[HTML][HTML] Assessing rainfall prediction models: Exploring the advantages of machine learning and remote sensing approaches
Using a comparison of three different major types, the best predictive model was
determined. Statistical models and machine learning algorithms automatically learn and …
determined. Statistical models and machine learning algorithms automatically learn and …
Exogenous variable driven deep learning models for improved price forecasting of TOP crops in India
Accurately predicting agricultural commodity prices is crucial for India's economy. Traditional
parametric models struggle with stringent assumptions, while machine learning (ML) …
parametric models struggle with stringent assumptions, while machine learning (ML) …
Spatiotemporal analysis and predicting rainfall trends in a tropical monsoon-dominated country using MAKESENS and machine learning techniques
Spatiotemporal rainfall trend analysis as an indicator of climatic change provides critical
information for improved water resource planning. However, the spatiotemporal changing …
information for improved water resource planning. However, the spatiotemporal changing …
A dynamic clustering ensemble learning approach for crude oil price forecasting
J Yuan, J Li, J Hao - Engineering Applications of Artificial Intelligence, 2023 - Elsevier
Accurate oil price forecasts matter, yet the nonstationarity of oil prices makes forecasting a
challenging task. In this study, we propose a dynamic ensemble forecasting method for …
challenging task. In this study, we propose a dynamic ensemble forecasting method for …
Long-short term memory technique for monthly rainfall prediction in Thale Sap Songkhla River Basin, Thailand
Rainfall is a primary factor for agricultural production, especially in a rainfed agricultural
region. Its accurate prediction is therefore vital for planning and managing farmers' …
region. Its accurate prediction is therefore vital for planning and managing farmers' …
Deep learning model for temperature prediction: A case study in New Delhi
This study is based on temperature prediction in the capital of India (New Delhi). We have
adopted different ML models such as (MPR and DNN) which are designed and implemented …
adopted different ML models such as (MPR and DNN) which are designed and implemented …
Optimizing hyperparameters of deep hybrid learning for rainfall prediction: a case study of a Mediterranean basin
Predicting rainfall amount is essential in water resources planning and for managing
structures, especially those against floods and long-term drought establishment. Machine …
structures, especially those against floods and long-term drought establishment. Machine …
Long-term precipitation prediction in different climate divisions of California using remotely sensed data and machine learning
This study presented a novel paradigm for forecasting 12-step-ahead monthly precipitation
at 126 California gauge stations. First, the satellite-based precipitation time series from …
at 126 California gauge stations. First, the satellite-based precipitation time series from …
Accurate water quality prediction with attention-based bidirectional LSTM and encoder–decoder
Accurate prediction of water quality indicators can effectively predict sudden water pollution
events and reveal them to water users for reducing the impact of water quality pollution …
events and reveal them to water users for reducing the impact of water quality pollution …
Optimized cascaded CNN for intelligent rainfall prediction model: a research towards statistic-based machine learning
M Akhtar, ASA Shatat, SAH Ahamad… - Theoretical Issues in …, 2023 - Taylor & Francis
Using artificial intelligence to anticipate weather conditions, according to prior research, can
provide positive results. Forecasts of meteorological time series can aid disaster-prevention …
provide positive results. Forecasts of meteorological time series can aid disaster-prevention …