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1
PhyloProfile: dynamic visualization and exploration of multi-layered phylogenetic profiles
PhyloProfile: dynamic visualization and exploration of multi-layered phylogenetic profiles
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PhyloProfile: dynamic visualization and exploration of multi-layered phylogenetic profiles

Bioinformatics, 2018-09, Vol.34 (17), p.3041-3043 [Peer Reviewed Journal]

The Author(s) 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com 2018 ;ISSN: 1367-4803 ;EISSN: 1460-2059 ;EISSN: 1367-4811 ;DOI: 10.1093/bioinformatics/bty225 ;PMID: 29659708

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2
Conserved Gene Microsynteny Unveils Functional Interaction Between Protein Disulfide Isomerase and Rho Guanine-Dissociation Inhibitor Families
Conserved Gene Microsynteny Unveils Functional Interaction Between Protein Disulfide Isomerase and Rho Guanine-Dissociation Inhibitor Families
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Conserved Gene Microsynteny Unveils Functional Interaction Between Protein Disulfide Isomerase and Rho Guanine-Dissociation Inhibitor Families

Scientific reports, 2017-12, Vol.7 (1), p.17262-18, Article 17262 [Peer Reviewed Journal]

2017. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s) 2017 ;ISSN: 2045-2322 ;EISSN: 2045-2322 ;DOI: 10.1038/s41598-017-16947-5 ;PMID: 29222525

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3
Quantification of predictive uncertainty with a metamodel: toward more efficient hydrologic simulations
Quantification of predictive uncertainty with a metamodel: toward more efficient hydrologic simulations
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Quantification of predictive uncertainty with a metamodel: toward more efficient hydrologic simulations

Stochastic environmental research and risk assessment, 2019-07, Vol.33 (7), p.1453-1476 [Peer Reviewed Journal]

Springer-Verlag GmbH Germany, part of Springer Nature 2019 ;Stochastic Environmental Research and Risk Assessment is a copyright of Springer, (2019). All Rights Reserved. ;ISSN: 1436-3240 ;EISSN: 1436-3259 ;DOI: 10.1007/s00477-019-01703-0

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4
Closing in on Hydrologic Predictive Accuracy: Combining the Strengths of High‐Fidelity and Physics‐Agnostic Models
Closing in on Hydrologic Predictive Accuracy: Combining the Strengths of High‐Fidelity and Physics‐Agnostic Models
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Closing in on Hydrologic Predictive Accuracy: Combining the Strengths of High‐Fidelity and Physics‐Agnostic Models

Geophysical research letters, 2023-09, Vol.50 (17), p.n/a [Peer Reviewed Journal]

2023. The Authors. ;2023. This article is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 0094-8276 ;EISSN: 1944-8007 ;DOI: 10.1029/2023GL104464

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5
A Novel Modeling Framework for Computationally Efficient and Accurate Real‐Time Ensemble Flood Forecasting With Uncertainty Quantification
A Novel Modeling Framework for Computationally Efficient and Accurate Real‐Time Ensemble Flood Forecasting With Uncertainty Quantification
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A Novel Modeling Framework for Computationally Efficient and Accurate Real‐Time Ensemble Flood Forecasting With Uncertainty Quantification

Water resources research, 2020-03, Vol.56 (3), p.n/a [Peer Reviewed Journal]

2020. American Geophysical Union. All Rights Reserved. ;ISSN: 0043-1397 ;EISSN: 1944-7973 ;DOI: 10.1029/2019WR025727

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6
Toward an Efficient Uncertainty Quantification of Streamflow Predictions Using Sparse Polynomial Chaos Expansion
Toward an Efficient Uncertainty Quantification of Streamflow Predictions Using Sparse Polynomial Chaos Expansion
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Toward an Efficient Uncertainty Quantification of Streamflow Predictions Using Sparse Polynomial Chaos Expansion

Water (Basel), 2021-01, Vol.13 (2), p.203 [Peer Reviewed Journal]

COPYRIGHT 2021 MDPI AG ;ISSN: 2073-4441 ;EISSN: 2073-4441 ;DOI: 10.3390/w13020203

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7
Temporal Changes in Water and Sediment Discharges: Impacts of Climate Change and Human Activities in the Red River Basin (1958–2021) with Projections up to 2100
Temporal Changes in Water and Sediment Discharges: Impacts of Climate Change and Human Activities in the Red River Basin (1958–2021) with Projections up to 2100
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Temporal Changes in Water and Sediment Discharges: Impacts of Climate Change and Human Activities in the Red River Basin (1958–2021) with Projections up to 2100

Water (Basel), 2024-04, Vol.16 (8), p.1155 [Peer Reviewed Journal]

2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;EISSN: 2073-4441 ;DOI: 10.3390/w16081155

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8
Improving the Accuracy of Dam Inflow Predictions Using a Long Short-Term Memory Network Coupled with Wavelet Transform and Predictor Selection
Improving the Accuracy of Dam Inflow Predictions Using a Long Short-Term Memory Network Coupled with Wavelet Transform and Predictor Selection
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Improving the Accuracy of Dam Inflow Predictions Using a Long Short-Term Memory Network Coupled with Wavelet Transform and Predictor Selection

