Plasmodium vivax malaria endemicity in Indonesia in 2010. by Iqbal R F Elyazar, Peter W Gething, Anand P Patil, Hanifah Rogayah, Elvieda Sariwati, Niken W Palupi, Siti N Tarmizi, Rita Kusriastuti, J Kevin Baird, Simon I Hay

“…Detailed understanding of the contemporary spatial distribution of this parasite is needed to combat it. …”
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Balancing accuracy versus precision: Enhancing the usability of sub-seasonal forecasts by Etienne Dunn-Sigouin, Erik W. Kolstad, C. Ole Wulff, Douglas J. Parker, Richard J. Keane

“…Forecasts are essential for climate adaptation and preparedness, such as in early warning systems and impact models. A key limitation to their practical use is often their coarse spatial grid spacing. …”
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A Patch-Wise Mechanism for Enhancing Sparse Radar Echo Extrapolation in Precipitation Nowcasting by Yueting Wang, Hou Jiang, Tang Liu, Ling Yao, Chenghu Zhou

“…Spatial visualizations of radar echoes reveal PW's superior in predicting localized and intense precipitation. …”
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Application of a hybrid algorithm of LSTM and Transformer based on random search optimization for improving rainfall-runoff simulation by Wenzhong Li, Chengshuai Liu, Caihong Hu, Chaojie Niu, Runxi Li, Ming Li, Yingying Xu, Lu Tian

“…Data-driven models offer novel solutions to these challenges, though they are hindered by difficulties in hyperparameter selection and a decline in prediction stability as the lead time extends. …”
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Automated detection of sea cucumbers in turbid subtidal marine habitats: An explainable approach by Cheryl Chu, Yi-Fei Gu, Adrian Wong, Bayden D. Russell

“…Our study includes Eigen-CAM, a novel visualization tool that enhances model interpretability and transparency during model prediction. …”
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Efficient and generalizable nested Fourier-DeepONet for three-dimensional geological carbon sequestration by Jonathan E. Lee, Min Zhu, Ziqiao Xi, Kun Wang, Yanhua O. Yuan, Lu Lu

“…However, these simulations are often computationally expensive due to highly coupled physics and large spatial-temporal simulation domains. Surrogate modelling with data-driven machine learning has become a promising alternative to accelerate physics-based simulations. …”
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Trend analysis of the coupling effect between new urbanization and resources-environment in the Guangdong-Hong Kong-Macao Greater Bay area by Hongyi Dou, Guoqin Zhang

“…This study constructs an urbanization-resource-environment system dynamics (SD) model that highlights the new urbanization characteristics to simulate and predict the development trends from 1990 to 2035 at urban cluster and city scales. …”
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Enhancing seabed sediment classification with multibeam echo-sounding and self-training: a case study from the East Sea of South Korea by Changhoon Lee, Sujung Park, Daeung Yoon, Bo-Yeon Yi, Moonsoo Lim

“…To mitigate sample scarcity and class imbalance, a semi-supervised self-training loop iteratively added high-confidence pseudo-labels to the training set.ResultsField validation in the East Sea (Republic of Korea) showed that the Extreme Gradient Boosting model achieved the highest accuracy. Overall prediction accuracy increased from 60.81 % with the baseline workflow to 72.73 % after applying data interpolation, enhanced feature extraction, and self-training.DiscussionThe proposed combination of U-Net interpolation, multi-scale texture features, and semi-supervised learning significantly improves sediment classification where MBES data are incomplete and sediment samples are sparse. …”
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Mapping indicator species of segetal flora for result-based payments in arable land using UAV imagery and deep learning by Caterina Barrasso, Robert Krüger, Anette Eltner, Anna F. Cord

“…Additionally, we investigated the potential of spatial co-occurrence and canopy height heterogeneity to predict the presence of species difficult to detect by UAVs. …”
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Simulation and Spatio-Temporal Analysis of Soil Erosion in the Source Region of the Yellow River Using Machine Learning Method by Jinxi Su, Rong Tang, Huilong Lin

“…Given these challenges, the objectives of this study were to develop a suitable assessment and prediction model for soil erosion tailored to the SRYR’s needs. …”
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Microscopic pore combination type identification of tight sandstone reservoir based on improved swin transformer architecture by Zhenyu Pang, Zhicong Chen, Sijie Lu, Zhenbo Cai, Yuqing Lu, Mengting Peng

