@article{lin_genzer_li_qiao_dickey_tang_2018, title={Sonication-enabled rapid production of stable liquid metal nanoparticles grafted with poly(1-octadecene-alt-maleic anhydride) in aqueous solutions}, volume={10}, ISSN={["2040-3372"]}, url={https://doi.org/10.1039/C8NR05600E}, DOI={10.1039/c8nr05600e}, abstractNote={We demonstrate sonication-enabled production of liquid metal nanoparticles grafted with poly(1-octadecene-alt-maleic anhydride) in water that remain stable in biological buffers.}, number={42}, journal={NANOSCALE}, publisher={Royal Society of Chemistry (RSC)}, author={Lin, Yiliang and Genzer, Jan and Li, Weihua and Qiao, Ruirui and Dickey, Michael D. and Tang, Shi-Yang}, year={2018}, month={Nov}, pages={19871–19878} }
 @article{li_sankarasubramanian_ranjithan_sinha_2015, title={Role of multimodel combination and data assimilation in improving streamflow prediction over multiple time scales}, volume={30}, ISSN={1436-3240 1436-3259}, url={http://dx.doi.org/10.1007/s00477-015-1158-6}, DOI={10.1007/s00477-015-1158-6}, number={8}, journal={Stochastic Environmental Research and Risk Assessment}, publisher={Springer Science and Business Media LLC}, author={Li, Weihua and Sankarasubramanian, A. and Ranjithan, R. S. and Sinha, Tushar}, year={2015}, month={Sep}, pages={2255–2269} }
 @article{li_sankarasubramanian_ranjithan_brill_2014, title={Improved regional water management utilizing climate forecasts: An interbasin transfer model with a risk management framework}, volume={50}, ISSN={0043-1397}, url={http://dx.doi.org/10.1002/2013WR015248}, DOI={10.1002/2013wr015248}, abstractNote={Abstract}, number={8}, journal={Water Resources Research}, publisher={American Geophysical Union (AGU)}, author={Li, Weihua and Sankarasubramanian, A. and Ranjithan, R. S. and Brill, E. D.}, year={2014}, month={Aug}, pages={6810–6827} }
 @article{li_sankarasubramanian_2012, title={Reducing hydrologic model uncertainty in monthly streamflow predictions using multimodel combination}, volume={48}, ISSN={["1944-7973"]}, DOI={10.1029/2011wr011380}, abstractNote={Model errors are inevitable in any prediction exercise. One approach that is currently gaining attention in reducing model errors is by combining multiple models to develop improved predictions. The rationale behind this approach primarily lies on the premise that optimal weights could be derived for each model so that the developed multimodel predictions will result in improved predictions. A new dynamic approach (MM‐1) to combine multiple hydrological models by evaluating their performance/skill contingent on the predictor state is proposed. We combine two hydrological models, “abcd” model and variable infiltration capacity (VIC) model, to develop multimodel streamflow predictions. To quantify precisely under what conditions the multimodel combination results in improved predictions, we compare multimodel scheme MM‐1 with optimal model combination scheme (MM‐O) by employing them in predicting the streamflow generated from a known hydrologic model (abcd model orVICmodel) with heteroscedastic error variance as well as from a hydrologic model that exhibits different structure than that of the candidate models (i.e., “abcd” model or VIC model). Results from the study show that streamflow estimated from single models performed better than multimodels under almost no measurement error. However, under increased measurement errors and model structural misspecification, both multimodel schemes (MM‐1 and MM‐O) consistently performed better than the single model prediction. Overall, MM‐1 performs better than MM‐O in predicting the monthly flow values as well as in predicting extreme monthly flows. Comparison of the weights obtained from each candidate model reveals that as measurement errors increase, MM‐1 assigns weights equally for all the models, whereas MM‐O assigns higher weights for always the best‐performing candidate model under the calibration period. Applying the multimodel algorithms for predicting streamflows over four different sites revealed that MM‐1 performs better than all single models and optimal model combination scheme, MM‐O, in predicting the monthly flows as well as the flows during wetter months.}, journal={WATER RESOURCES RESEARCH}, author={Li, Weihua and Sankarasubramanian, A.}, year={2012}, month={Dec} }