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Effect of aqueous ozone treatment on the reduction of chlorpyrifos and physicochemical and microbial qualities of cucumber (Cucumis sativusL.): Process modeling and optimization.
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- Journal of Food Process Engineering, 2024, v. 47, n. 3, p. 1, doi. 10.1111/jfpe.14572
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Development and Evaluation of Ready to Cook Extruded Food from Mushroom and Fruit Flours.
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- Mysore Journal of Agricultural Sciences, 2024, v. 58, n. 1, p. 358
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- Article
Impact of cross‐sectional orientation in one‐dimensional hydrodynamic modeling on flood inundation mapping.
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- Journal of Flood Risk Management, 2023, v. 16, n. 3, p. 1, doi. 10.1111/jfr3.12893
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- Article
Process optimization for drying of Shrimp (Metapenaeus dobsoni) under hot air‐assisted microwave drying technology using response surface methodology.
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- Journal of Food Process Engineering, 2023, v. 46, n. 7, p. 1, doi. 10.1111/jfpe.14338
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- Article
SUSCEPTIBILITY OF LIFE STAGES OF TRIBOLIUM CASTANEUM (HERBST) TO MICROWAVE RADIATION.
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- Indian Journal of Entomology, 2023, v. 85, n. 1, p. 194, doi. 10.55446/IJE.2022.521
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- Article
Impact of water conservation structures on the agricultural productivity in the context of climate change.
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- Water Resources Management, 2022, v. 36, n. 5, p. 1627, doi. 10.1007/s11269-022-03094-4
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A decision support system for the identification of critical zones in a watershed to implement land management practices.
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- Stochastic Environmental Research & Risk Assessment, 2021, v. 35, n. 8, p. 1649, doi. 10.1007/s00477-021-01983-5
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- Article
A multistate first-order Markov model for modeling time distribution of extreme rainfall events.
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- Stochastic Environmental Research & Risk Assessment, 2021, v. 35, n. 6, p. 1205, doi. 10.1007/s00477-020-01939-1
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- Article
Calibration of distributed hydrological models considering the heterogeneity of the parameters across the basin: a case study of SWAT model.
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- Environmental Earth Sciences, 2021, v. 80, n. 4, p. 1, doi. 10.1007/s12665-021-09434-8
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Implications of uncertainty in inflow forecasting on reservoir operation for irrigation.
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- Paddy & Water Environment, 2021, v. 19, n. 1, p. 99, doi. 10.1007/s10333-020-00822-7
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- Article
Quality evaluation of tender jackfruit using near-infrared reflectance spectroscopy.
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- Journal of Applied Horticulture, 2020, v. 22, n. 3, p. 171, doi. 10.37855/jah.2020.v22i03.31
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Uncertainty of hydrologic simulation, and its impact on the design and the effectiveness of water conservation structures.
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- Stochastic Environmental Research & Risk Assessment, 2020, v. 34, n. 7, p. 973, doi. 10.1007/s00477-020-01814-z
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- Article
Investigating Atrazine Concentrations in the Zwischenscholle Aquifer Using MODFLOW with the HYDRUS-1D Package and MT3DMS.
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- Water (20734441), 2020, v. 12, n. 4, p. 1019, doi. 10.3390/w12041019
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- Article
Soil Temperature Dynamics at Hillslope Scale—Field Observation and Machine Learning-Based Approach.
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- Water (20734441), 2020, v. 12, n. 3, p. 713, doi. 10.3390/w12030713
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Quality evaluation of thermal processed tender jackfruit during storage.
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- Journal of Tropical Agriculture, 2020, v. 58, n. 1, p. 22
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Mild thermal processing of cashew apple juice using ohmic heating.
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- Journal of Tropical Agriculture, 2020, v. 58, n. 1, p. 44
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- Article
Implementation of Solute Transport in the Vadose Zone into the "HYDRUS Package for MODFLOW".
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- Ground Water, 2019, v. 57, n. 3, p. 392, doi. 10.1111/gwat.12815
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- Article
Role of dams on the floods of August 2018 in Periyar River Basin, Kerala.
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- Current Science (00113891), 2019, v. 116, n. 5, p. 780, doi. 10.18520/cs/v116/i5/780-794
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- Article
Enhancement of Model Reliability by Integrating Prediction Interval Optimization into Hydrogeological Modeling.
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- Water Resources Management, 2019, v. 33, n. 1, p. 229, doi. 10.1007/s11269-018-2099-x
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Parameter estimation of SWAT and quantification of consequent confidence bands of model simulations.
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- Environmental Earth Sciences, 2018, v. 77, n. 12, p. 1, doi. 10.1007/s12665-018-7619-8
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- Article
Perennial biomass production from marginal land in the Upper Mississippi River Basin.
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- Land Degradation & Development, 2018, v. 29, n. 6, p. 1748, doi. 10.1002/ldr.2971
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A computationally efficient method for uncertainty analysis of SWAT model simulations.
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- Stochastic Environmental Research & Risk Assessment, 2018, v. 32, n. 6, p. 1479, doi. 10.1007/s00477-018-1538-9
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- Article
Development of fortified banana pseudostem juice powder utilizing spray drying technology.
