AI-Driven Climate Forecasting Using Biologically Inspired Models and Remote Sensing

Dear Colleagues,

The goal of AI-driven climate prediction using biologically inspired predictions and satellite imagery is to increase the precision and elimination of climate projections by utilizing the multifaceted adaptability seen in ecological systems and the extensive data gathering capabilities of remote sensing technologies. However, there are still many obstacles to overcome, such as data quality, model interpretability, and computational demands, even though the field has promising applications such as more accurate weather forecasting, early warning systems for extreme events, and optimized climate mitigation strategies. Compared to conventional physics-based models, these models may produce more nuanced forecasts by simulating natural systems such as ecosystems, animal migration patterns, or swarm intelligence in order to capture intricate interactions within the climate system. AI models can be trained and prediction accuracy increased by using the vast datasets on atmospheric conditions, land use, ocean currents, and other important climate variables that are provided by satellite imaging and other remote sensing methods.

Large volumes of high-quality data are needed to train AI models, but this can be difficult to obtain because of missing information, inconsistent observational data, and the limitations of remote sensing's spatial resolution. Some AI algorithms are "black box" in nature, which can make it hard to understand how predictions are made. This undermines confidence in the model and limits the capacity to validate its results. It can take a lot of processing power to train intricate AI models with big datasets, which prevents wider adoption. Artificial intelligence (AI) models can use biologically inspired principles to more accurately and precisely forecast local weather patterns and catastrophic occurrences like heat waves, floods, and droughts. AI algorithms and remote sensing data can be combined to create early warning systems that notify populations of approaching natural disasters.

AI can predict changes in agricultural yields or find appropriate sites for renewable energy sources, among other adaptation and mitigation techniques, by analyzing complicated climate scenarios. Ocean currents, marine ecosystems, and their interactions with the atmosphere can all be better understood and predicted using models that draw inspiration from biology, optimizing climate model settings by imitating swarming insect behavior. This technology has been used in several domains, such as change detection, object recognition, and image categorization. AI-enabled solutions, from early warning systems for natural disasters to maximizing agri-food production and enhancing energy system efficiency, can help with both adaptation and mitigation efforts related to climate change. We welcome contributions from various disciplines and perspectives, without being limited to specific fields: AI-driven Climate Forecasting Using Biologically Inspired Models and Remote Sensing.

The topics of interest include, but are not limited to the following:

• Remote sensing and machine learning for climate prediction;
• AI-powered models for climate prediction that use evolutionary algorithms;
• Systems using fuzzy logic to accurately analyze global warming;
• Predicting severe weather patterns using deep learning;
• Swarm intelligence in simulations for identifying patterns in the climate;
• AI with a bioinspired approach to anticipate ocean temperature;
• Detecting anomalies in the global climate using hybrid AI models;
• Data fusion from satellite imagery for the investigation of climate trends;
• Genetic algorithms for improving models of climate forecasting;
• Neuro-fuzzy methods to increase the precision of weather forecasts;
• AI self-learning algorithms for predicting the climate over the long run;
• AI-powered satellite imagery processing for climate change study;
• AI-powered prediction of drought through biologically inspired computing;
• Frameworks for responsive AI in real-time atmospheric monitoring;
• AI models powered by sensors to predict rainfall with reliability.

Dr. Chandan Roy
Dr. Philip Kibet Langat
Dr. Manoj Kumer Ghosh
Guest Editors

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• AI-driven climate forecasting
• biologically inspired models
• remote sensing
• machine learning for climate prediction
• evolutionary algorithms
• swarm intelligence
• deep learning for weather prediction
• hybrid AI models
• satellite data fusion
• climate change adaptation