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Nur Hamiza Adenan, Mohd Salmi Md Noorani
2016 Jurnal Teknologi  
The prediction result is close to agreement with a high correlation coefficient for each time scale.  ...  Hence, an analysis and prediction of multiple time-scales data for daily, weekly and 10-day averaged time series were performed using chaos approach.  ...  Nonlinear prediction based on local linear approximation method is used for the purpose of prediction. The whole data set for 10 years (January1981 to December1990) is involved.  ... 
doi:10.11113/jt.v78.3561 fatcat:rqmqupwjynfq7hcl2eepmcdecu

Review of Nitrogen Compounds Prediction in Water Bodies Using Artificial Neural Networks and Other Models

Pavitra Kumar, Sai Hin Lai, Jee Khai Wong, Nuruol Syuhadaa Mohd, Md Rowshon Kamal, Haitham Abdulmohsin Afan, Ali Najah Ahmed, Mohsen Sherif, Ahmed Sefelnasr, Ahmed El-Shafie
2020 Sustainability  
Considering the stochastic nature and the various hydrological variables upon which nitrogen concentration depends, a predictive model should be efficient enough to account for all the complexities of  ...  A precise prediction model guarantees the delivering of better-quality water for human use, as the operations of various water treatment plants depend on the concentration of nitrogen in streams.  ...  Conflicts of interest: We declare no conflicts of interest with any person or institute.  ... 
doi:10.3390/su12114359 fatcat:wjx6keja5revhg6rzndr7m3cvm

The role of data assimilation in predictive ecology

Shuli Niu, Yiqi Luo, Michael C. Dietze, Trevor F. Keenan, Zheng Shi, Jianwei Li, F. Stuart Chapin III
2014 Ecosphere  
In this rapidly changing world, improving the capacity to predict future dynamics of ecological systems and their services is essential for better stewardship of the earth system.  ...  Prediction relies on models that describe our understanding of the major processes that underlie system dynamics and data about these processes and the present state of ecosystems.  ...  Data assimilation (DA) has the potential to enable and empower predictive ecology . DA refers to a suite of statistical techniques used to improve process models based on data.  ... 
doi:10.1890/es13-00273.1 fatcat:bir7jxtganek3bssobke7ck7yq

Integrating seasonal climate prediction and agricultural models for insights into agricultural practice

J. W. Hansen
2005 Philosophical Transactions of the Royal Society of London. Biological Sciences  
Interest in integrating crop simulation models with dynamic seasonal climate forecast models is expanding in response to a perceived opportunity to add value to seasonal climate forecasts for agriculture  ...  capabilities and limitations, and with cautious evaluation of model predictions and of the insights that arises from model-based decision analysis.  ...  to daily values using a stochastic weather generator.  ... 
doi:10.1098/rstb.2005.1747 pmid:16433092 pmcid:PMC1569571 fatcat:7ubegoanbbbprnjaiip4mtgcqy

Advances in ungauged streamflow prediction using artificial neural networks

Lance E. Besaw, Donna M. Rizzo, Paul R. Bierman, William R. Hackett
2010 Journal of Hydrology  
A scaling ratio, based on a relationship between bankfull discharge and basin drainage area, accounts for the change in drainage area from one basin to another.  ...  Hourly streamflow predictions were superior to those using daily data for the small streams tested due the loss of critical lag times through upscaling.  ...  VT EPSCoR Grant (NSF EPS #0701410), the US Geological Survey, and the Vermont Agency of Natural Resources and is a contribution from the Water Resources and Lake Studies Center.  ... 
doi:10.1016/j.jhydrol.2010.02.037 fatcat:wvgelqdmy5cmfafkaov44r7a3q

Modeling Streamflow in a Snow-Dominated Forest Watershed Using the Water Erosion Prediction Project (WEPP) Model

