A novel method to improve vertical accuracy of CARTOSAT DEM using machine learning models DOI
Venkatesh Kasi, Pavan Kumar Yeditha, Maheswaran Rathinasamy

и другие.

Earth Science Informatics, Год журнала: 2020, Номер 13(4), С. 1139 - 1150

Опубликована: Авг. 4, 2020

Язык: Английский

Improving deep learning-based streamflow forecasting under trend varying conditions through evaluation of new wavelet preprocessing technique DOI
Mohammad Reza M. Behbahani,

Maryam Mazarei,

Amvrossios C. Bagtzoglou

и другие.

Stochastic Environmental Research and Risk Assessment, Год журнала: 2024, Номер 38(10), С. 3963 - 3984

Опубликована: Авг. 5, 2024

Язык: Английский

Процитировано

2
Quantifying predictive knowledge: Wavelet energy α-divergence measure for time series uncertainty reduction DOI
Loretta Mastroeni, Alessandro Mazzoccoli

Chaos Solitons & Fractals, Год журнала: 2024, Номер 188, С. 115488 - 115488

Опубликована: Сен. 6, 2024

Язык: Английский

Процитировано

2
Effects of the climate-related sentiment on agricultural spot prices: Insights from Wavelet Rényi Entropy analysis DOI
Loretta Mastroeni, Alessandro Mazzoccoli,

Greta Quaresima

и другие.

Energy Economics, Год журнала: 2024, Номер unknown, С. 108146 - 108146

Опубликована: Дек. 1, 2024

Язык: Английский

Процитировано

2
Network-based exploration of basin precipitation based on satellite and observed data DOI Creative Commons
Mayuri Ashokrao Gadhawe, Ravi Kumar Guntu, Ankit Agarwal

и другие.

The European Physical Journal Special Topics, Год журнала: 2021, Номер 230(16-17), С. 3343 - 3357

Опубликована: Апрель 9, 2021

Adequate and efficient precipitation data is a major concern due to its spatiotemporal variability topographic climatic factors. Satellite-based products are an alternative for reliable estimate in basins having complicated topography diverse climate zones. Satellite with global coverage continuous freely available; however, understanding spatial connections essential hydrological applications. In this study, complex network concepts like clustering coefficient, degree, degree distribution, average neighbour architecture employed investigate basin. We also identified influential grid points the using weighted betweenness. Our results reveal that correlation method does not significantly affect topology. However, threshold influences distribution of coefficient values network. The indicated inverse relationship independent similarity measures thresholds. based on satellite observed shows small-world behaviour certain range. findings unravel provide way applications further research.

Язык: Английский

Процитировано

14
A novel method to improve vertical accuracy of CARTOSAT DEM using machine learning models DOI
Venkatesh Kasi, Pavan Kumar Yeditha, Maheswaran Rathinasamy

и другие.

Earth Science Informatics, Год журнала: 2020, Номер 13(4), С. 1139 - 1150

Опубликована: Авг. 4, 2020

Язык: Английский

Процитировано

12