Ditapis dengan
Water resource forecasting with machine learning and deep learning: A sciento…
Water prediction plays a crucial role in modern-day water resource management, encompassing both hydrological patterns and demand forecasts. To gain insights into its current focus, status, and emerging themes, this study analyzed 876 articles published between 2015 and 2022, retrieved from the Web of Science database. Leveraging CiteSpace visualization software, bibliometric techniques, and li…
- Edisi
- Vol.5, December 2024
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 12 hlm PDF, 4.935 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Forecast future disasters using hydro-meteorological datasets in the Yamuna r…
This research aim to evaluate hydro-meteorological data from the Yamuna River Basin, Uttarakhand, India, utilizing Extreme Value Distribution of Frequency Analysis and the Markov Chain Approach. This method assesses persistence and allows for combinatorial probability estimations such as initial and transitional probabilities. The hydrologic data was generated (in-situ) and received from Uttara…
- Edisi
- Vol.5, December 2024
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 23 hlm PDF, 18,684 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Revolutionizing the future of hydrological science: Impact of machine learnin…
Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are revolutionizing hydrology, driving significant advancements in water resource management, modeling, and prediction. This review synthesizes cutting-edge developments, methodologies, and applications of AI-ML-DL across key hydrological processes. By critically evaluating these techniques against traditional models, w…
- Edisi
- Vol.24, December 2024
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 16 hlm PDF, 2.174 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
Modeling river flow for flood forecasting: A case study on the Ter river
Floods affect chronically many communities around the world. Their socioeconomic impact increases year-by-year, boosted by global warming and climate change. Combined with long-term preemptive measures, preparatory actions are crucial when floods are imminent. In the last decade, machine learning models have been used to anticipate these hazards. In this work, we model the Ter river (NE Spain),…
- Edisi
- Vol.23, September 2024
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 12 hlm PDF, 2.425 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
Global Normalized Difference Vegetation Index forecasting from air temperatur…
The complexity of the relationship between climate variables including temperature, precipitation, soil moisture, and the Normalized Difference Vegetation Index (NDVI) arises from the complex interaction between these factors. NDVI is a widely used index to analyze the characteristics of vegetation cover, including its dynamic patterns. It is a crucial parameter for examining vegetation stabili…
- Edisi
- Vol.23, September 2024
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 11 hlm PDF, 6.026 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
Forecast of convective events via hybrid model: WRF and machine learning algo…
This presents a novel hybrid 24-h forecasting model of convective weather events based on numerical simulation and machine learning algorithms. To characterize the convective events, 13-year from 2008 up to 2020 of precipitation data from the main airport stations in Rio de Janeiro, Brazil, and atmospheric discharges from the surrounding area of around 150 km are investigated. The Weather Resea…
- Edisi
- Vol.16, December 2022
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 14 hlm PDF, 14.531 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
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