Anurag Srivastava, Joan Q. Wu, William J. Elliot, Erin S. Brooks, Dennis C. Flanagan
2017 American Society of Agricultural and Biological Engineers. Transactions  
The Water Erosion Prediction Project (WEPP) model was originally developed for hillslope and small watershed applications.  ...  Recent improvements to WEPP have led to enhanced computations for deep percolation, subsurface lateral flow, and frozen soil.  ...  That the soil may continually sustain vegetation growth while carrying a soil water deficit from one year to the next shows the importance of understanding long-term weather patterns.  ... 
doi:10.13031/trans.12035 fatcat:ixkbmcxli5fz3cjsmcfirrfi6y

Farm-Scale Crop Yield Prediction from Multi-Temporal Data Using Deep Hybrid Neural Networks

Martin Engen, Erik Sandø, Benjamin Lucas Oscar Sjølander, Simon Arenberg, Rashmi Gupta, Morten Goodwin
2021 Agronomy  
Farm-scale crop yield prediction is a natural development of sustainable agriculture, producing a rich amount of food without depleting and polluting environmental resources.  ...  The regional-scale crop yield predictions usually face challenges in capturing local yield variations based on farm management decisions and the condition of the field.  ...  Acknowledgments: We would like to thank the Faculty of Engineering and Science and the CAIR Research lab at the University of Agder, Norway, for allowing us to conduct the research on this topic.  ... 
doi:10.3390/agronomy11122576 fatcat:32ccu6xccvgkfknmxll45lblgy

Assessing the potential of an algorithm based on mean climatic data to predict wheat yield

Benjamin Dumont, Vincent Leemans, Salvador Ferrandis, Bernard Bodson, Jean-Pierre Destain, Marie-France Destain
2014 Precision Agriculture  
This paper presents a methodology that addresses the problem of unknown future weather by using a daily mean climatic database, based exclusively on available past measurements.  ...  The real-time non-invasive determination of crop biomass and yield prediction is one of the major challenges in agriculture.  ...  They are very grateful to CRA-w, especially the Department 'Agriculture et milieu naturel', for the Ernage station climate database and to staff in the ULg-GxABT Geopedological Unit for their soil analyses  ... 
doi:10.1007/s11119-014-9346-9 fatcat:ujyfwzbmpjfirlmcfwbnpwgvna

Using Satellite Remote Sensing and Machine Learning Techniques Towards Precipitation Prediction and Vegetation Classification

D. Stampoulis, Future H2O, Office of Knowledge Enterprise Development, Arizona State University, Tempe, AZ 85281, USA, H. G. Damavandi, D. Boscovic, J. Sabo, Future H2O, Office of Knowledge Enterprise Development, Arizona State University, Tempe, AZ 85281, USA, Center for Assured and Scalable Data Engineering, Arizona State University, Tempe, AZ 85281, USA, Future H2O, Office of Knowledge Enterprise Development, Arizona State University, Tempe, AZ 85281, USA
2020 Journal of Environmental Informatics  
soil moisture (SM) and surface temperature (ST).  ...  novel approach that predicts precipitation spatio-temporal trends over the drought-burdened region of East Africa, based on other major hydrological components, such as vegetation water content (VWC),  ...  This work was supported by the National Science Foundation award (grant: GR10458) and conducted at Future H2O, Office of Knowledge Enterprise Development (OKED) at Arizona State University.  ... 
doi:10.3808/jei.202000427 fatcat:3zwlwu6gyndlzhmohucjrmsyhi

Integrated uncertainty assessment of discharge predictions with a statistical error model

M. Honti, C. Stamm, P. Reichert
2013 Water Resources Research  
The effects of the input uncertainty are simulated with a stochastic linearized rainfall-runoff model.  ...  1] A proper uncertainty assessment of rainfall-runoff predictions has always been an important objective for modelers.  ...  This study was part of the iWaQa project financed by the Swiss National Science Foundation (National Research Program 61 on Sustainable Water Management, grant 406140-125866) and the Swiss Federal Office  ... 
doi:10.1002/wrcr.20374 fatcat:nykgl5qcf5e4tkj7ecv3fxcf3q

