sprague-Getsy, Holly A. Michael

“…Data presented in this paper were collected from three agricultural and three forested sites along the Delmarva Peninsula, United States between March 2021 and July 2024. …”
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JAPANESE IRIS (IRIS ENSATA THUNB.) IN SOUTHERN WEST SIBERIA by Z. V. Dolganova

“…Japanese varieties were successfully introduced under forest-steppe conditions of the Altai Territory at the Lisavenko RI of Horticulture for Siberia (RIHS), and then breeding was conducted with the participation of retro varieties developed in 1980–1982. …”
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Modeling of winter wheat yield prediction based on solar-induced chlorophyll fluorescence by machine learning methods by Minxue Zheng, Han Hu, Yue Niu, Qiu Shen, Feng Jia, Xiaolei Geng

“…However, in the Extreme Gradient Boosting (XGB) model, SIF’s predictive performance was better than that of the combination of SIF and NIRv, indicating that combining SIF and NIRv could not completely enhance SIF’s predictive performance. (b) Random Forest (RF) and XGB models were significantly better than the other models in yield prediction; specifically, the RF model had high stability. …”
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Analysis of Apple Flavours: The Use of Volatile Organic Compounds to Address Cultivar Differences and the Correlation between Consumer Appreciation and Aroma Profiling by Gareth Roberts, Natasha D. Spadafora

“…Permutational multivariate analysis of variance (PerMANOVA) and canonical analysis of principal coordinates (CAP) done on the whole volatile organic compound profiles show separation of the four cultivars. The use of random forest classification enabled the determination of a minimum set of fifteen potential VOC markers that successfully differentiate the cultivars under study. …”
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Design of Strengthening in Agile Governance: Efforts to Develop the Tourism Sector Sustainably in Dayun Sub-District, Siak Regency by Asrida Wan, Rafi Muhammad, Pane Rico Purnawandi, Wazni Wazni, Yusri Ali, Wicaksono Baskoro, Rahmat Al Fauzi

“…Based on the analysis results, this study concludes that agile governance is a crucial component that must be applied to sustainably develop the tourism sector in Dayun District, Siak Regency. Handling forest and land fire disasters can be combined with ecotourism activities in tourist village areas using the principles of agile governance, which include the development of regional information systems, the development of business-oriented community facilities, increasing community participation in tourism governance, and emphasizing the rapid resolution of problems that arise.…”
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Artificial intelligence for the prediction of the physical and mechanical properties of a compressed earth reinforced by fibers by Bentegri Houcine, Rabehi Mohamed, Kherfane Samir, Boukansous Sarra

“…Using various validation criteria such as coefficient of determination (R), root mean squared error (RMSE) and mean absolute error (MAE), the ANN model was validated and compared to two machine learning (ML) Random Forest (RF) techniques and Multilayer Perceptron (MLP). …”
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Unmanned Aerial Vehicles for Real-Time Vegetation Monitoring in Antarctica: A Review by Kaelan Lockhart, Juan Sandino, Narmilan Amarasingam, Richard Hann, Barbara Bollard, Felipe Gonzalez

“…Despite the potential of established Machine-Learning (ML) classifiers such as Random Forest, K Nearest Neighbour, and Support Vector Machine, and gradient boosting in the semantic segmentation of UAV-captured images, there is a notable scarcity of research employing Deep Learning (DL) models in these extreme environments. …”
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Effect of climate on thermal response in cows of different racial groups in lower tropic by Raúl Andrés Molina-Benavides, Sandra Milena Perilla-Duque, Rómulo Campos-Gaona, Hugo Sánchez-Guerrero, Juan Camilo Rivera-Palacio, Luis Armando Muñoz-Borja, Daniel Ricardo Jiménez-Rodas

“…The information was analyzed using descriptive statistics, correlation matrices and Random Forest models, through the R software. Results. From the physiological data from automatic collection systems, the response variables that would allow the evaluation of thermoregulation processes were analyzed using big data. …”
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Experimental Study of Site-Specific Soil Water Content and Rainfall Inducing Shallow Landslides: Case of Gakenke District, Rwanda by Martin Kuradusenge, Santhi Kumaran, Marco Zennaro, Albert Niyonzima

“…Among the sampled sites, the sites covered by forest indicated no sign of slope failure, whereas sites with crops could slip. …”
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Brief communication: Monitoring snow depth using small, cheap, and easy-to-deploy snow–ground interface temperature sensors by C. L. Bachand, C. L. Bachand, C. Wang, B. Dafflon, L. N. Thomas, L. N. Thomas, I. Shirley, S. Maebius, S. Maebius, C. M. Iversen, K. E. Bennett

“…<p>Temporally continuous snow depth estimates are vital for understanding changing snow patterns and impacts on permafrost in the Arctic. We trained a random forest machine learning model to predict snow depth from variability in snow–ground interface temperature. …”
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Innovation and Collaboration Model Based on Ecological Fiscal Transfer Approach: Case Study on Green Siak Regency Policy Development by Wicaksono Baskoro, Marta Auradian, Febrina Rury, Rafi Muhammad, Gusmansyah Gusmansyah, Hergianasari Putri

“…Substantively, the ecological fiscal transfer approach is an innovative and crucial policy instrument that needs to be adopted to maintain the sustainability of forest areas in Siak Regency while continuing to encourage increased stakeholder collaboration through fiscal transfers and intergovernmental revenue sharing schemes based on ecological indicators as awards given based on the quality-of-life index in sustainable conservation efforts in Siak Regency.…”
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Presenting a model for the diagnosis of heart failure using cumulative and deep learning algorithms: a case study of tehran heart center by Amir Hossein Hariri, Esmaeil Bagheri, Sayyed Mohammad Reza Davoodi

“…In the classification phase, basic techniques were used, including a decision tree, a neural network, and different cumulative techniques such as gradient boosting, random forest, and the novel deep learning method. A comparison revealed that deep learning with the accuracy of 95.33%, disease class accuracy of 95.77%, and health class accuracy of 94.74% could enhance the presentation and results of the neural network. …”
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Flood hazard simulation and mapping using digital elevation models with different resolutions by G.R. Puno, R.C.C. Puno, I.V. Maghuyop

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