Artificial intelligence for climate prediction of extremes: State of the art, challenges, and future perspectives
Extreme events such as heat waves and cold spells, droughts, heavy rain, and storms are
particularly challenging to predict accurately due to their rarity and chaotic nature, and …
particularly challenging to predict accurately due to their rarity and chaotic nature, and …
A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications
Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …
large amount of data to achieve exceptional performance. Unfortunately, many applications …
Drought prediction: a comprehensive review of different drought prediction models and adopted technologies
Precipitation deficit conditions and temperature anomalies are responsible for the
occurrence of various types of natural disasters that cause tremendous loss of human life …
occurrence of various types of natural disasters that cause tremendous loss of human life …
The viability of extended marine predators algorithm-based artificial neural networks for streamflow prediction
Precise streamflow prediction is necessary for better planning and managing available
water and future water resources, especially for high altitude mountainous glacier melting …
water and future water resources, especially for high altitude mountainous glacier melting …
HBO-LSTM: Optimized long short term memory with heap-based optimizer for wind power forecasting
The forecasting and estimation of wind power is a challenging problem in renewable energy
generation due to the high volatility of wind power resources, inevitable intermittency, and …
generation due to the high volatility of wind power resources, inevitable intermittency, and …
Framework for predicting and modeling stock market prices based on deep learning algorithms
The creation of trustworthy models of the equities market enables investors to make better-
informed choices. A trading model may lessen the risks that are connected with investing …
informed choices. A trading model may lessen the risks that are connected with investing …
Forecasting weekly reference evapotranspiration using Auto Encoder Decoder Bidirectional LSTM model hybridized with a Boruta-CatBoost input optimizer
Reference evapotranspiration (ET o) is one of the most important and influential components
in optimizing agricultural water consumption and water resources management. In the …
in optimizing agricultural water consumption and water resources management. In the …
Prediction of the standardized precipitation index based on the long short-term memory and empirical mode decomposition-extreme learning machine models: The …
Ö Coşkun, H Citakoglu - Physics and Chemistry of the Earth, Parts A/B/C, 2023 - Elsevier
This research predicted the meteorological drought of Sakarya province in northwest Türkiye
using long short-term memory (LSTM). This deep learning algorithm has gained popularity …
using long short-term memory (LSTM). This deep learning algorithm has gained popularity …
Multi-step daily forecasting of reference evapotranspiration for different climates of India: A modern multivariate complementary technique reinforced with ridge …
Accurate ahead forecasting of reference evapotranspiration (ET o) is crucial for effective
irrigation scheduling and management of water resources on a regional scale. A variety of …
irrigation scheduling and management of water resources on a regional scale. A variety of …
A new stock price forecasting method using active deep learning approach
Stock price prediction is a significant research field due to its importance in terms of benefits
for individuals, corporations, and governments. This research explores the application of the …
for individuals, corporations, and governments. This research explores the application of the …