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Artificial neural networks in drought prediction in the 21st century–A scientometric analysis
Droughts are the most spatially complex geohazard, which often lasts for years, thereby
severely impacting socio-economic sectors. One of the critical aspects of drought studies is …
severely impacting socio-economic sectors. One of the critical aspects of drought studies is …
Analysis, characterization, prediction, and attribution of extreme atmospheric events with machine learning and deep learning techniques: a review
Atmospheric extreme events cause severe damage to human societies and ecosystems.
The frequency and intensity of extremes and other associated events are continuously …
The frequency and intensity of extremes and other associated events are continuously …
Interpretable and explainable AI (XAI) model for spatial drought prediction
Accurate prediction of any type of natural hazard is a challenging task. Of all the various
hazards, drought prediction is challenging as it lacks a universal definition and is getting …
hazards, drought prediction is challenging as it lacks a universal definition and is getting …
Machine learning data-driven approaches for land use/cover map** and trend analysis using Google Earth Engine
With the recent advances in earth observation technologies, the increasing availability of
data from more and more different satellite sensors as well as progress in semi-automated …
data from more and more different satellite sensors as well as progress in semi-automated …
Estimation of SPEI meteorological drought using machine learning algorithms
Accurate estimation of drought events is vital for the mitigation of their adverse
consequences on water resources, agriculture and ecosystems. Machine learning …
consequences on water resources, agriculture and ecosystems. Machine learning …
Assessing spatio-temporal map** and monitoring of climatic variability using SPEI and RF machine learning models
Droughts may inflict significant damage to agricultural and water supplies, resulting in
substantial financial losses as well as the death of people and livestock. This study intends …
substantial financial losses as well as the death of people and livestock. This study intends …
Modelling of land use and land cover changes and prediction using CA-Markov and Random Forest
Abstract We used the Cellular Automata Markov (CA-Markov) integrated technique to study
land use and land cover (LULC) changes in the Cholistan and Thal deserts in Punjab …
land use and land cover (LULC) changes in the Cholistan and Thal deserts in Punjab …
A research landscape bibliometric analysis on climate change for last decades: Evidence from applications of machine learning
Climate change (CC) is one of the greatest threats to human health, safety, and the
environment. Given its current and future impacts, numerous studies have employed …
environment. Given its current and future impacts, numerous studies have employed …
Long lead time drought forecasting using lagged climate variables and a stacked long short-term memory model
Drought forecasting with a long lead time is essential for early warning systems and risk
management strategies. The use of machine learning algorithms has been proven to be …
management strategies. The use of machine learning algorithms has been proven to be …
Advanced machine learning model for prediction of drought indices using hybrid SVR-RSM
Drought, as a phenomenon that causes significant damage to agriculture and water
resources, has increased across the globe due to climate change. Hence, scientists are …
resources, has increased across the globe due to climate change. Hence, scientists are …