Prediction of Rainfall Using Intensified LSTM Based Recurrent Neural Network with Weighted Linear Units

S. Poornima, M. Pushpalatha
2019 Atmosphere  
Several techniques have been formerly proposed to predict rainfall based on statistical analysis, machine learning and deep learning techniques.  ...  Prediction of rainfall is one of the major concerns in the domain of meteorology.  ...  A K-nearest-neighbor (KNN)-based non-parametric non-homogeneous hidden Markov model was developed in [19] and applied for spatial down scaling of multi-station daily rainfall occurrences.  ... 
doi:10.3390/atmos10110668 fatcat:bo3le6jbt5g3vlug3wyp67gbfa

A decade of Predictions in Ungauged Basins (PUB)—a review

M. Hrachowitz, H.H.G. Savenije, G. Blöschl, J.J. McDonnell, M. Sivapalan, J.W. Pomeroy, B. Arheimer, T. Blume, M.P. Clark, U. Ehret, F. Fenicia, J.E. Freer (+14 others)
2013 Hydrological Sciences Journal  
2012) , set out to shift the scientific culture of hydrology towards improved scientific understanding of hydrological processes, as well as associated uncertainties and the development of models with  ...  decade of Predictions in Ungauged Basins (PUB)-a review  ...  existing and developing new models based on improved process understanding with reduced need for calibration, which were conceived to be the necessary steps forward at that time.  ... 
doi:10.1080/02626667.2013.803183 fatcat:yvqlcrsuszcd5gbtxbre7ra7n4

Conversion of the Time Series of Measured Soil Moisture Data to a Daily Time Step – a Case Study Utilizing the Random Forests Algorithm

Milan Cisty, Lubomir Celar
2016 Journal of Sustainable Development of Energy, Water and Environment Systems  
Soil water prediction, e.g., for irrigation planning, should be performed with a daily time step.  ...  ABSTRACT Modeling the water content in soil is important for the development of agricultural information systems. Various data are necessary for such modelling.  ...  The work submitted compares soil water content models based on a data-driven methodology with the aim of accomplishing an interpolation task and obtaining a time series of soil moisture with a daily time  ... 
doi:10.13044/j.sdewes.2016.04.0015 fatcat:avnwt4g5b5ebtmkt77d4qedfoa

Hydrograph Separation by Incorporating Climatological Factors: Application to Small Experimental Watersheds

Amir P. Nejadhashemi, Joseph M. Sheridan, Adel Shirmohammadi, Hubert J. Montas
2007 Journal of the American Water Resources Association  
This study therefore focused on applying a piecewise linear regression model for predicting alpha.  ...  The piecewise linear regression equations for predicting the fraction (ce) value based on climatological factors was developed using the forward difference form of Boughton's equation and streamfiow data  ... 
doi:10.1111/j.1752-1688.2007.00059.x fatcat:l5zzdtuvlrfrhii7ql5ky4w3b4

Current and emerging developments in subseasonal to decadal prediction

William J. Merryfield, Johanna Baehr, Lauriane Batté, Emily J. Becker, Amy H. Butler, Caio A. S. Coelho, Gokhan Danabasoglu, Paul A. Dirmeyer, Francisco J. Doblas-Reyes, Daniela I. V. Domeisen, Laura Ferranti, Tatiana Ilynia (+53 others)
2020 Bulletin of The American Meteorological Society - (BAMS)  
Capsule Climate prediction on subseasonal to decadal time scales is a rapidly advancing field that is synthesizing improvements in climate process understanding and modeling to improve and expand operational  ...  The International Conferences on Subseasonal to Decadal Prediction on which this paper is based were sponsored by U.S.  ...  Climate Change Service, IPSL, and WWRP/WCRP's Subseasonal-to-Seasonal (S2S) Prediction Project.  ... 
doi:10.1175/bams-d-19-0037.1 fatcat:hgpwcmb6mnb3xoeathb5rzlq5q
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