Fine-tuning inflow prediction models: integrating optimization algorithms and TRMM data for enhanced accuracy
This research explores machine learning algorithms for reservoir inflow prediction, including
long short-term memory (LSTM), random forest (RF), and metaheuristic-optimized models …
long short-term memory (LSTM), random forest (RF), and metaheuristic-optimized models …
Modelling monthly rainfall of India through transformer-based deep learning architecture
GHH Nayak, W Alam, KN Singh, G Avinash… - Modeling Earth Systems …, 2024 - Springer
In the realm of Earth systems modelling, the forecasting of rainfall holds crucial significance.
The accurate prediction of monthly rainfall in India is paramount due to its pivotal role in …
The accurate prediction of monthly rainfall in India is paramount due to its pivotal role in …
Optimizing long short-term memory networks for univariate time series forecasting: a comprehensive guide
This article presents a comprehensive exploration of the adaptation of long short-term
memory (LSTM) neural networks for univariate time series forecasting, a critical area in …
memory (LSTM) neural networks for univariate time series forecasting, a critical area in …
Predicting groundwater drawdown in Zakho region, Northern Iraq, using machine learning models optimized by the whale optimization algorithm
Predicting groundwater drawdown is crucial to the Duhok Governorate's sustainable
management of its water resources. To ensure long-term water availability as extraction from …
management of its water resources. To ensure long-term water availability as extraction from …
Enhancing Decision-Making in Highway Overtaking Scenarios with Graph Convolution Reinforcement Learning
MK Sam, W Gee, S Arkhstan, H Khan… - Journal of Computer …, 2024 - jcsis.org
Autonomous vehicles have a number of open challenges, one of which is decision-making
regarding motion, particularly while operating in an environment that is both complex and …
regarding motion, particularly while operating in an environment that is both complex and …
Deep Learning and Enhanced Emissions Modeling and Deposition Prediction
Deep Learning and Enhanced Emissions Modeling and Deposition Prediction JCSIS
019928311823 info@jcsis.org JCSIS About Volumes Instructions Contact Editorials Login …
019928311823 info@jcsis.org JCSIS About Volumes Instructions Contact Editorials Login …
Optimizing convolutional neural networks for univariate time series forecasting: a comprehensive guide
This chapter delves into the nuanced process of optimizing convolutional neural networks
(CNNs) for univariate time series forecasting, an area of critical importance in predictive …
(CNNs) for univariate time series forecasting, an area of critical importance in predictive …
Innovative approaches to surface water quality management: advancing nitrate (NO3) forecasting with hybrid CNN-LSTM and CNN-GRU techniques
Accurate nitrate estimation in surface water is essential for ecological and human health. To
predict nitrate levels (NO3; mg/L) in the Willamette River at Portland, Oregon, USA, three …
predict nitrate levels (NO3; mg/L) in the Willamette River at Portland, Oregon, USA, three …
Crop Yield Estimation Using Spiking Neural Networks Through Spatiotemporal Analysis of Image Time Series
N OubeBlika, S Arkhstan, L Hongou… - Journal of Computer …, 2024 - jcsis.org
Crop Yield Estimation Using Spiking Neural Networks Through Spatiotemporal Analysis of
Image Time Series JCSIS 019928311823 info@jcsis.org JCSIS About Volumes Instructions …
Image Time Series JCSIS 019928311823 info@jcsis.org JCSIS About Volumes Instructions …
CNN-Based Algorithm for Anomaly Detection in Electrocardiogram Signals (BiLSTM)
CNN-Based Algorithm for Anomaly Detection in Electrocardiogram Signals (BiLSTM) JCSIS
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