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- Journal of Tropical Agriculture, 2017, v. 55, n. 2, p. 145
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- Article
Effect of pre-treatments on quality parameters of vacuum fried ripened banana (Nendran) chips.
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- Journal of Tropical Agriculture, 2017, v. 55, n. 2, p. 161
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- Article
Terrestrial Macrofungal Diversity from the Tropical Dry Evergreen Biome of Southern India and Its Potential Role in Aerobiology.
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- PLoS ONE, 2017, v. 12, n. 1, p. 1, doi. 10.1371/journal.pone.0169333
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Indian Summer Monsoon Rainfall: Implications of Contrasting Trends in the Spatial Variability of Means and Extremes.
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- PLoS ONE, 2016, v. 11, n. 7, p. 1, doi. 10.1371/journal.pone.0158670
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- Article
Application of distributed hydrological models for predictions in ungauged basins: a method to quantify predictive uncertainty.
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- Hydrological Processes, 2014, v. 28, n. 4, p. 2033, doi. 10.1002/hyp.9721
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Effect of spatial resolution on regionalization of hydrological model parameters.
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- Hydrological Processes, 2012, v. 26, n. 23, p. 3499, doi. 10.1002/hyp.8424
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Sensitivity and identifiability of stream flow generation parameters of the SWAT model.
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- Hydrological Processes, 2010, v. 24, n. 9, p. 1132, doi. 10.1002/hyp.7568
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Fitting of Hydrologic Models: A Close Look at the Nash–Sutcliffe Index.
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- Journal of Hydrologic Engineering, 2008, v. 13, n. 10, p. 981, doi. 10.1061/(ASCE)1084-0699(2008)13:10(981)
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Models for estimating evapotranspiration using artificial neural networks, and their physical interpretation.
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- Hydrological Processes, 2008, v. 22, n. 13, p. 2225, doi. 10.1002/hyp.6819
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A nonlinear data-driven model for synthetic generation of annual streamflows.
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- Hydrological Processes, 2008, v. 22, n. 12, p. 1831, doi. 10.1002/hyp.6764
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Fuzzy model identification based on cluster estimation for reservoir inflow forecasting.
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- Hydrological Processes, 2008, v. 22, n. 6, p. 827, doi. 10.1002/hyp.6644
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- Article
Impact of time-scale of the calibration objective function on the performance of watershed models.
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- Hydrological Processes, 2007, v. 21, n. 25, p. 3409, doi. 10.1002/hyp.6555
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- Article
A simplified approach to quantifying predictive and parametric uncertainty in artificial neural network hydrologic models.
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- Water Resources Research, 2007, v. 43, n. 10, p. n/a, doi. 10.1029/2006WR005352
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Rainfall-runoff modeling through hybrid intelligent system.
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- Water Resources Research, 2007, v. 43, n. 7, p. n/a, doi. 10.1029/2006WR004930
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- Article
LAKE WATER QUALITY ASSESSMENT FROM LANDSAT THEMATIC MAPPER DATA USING NEURAL NETWORK: AN APPROACH TO OPTIMAL BAND COMBINATION SELECTION.
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- Journal of the American Water Resources Association, 2006, v. 42, n. 6, p. 1683, doi. 10.1111/j.1752-1688.2006.tb06029.x
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A hybrid linear-neural model for river flow forecasting.
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- Water Resources Research, 2006, v. 42, n. 4, p. n/a, doi. 10.1029/2005WR004072
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Groundwater Level Forecasting in a Shallow Aquifer Using Artificial Neural Network Approach.
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- Water Resources Management, 2006, v. 20, n. 1, p. 77, doi. 10.1007/s11269-006-4007-z
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- Article
Rainfall-runoff modelling using artificial neural networks: comparison of network types.
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- Hydrological Processes, 2005, v. 19, n. 6, p. 1277, doi. 10.1002/hyp.5581
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Short-term flood forecasting with a neurofuzzy model.
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- Water Resources Research, 2005, v. 41, n. 4, p. n/a, doi. 10.1029/2004WR003562
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Fuzzy computing based rainfall–runoff model for real time flood forecasting.
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- Hydrological Processes, 2005, v. 19, n. 4, p. 955, doi. 10.1002/hyp.5553
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Explaining the internal behaviour of artificial neural network river flow models.
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- Hydrological Processes, 2004, v. 18, n. 4, p. 833, doi. 10.1002/hyp.5517
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Identification of physical processes inherent in artificial neural network rainfall runoff models.
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- Hydrological Processes, 2004, v. 18, n. 3, p. 571, doi. 10.1002/hyp.5502
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Modelling evaporation using an artificial neural network algorithm .
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- Hydrological Processes, 2002, v. 16, n. 16, p. 3189, doi. 10.1002/hyp.1096
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A data-driven algorithm for constructing artificial neural network rainfall-runoff models.
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- Hydrological Processes, 2002, v. 16, n. 6, p. 1325, doi. 10.1002/hyp.554
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- Article