“…Experimental results demonstrate that SwinLSC achieves a prediction accuracy of 93.3 %, significantly outperforming the comparative models. …”
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Evaluating Radio Occultation (RO) Constellation Designs Using Observing System Simulation Experiments (OSSEs) for Ionospheric Specification by Nicholas Dietrich, Tomoko Matsuo, Chi‐Yen Lin, Brandon diLorenzo, Charles Chien‐Hung Lin, Tzu‐Wei Fang

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Geomorphological and Geological Characteristics Slope Unit: Advancing Township-Scale Landslide Susceptibility Assessment Strategies by Gang Chen, Taorui Zeng, Dongsheng Liu, Hao Chen, Linfeng Wang, Liping Wang, Kaiqiang Zhang, Thomas Glade

“…A landslide susceptibility index system is developed using multi-source data, with susceptibility prediction conducted via the XGBoost model optimized by Bayesian methods. …”
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Surveying Nearshore Bathymetry Using Multispectral and Hyperspectral Satellite Imagery and Machine Learning by David Hartmann, Mathieu Gravey, Timothy David Price, Wiebe Nijland, Steven Michael de Jong

“…Here, the nearshore bathymetry of southwest Puerto Rico is estimated with multispectral Sentinel-2 and hyperspectral PRISMA imagery using conventional spectral band ratio models and more advanced XGBoost models and convolutional neural networks. …”
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Rain-Induced Shallow Landslide Susceptibility Under Multiple Scenarios Based on Effective Antecedent Precipitation by Chuanmei Cheng, Ying Li, Dong Zhu, Yu Liu, Yongqiu Wu, Degen Lin, Hao Guo

“…Therefore, it is essential to incorporate antecedent effective precipitation as a factor in landslide prediction models that allow for the creation of more comprehensive landslide susceptibility maps. …”
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Potential of solar-induced chlorophyll fluorescence for monitoring long-term dynamics of soil salinity in Central Asia the Xinjiang Region China by Kuangda Cui, Kuangda Cui, Jianli Ding, Jianli Ding, Jinjie Wang, Jinjie Wang, Jiao Tan, Jiao Tan, Lijing Han, Jiangtao Li, Jiangtao Li

“…Model performance, seasonal sensitivity, and spatial variation were analyzed across Central Asian countries and Xinjiang.ResultsSIF effectively detected salinization dynamics, with highest sensitivity in Kazakhstan and Xinjiang. …”
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Urban-rural inequality in soil heavy metal health risks: Insights from Baoding, China by Lingzhi Luo, Liang Wang, You Li, Hongying Cao, Yanling Guo, Xiaoyong Liao

“…We used random forest models to predict high-resolution soil metal concentration maps, Positive Matrix Factorization for source apportionment, and spatial exposure models to estimate human health risks under multiple exposure pathways. …”
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Space-Based Mapping of Pre- and Post-Hurricane Mangrove Canopy Heights Using Machine Learning with Multi-Sensor Observations by Boya Zhang, Daniel Gann, Shimon Wdowinski, Chaohao Lin, Erin Hestir, Lukas Lamb-Wotton, Khandker S. Ishtiaq, Kaleb Smith, Yuepeng Li

“…We evaluated (1) spatial transfer learning to predict regional CH for both time periods and (2) temporal transfer learning coupled with species-specific error correction models to predict post-Irma CH using models trained by pre-Irma data. …”
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Trends of Desertification Change and Its Driving Factors in Aksu Region by Zheng Jiaxiang, Sun Guili, Su Xiangling, Ran Yajun, Zheng Xu

“…The mode and intensity of action showed enhancement and non-linear enhancement, respectively. ③ The prediction results from the CA-Markov model showed that if the driving factors did not change, the degree of desertification in the Aksu region would continue to reverse during 2019—2024, and the overall change for the area would be from extremely severe desertification to severe desertification. …”
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Present-day changes of mountain glaciers on the southern slope of the Dzhungarian Alatau range by A. L. Kokarev, I. N. Shesterova

“…Glacierization of southern slope of the Dzhungarian (Zhetysu) Alatau range was estimated by means of data obtained by operational satellite Landsat 7 surveys on August 19 and September 2011 (sensors ETM+) with the use of digital relief models (ASTER GDEM). Analysis of these materials by means of computer programs ENVI, ERDAS Imagine, MapInfo, and ArcGIS made it possible to obtain a spatial information of glacier systems of the territory under investigation and to define morphological characteristics of glaciers, present-day moraine complexes, glacier lakes as well as other elements of glacial monitoring at the survey moments. …”
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