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521
Spatial modeling and ecological suitability of Ebola virus disease in Africa.
Published 2024-01-01Get full text
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522
Using System‐Inspired Metrics to Improve Water Quality Prediction in Stratified Lakes
Published 2024-08-01Get full text
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523
Hybrid CNN-LSTM Model with Custom Activation and Loss Functions for Predicting Fan Actuator States in Smart Greenhouses
Published 2025-04-01“…The hybrid model integrates CNNs for spatial feature extraction and LSTMs for temporal dependency modeling, enhanced by a custom activation function and loss function tailored for the problem’s characteristics. …”
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524
Spatiotemporal Dynamics and Prediction of Habitat Quality Based on Land Use and Cover Change in Jiangsu, China
Published 2024-11-01“…This study utilizes the land use data of Jiangsu Province for the years 2000, 2010, and 2020, applying the FLUS model to investigate the driving force behind land expansion and to simulate a prediction for the land use of 2030. …”
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525
Spatial heterogeneity and spatial bias analyses in hedonic price models: some practical considerations
Published 2015-06-01“…Estimation of a hedonic price function using Malaysian dataset of agricultural land sale values indicates spatial disaggregation and spatial dependence. However, diagnostic tests and actual estimation of spatial models do not always provide unambiguous conclusions while predicted errors do not vary all that much from those generated by simpler models. …”
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526
Assessing the ecological risk and its driving forces on Islands using the Pressure-State-Response model
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527
Statistical Analysis of Atmospheric Delay Gradient and Rainfall Prediction in a Tropical Region
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528
Expanding cryospheric landform inventories – quantitative approaches for underestimated periglacial block- and talus slopes in the Dry Andes of Argentina
Published 2025-05-01“…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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529
Orthogonal intercellular signaling for programmed spatial behavior
Published 2016-01-01“…We used this model to predict optimal expression levels for receiver proteins, to create an effective two‐channel cell communication device. …”
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530
SPATIALLY INFORMED INSIGHTS: MODELING PERCENTAGE POVERTY IN EAST JAVA PROVINCE USING SEM WITH SPATIAL WEIGHT VARIATIONS
Published 2024-05-01“…Diverse weighting schemes are applied based on both distance (1) and contiguity (2). The optimal predictive model utilized is the Spatial Error Model (SEM) incorporating a Distance Band Weighing (DBW) mechanism with a designated maximum distance ( ) of 75000 meters. …”
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531
Enhancing environmental monitoring of harmful algal blooms with ConvLSTM image prediction
Published 2025-01-01“…Using 3D universal kriging, the study interpolates missing HAB concentration values, transforming geospatial point data into spatially continuous grid images that serve as the foundation for predictive modeling. …”
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532
Spatiotemporal evolution and trend prediction of coupled coordination between digital technology and manufacturing green transformation from provinces in China
Published 2025-05-01“…Based on this, this paper adopts the coupling coordination model, kernel density estimation, Dagum Gini coefficient decomposition, and spatial autocorrelation to conduct a spatiotemporal evolution analysis of the coupling coordination degree (the D‐G system) of digital technology and MGT in 30 provinces (municipalities, autonomous regions) of mainland China from 2011 to 2020, and adopting the spatial Markov chain to predict its evolutionary trend. …”
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533
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534
Revisiting the "satisfaction of spatial restraints" approach of MODELLER for protein homology modeling.
Published 2019-12-01“…The most frequently used approach for protein structure prediction is currently homology modeling. The 3D model building phase of this methodology is critical for obtaining an accurate and biologically useful prediction. …”
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535
Flash flood prediction modeling in the hilly regions of Southeastern Bangladesh: A machine learning attempt on present and future climate scenarios
Published 2024-12-01“…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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536
Optimizing ensemble learning for satellite-based multi-hazard monitoring and susceptibility assessment of landslides, land subsidence, floods, and wildfires
Published 2025-08-01“…Past studies have relied mainly on traditional machine learning models, but these models do not perform well for complex spatial patterns. …”
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537
Soil Erosion Prediction Using Morgan-Morgan-Finney Model in a GIS Environment in Northern Ethiopia Catchment
Published 2014-01-01“…The average soil loss estimated by TC using MMF model at catchment level was 26 t ha−1 y−1. In most parts of the catchment (>80%), the model predicted soil loss rates higher than the maximum tolerable rate (18 t ha−1 y−1) estimated for Ethiopia. …”
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538
Estimation and prediction of water conservation in the upper reaches of the Hanjiang River Basin based on InVEST-PLUS model
Published 2024-11-01“…With the gradual prominence of global water shortage and other problems, evaluating and predicting the impact of land use change on regional water conservation function is of great reference significance for carrying out national spatial planning and environmental protection, and realizing land intelligent management. …”
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539
A Novel Short‐Term Prediction Model for Regional Equatorial Plasma Bubble Irregularities in East and Southeast Asia
Published 2025-02-01“…For 60‐min prediction, the STEP model can still achieve reasonable accuracy with an RMSE of 0.110 TECU/min and an R2 of 0.482, showing significant improvement over traditional models. …”
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540
Predicting the needs of people living with a disability using the two-level logit-skewed exponential power model
Published 2024-07-01“…Cartograms were used to determine the spatial distribution for the proportion of doctor’s visit and cost using the predicted values. …”
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