Mathematics (Basel), 2021-03, Vol.9 (5), p.551 [Peer Reviewed Journal]

2021. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2227-7390 ;EISSN: 2227-7390 ;DOI: 10.3390/math9050551

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9
Investigating the Future Flood and Drought Shifts in the Transboundary Srepok River Basin Using CMIP6 Projections
Investigating the Future Flood and Drought Shifts in the Transboundary Srepok River Basin Using CMIP6 Projections
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Investigating the Future Flood and Drought Shifts in the Transboundary Srepok River Basin Using CMIP6 Projections

IEEE journal of selected topics in applied earth observations and remote sensing, 2024, Vol.17, p.7516-7529 [Peer Reviewed Journal]

Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024 ;ISSN: 1939-1404 ;EISSN: 2151-1535 ;DOI: 10.1109/JSTARS.2024.3380514 ;CODEN: IJSTHZ

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10
Sequencing the Genomes of the First Terrestrial Fungal Lineages: What Have We Learned?
Sequencing the Genomes of the First Terrestrial Fungal Lineages: What Have We Learned?
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Sequencing the Genomes of the First Terrestrial Fungal Lineages: What Have We Learned?

Microorganisms (Basel), 2023-07, Vol.11 (7), p.1830 [Peer Reviewed Journal]

COPYRIGHT 2023 MDPI AG ;2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2023 by the authors. 2023 ;ISSN: 2076-2607 ;EISSN: 2076-2607 ;DOI: 10.3390/microorganisms11071830 ;PMID: 37513002

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11
A Novel Modeling Framework for Computationally Efficient and Accurate Real-Time Ensemble Flood Forecasting With Uncertainty Quantification
A Novel Modeling Framework for Computationally Efficient and Accurate Real-Time Ensemble Flood Forecasting With Uncertainty Quantification
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A Novel Modeling Framework for Computationally Efficient and Accurate Real-Time Ensemble Flood Forecasting With Uncertainty Quantification

Water resources research, 2020-02, Vol.56 (3) [Peer Reviewed Journal]

ISSN: 0043-1397 ;EISSN: 1944-7973

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12
Breaking Down the Computational Barriers to Real‐Time Urban Flood Forecasting
Breaking Down the Computational Barriers to Real‐Time Urban Flood Forecasting
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Breaking Down the Computational Barriers to Real‐Time Urban Flood Forecasting

Geophysical research letters, 2021-10, Vol.48 (20), p.n/a [Peer Reviewed Journal]

2021. American Geophysical Union. All Rights Reserved. ;ISSN: 0094-8276 ;EISSN: 1944-8007 ;DOI: 10.1029/2021GL093585

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13
Data-Driven Dam Outflow Prediction Using Deep Learning with Simultaneous Selection of Input Predictors and Hyperparameters Using the Bayesian Optimization Algorithm
Data-Driven Dam Outflow Prediction Using Deep Learning with Simultaneous Selection of Input Predictors and Hyperparameters Using the Bayesian Optimization Algorithm
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Data-Driven Dam Outflow Prediction Using Deep Learning with Simultaneous Selection of Input Predictors and Hyperparameters Using the Bayesian Optimization Algorithm

Water resources management, 2024, Vol.38 (2), p.401-421 [Peer Reviewed Journal]

The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. ;ISSN: 0920-4741 ;EISSN: 1573-1650 ;DOI: 10.1007/s11269-023-03677-9

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14
Enhancing wildfire mapping accuracy using mono-temporal Sentinel-2 data: A novel approach through qualitative and quantitative feature selection with explainable AI
Enhancing wildfire mapping accuracy using mono-temporal Sentinel-2 data: A novel approach through qualitative and quantitative feature selection with explainable AI
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Enhancing wildfire mapping accuracy using mono-temporal Sentinel-2 data: A novel approach through qualitative and quantitative feature selection with explainable AI

Ecological informatics, 2024-07, Vol.81, Article 102601 [Peer Reviewed Journal]

2024 The Author(s) ;ISSN: 1574-9541 ;DOI: 10.1016/j.ecoinf.2024.102601

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15
Breaking Down the Computational Barriers to Real-Time Urban Flood Forecasting
Breaking Down the Computational Barriers to Real-Time Urban Flood Forecasting
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Article 		Add to My Research

Breaking Down the Computational Barriers to Real-Time Urban Flood Forecasting

Geophysical research letters, 2021-09, Vol.48 (20) [Peer Reviewed Journal]

ISSN: 0094-8276 ;EISSN: 1944-8007

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16
Sequencing the Genomes of the First Terrestrial Fungal Lineages: What Have We Learned?
Sequencing the Genomes of the First Terrestrial Fungal Lineages: What Have We Learned?
Material Type:
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Sequencing the Genomes of the First Terrestrial Fungal Lineages: What Have We Learned?

Microorganisms, 2023-01, Vol.11 (7)

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