Zhaoan Wang¹, Kishlay Jha², Shaoping Xiao^1,*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1319-1336, 2024, DOI:10.32604/cmc.2024.055809 - 15 October 2024

Abstract Climate change poses significant challenges to agricultural management, particularly in adapting to extreme weather conditions that impact agricultural production. Existing works with traditional Reinforcement Learning (RL) methods often falter under such extreme conditions. To address this challenge, our study introduces a novel approach by integrating Continual Learning (CL) with RL to form Continual Reinforcement Learning (CRL), enhancing the adaptability of agricultural management strategies. Leveraging the Gym-DSSAT simulation environment, our research enables RL agents to learn optimal fertilization strategies based on variable weather conditions. By incorporating CL algorithms, such as Elastic Weight Consolidation (EWC), with established… More >

Muntaha Alzubade^1,*, Orkan Ozcan¹, Nebiye Musaoglu¹, Murat Türkeş²

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2255-2269, 2021, DOI:10.32604/cmc.2021.013565 - 05 February 2021

Abstract Remote sensing analysis techniques have been investigated extensively, represented by a critical vision, and are used to advance our understanding of the impacts of climate change and variability on the environment. This study aims to find a means of analysis that relies on remote sensing techniques to demonstrate the effects of observed climate variability on Land Use and Land Cover (LULC) of the Mesopotamia region, defined as a historical region located in the Middle East. This study employed the combined analysis of the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), and two statistical… More >