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Chulsang Yoo and Eunsaem Cho
This study evaluated 20 general circulation models (GCMs) of the Coupled Model Intercomparison Project, Phase 5 (CMIP5), which provide the prediction results for the period of 2006 to 2014, the period from which the observation data (the Global Precipita...
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Victor Hugo da Motta Paca, Everaldo Barreiros de Souza, Joaquim Carlos Barbosa Queiroz and Gonzalo E. Espinoza-Dávalos
The aim of this study was to assess precipitation (P) and actual evapotranspiration (ET) by analyzing data from in situ stations compared with remote sensing products. Climate Hazards Center InfraRed Precipitation (CHIRP) and Climate Prediction Center mo...
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Luis Giménez, Paula Paredes and Luis S. Pereira
Data relative to two soybean seasons, several irrigation scheduling treatments, including moderate and severe deficit irrigation, and rain-fed cropping were used to parameterize and assess the performance of models AquaCrop and SIMDualKc, the latter comb...
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Luis Giménez, Paula Paredes, Luis S. Pereira
Pág. 1 - 18
Data relative to two soybean seasons, several irrigation scheduling treatments, including moderate and severe deficit irrigation, and rain-fed cropping were used to parameterize and assess the performance of models AquaCrop and SIMDualKc, the latter comb...
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César Augusto Terán-Chaves, Alberto García-Prats and Sonia Mercedes Polo-Murcia
Crop models that can accurately estimate yield and final biomass have been used for major herbaceous crops and to a lesser extent in forage systems. The AquaCrop version 7.0 contains new modules that have been introduced to simulate the growth and produc...
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Jianyong Zhang, Yanling Zhao, Zhenqi Hu and Wu Xiao
Rapid estimation of above-ground biomass (AGB) with high accuracy is essential for monitoring crop growth status and predicting crop yield. Recently, remote sensing techniques using unmanned aerial systems (UASs) have exhibited great potential in obtaini...
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Gilles Notton, Cyril Voyant, Alexis Fouilloy, Jean Laurent Duchaud and Marie Laure Nivet
In solar energy, the knowledge of solar radiation is very important for the integration of energy systems in building or electrical networks. Global horizontal irradiation (GHI) data are rarely measured over the world, thus an artificial neural network (...
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Moisés Cordeiro-Costas, Daniel Villanueva, Pablo Eguía-Oller, Miguel Martínez-Comesaña and Sérgio Ramos
Characterizing the electric energy curve can improve the energy efficiency of existing buildings without any structural change and is the basis for controlling and optimizing building performance. Artificial Intelligence (AI) techniques show much potenti...
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Fuat Kaya, Ali Keshavarzi, Rosa Francaviglia, Gordana Kaplan, Levent Basayigit and Mert Dedeoglu
Predicting soil chemical properties such as soil organic carbon (SOC) and available phosphorus (Ava-P) content is critical in areas where different land uses exist. The distribution of SOC and Ava-P is influenced by both natural and anthropogenic factors...
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Luca Castellini, Federico Gallorini, Giacomo Alessandri, Erick Fernando Alves, Dan Montoya, Bhavana Mudigonda and Elisabetta Tedeschi
The power take-off (PTO) of a wave energy converter (WEC) converts mechanical power extracted from the waves into electrical power. Increasing PTO performance under several operational conditions is therefore essential to reduce the levelized cost of ene...
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Donghyun Kim, Heechan Han, Wonjoon Wang and Hung Soo Kim
Accurate water level prediction is one of the important challenges in various fields such as hydrology, natural disasters, and water resources management studies. In this study, a deep neural network and a long short-term memory model were applied for wa...
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Amrit Bhusal, Utsav Parajuli, Sushmita Regmi and Ajay Kalra
Rainfall-runoff simulation is vital for planning and controlling flood control events. Hydrology modeling using Hydrological Engineering Center?Hydrologic Modeling System (HEC-HMS) is accepted globally for event-based or continuous simulation of the rain...
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Bagus Setiabudi Wiwoho, Ike Sari Astuti, Imam Abdul Gani Alfarizi and Hetty Rahmawati Sucahyo
A total of three different satellite products, CHIRPS, GPM, and PERSIANN, with different spatial resolutions, were examined for their ability to estimate rainfall data at a pixel level, using 30-year-long observations from six locations. Quantitative and...
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Sanjiwana Arjasakusuma, Sandiaga Swahyu Kusuma and Stuart Phinn
Machine learning has been employed for various mapping and modeling tasks using input variables from different sources of remote sensing data. For feature selection involving high- spatial and spectral dimensionality data, various methods have been devel...
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Yuebing Xu, Jing Zhang, Zuqiang Long, Hongzhong Tang and Xiaogang Zhang
Effective and accurate water demand prediction is an important part of the optimal scheduling of a city water supply system. A novel deep architecture model called the continuous deep belief echo state network (CDBESN) is proposed in this study for the p...
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Dean C. J. Rice, Rupp Carriveau, David S. -K. Ting and Mo?tamad H. Bata
Forecasting crop water demand is a critical part of any greenhouse?s day-to-day operations. This study focuses on a region located in Essex County, Ontario Canada where water demand is dominated by commercial greenhouse operations (78% of capacity). Deve...
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Mugdha P. Kshirsagar and Kanchan C. Khare
The present study is an attempt to model the stormwater quality of a stream located in Pune, India. The city is split up into twenty-three basins (named A to W) by the Pune Municipal Corporation. The selected stream lies in the haphazardly expanded peri-...
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Ganapathy Ramesh, Jaganathan Logeshwaran, Thangavel Kiruthiga and Jaime Lloret
In general, reliable PV generation prediction is required to increase complete control quality and avoid potential damage. Accurate forecasting of direct solar radiation trends in PV power production could limit the influence of uncertainties on photovol...
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Juan V. Molner, Juan M. Soria, Rebeca Pérez-González and Xavier Sòria-Perpinyà
Water transparency, a crucial environmental indicator, was assessed during fieldwork via Secchi disk depth (ZSD) measurements. Three optical models (R490/R560, R490/R705, and R560/R705) were explored to establish a robust algorithm for ZSD estimation. Th...
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Sebastian C. Ibañez and Christopher P. Monterola
Accurate prediction of crop production is essential in effectively managing the food security and economic resilience of agricultural countries. This study evaluates the performance of statistical and machine learning-based methods for large-scale crop p...
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