Comparative analysis of state-of-the-art neural networks for predicting land surface temperature and NDVI patterns using earth engine data in semi-arid regions

Evaluation Of CNN-LSTM with Attention for Forest Fire Prediction in Indonesia: Challenges of  Imbalanced Data

It is concluded that deep learning approaches for forest fire prediction in tropical regions face significant challenges related to class imbalance and temporal stability, necessitating the development of more adaptive modeling strategies.

A novel SVMD-CNN-LSTM hybrid model for spatiotemporal groundwater quality index prediction in the ecologically sensitive Eastern Lake Urmia Region

Remote Sensing Inversion Model of Cultivated Land Salinity Based on Attention Mechanism and Lightweight CNN: Construction, Validation, and Multi-Model Comparative Analysis

Soil salinization threatens agriculture in arid regions, and remote sensing retrieval still faces challenges of unclear mechanisms and poor generalization. Based on Sentinel-2 data, this study compares the retrieval performance of various machine learning and deep learning models for farmland soil salt content, and int...

A Hybrid Ensemble Learning Approach for Accurate SPI₆-Based Drought Forecasting in Semi-Arid Regions

This study proposes a hybrid machine learning framework to predict the six-month Standardized Precipitation Index (SPI₆) for meteorological drought assessment in Nanded, India, using NASA POWER data and demonstrates that ensemble learning enhances SPI prediction accuracy.

Comprehensive Performance Evaluation of Deep Learning Algorithms for Multi-Variable Climate Prediction: RNN, LSTM, and GRU Analysis

The selection of appropriate deep learning architectures for climate prediction remains a  critical challenge in atmospheric sciences, with different algorithms showing varying performance across climate variables and geographical regions. This study presents a comprehensive comparative analysis of three prominent deep...

A novel hybrid remote sensing–machine learning framework for multiday soil moisture forecasting

The proposed method performed better than the conventional LSTM algorithm in all forecasting scenarios and showed robust performance even at a 7-day forecasting lead time, showing promise for applications in short-range soil moisture prediction and environmental monitoring studies.