|
|
|
|
|
Saeed Samadianfard, Salar Jarhan, Ely Salwana, Amir Mosavi, Shahaboddin Shamshirband and Shatirah Akib
Advancement in river flow prediction systems can greatly empower the operational river management to make better decisions, practices, and policies. Machine learning methods recently have shown promising results in building accurate models for river flow...
ver más
|
|
|
|
|
|
|
|
|
|
|
Uxia Garcia-Luis, Alejandro M. Gomez-San-Juan, Fermin Navarro-Medina, Carlos Ulloa-Sande, Alfonso Yñigo-Rivera and Alba Eva Peláez-Santos
The integration of uncertainty analysis methodologies allows for improving design efficiency, particularly in the context of instruments that demand precise pointing accuracy, such as space telescopes. Focusing on the VINIS Earth observation telescope de...
ver más
|
|
|
|
|
|
|
|
|
|
|
Charalampos Skoulikaris
Large-scale hydrological modeling is an emerging approach in river hydrology, especially in regions with limited available data. This research focuses on evaluating the performance of two well-known large-scale hydrological models, namely E-HYPE and LISF...
ver más
|
|
|
|
|
|
|
|
|
|
|
Jhon Lennon Bezerra da Silva, Marcos Vinícius da Silva, Alexandre Maniçoba da Rosa Ferraz Jardim, Pabrício Marcos Oliveira Lopes, Henrique Fonseca Elias de Oliveira, Josef Augusto Oberdan Souza Silva, Márcio Mesquita, Ailton Alves de Carvalho, Alan Cézar Bezerra, José Francisco de Oliveira-Júnior, Maria Beatriz Ferreira, Iara Tamires Rodrigues Cavalcante, Elania Freire da Silva and Geber Barbosa de Albuquerque Moura
Northeast Brazil (NEB), particularly its semiarid region, represents an area highly susceptible to the impacts of climate change, including severe droughts, and intense anthropogenic activities. These stresses may be accelerating environmental degradatio...
ver más
|
|
|
|
|
|
|
|
|
|
|
Károly Héberger
Background: The development and application of machine learning (ML) methods have become so fast that almost nobody can follow their developments in every detail. It is no wonder that numerous errors and inconsistencies in their usage have also spread wi...
ver más
|
|
|
|
|
|
|
|
|
|
|
Che-Hao Chang, Jason Lin, Jia-Wei Chang, Yu-Shun Huang, Ming-Hsin Lai and Yen-Jen Chang
Recently, data-driven approaches have become the dominant solution for prediction problems in agricultural industries. Several deep learning models have been applied to crop yield prediction in smart farming. In this paper, we proposed an efficient hybri...
ver más
|
|
|
|
|
|
|
|
|
|
|
Ahmed Skhiri, Ali Ferhi, Anis Bousselmi, Slaheddine Khlifi and Mohamed A. Mattar
A correct determination of irrigation water requirements necessitates an adequate estimation of reference evapotranspiration (ETo). In this study, monthly ETo is estimated using artificial neural network (ANN) models. Eleven combinations of long-term ave...
ver más
|
|
|
|
|
|
|
|
|
|
|
Aymane Ahajjam, Jaakko Putkonen, Emmanuel Chukwuemeka, Robert Chance and Timothy J. Pasch
Local weather forecasts in the Arctic outside of settlements are challenging due to the dearth of ground-level observation stations and high computational costs. During winter, these forecasts are critical to help prepare for potentially hazardous weathe...
ver más
|
|
|
|
|
|
|
|
|
|
|
Tahsin Koroglu and Elanur Ekici
In recent years, wind energy has become remarkably popular among renewable energy sources due to its low installation costs and easy maintenance. Having high energy potential is of great importance in the selection of regions where wind energy investment...
ver más
|
|
|
|
|
|
|
|
|
|
|
Vinayak Bhanage, Han Soo Lee, Tetsu Kubota, Radyan Putra Pradana, Faiz Rohman Fajary, I Dewa Gede Arya Putra and Hideyo Nimiya
This study evaluates the performance of 6 global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) for simulating temperature, precipitation, wind speed, and relative humidity over 29 cities in Indonesia. Modern-Era Ret...
ver más
|
|
|
|
|
|