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Deep Learning-Based Manatee Crowd Counting Using Anisotropic Gaussian Kernel for Low-Quality Images

This paper presents a novel approach for automatically counting manatees within a region using deep learning, even when provided with low-quality images. Manatees, being slow-moving aquatic mammals often found in aggregations in shallow waters, pose challenges such as water surface reflections, occlusion, and camouflage.

17 Nov 2023

Assessing Flood Susceptibility in Cyclone-Prone Regions Using Random Forest Machine Learning

This article delves into the assessment of flood susceptibility in Australian tropical cyclone-prone regions, focusing on the impact of tropical cyclone Debbie in 2017. Researchers employ a Random Forest (RF) machine learning model, optimized by differential evolution, and satellite remote sensing data to create a flood hazard map for the Airlie Beach, Mackay, and Bowen regions in North Queensland.

15 Nov 2023

Enhancing Sugarcane Yield Prediction in China: A WCA-LSSVM Approach

This study introduces a novel approach for forecasting sugarcane yield in major Chinese production regions. Utilizing the Water Cycle Algorithm (WCA) to fine-tune the Least Squares Support Vector Machine (LSSVM) model, the proposed method demonstrates superior accuracy and generalization capabilities, offering valuable insights for optimizing sugarcane production practices.

10 Nov 2023

Machine Learning in Weather and Climate Forecasting: Advancements and Challenges

This review article discusses the evolution of machine learning applications in weather and climate forecasting. It outlines the historical transition from statistical methods to physical models and the recent emergence of machine learning techniques. The article categorizes machine learning applications in climate prediction, covering both short-term weather forecasts and medium-to-long-term climate predictions.

7 Nov 2023

Leveraging Artificial Intelligence for Food Security: A Comprehensive Review

This paper delves into the extensive use of artificial intelligence (AI) models for assessing food security indicators across the globe, with a notable focus on sub-Saharan Africa. The study emphasizes the importance of stakeholder involvement in AI modeling for food security, highlighting three key approaches to integrating AI into food security research.

29 Oct 2023

Revolutionizing Landslide Monitoring in Japan with AI-Enhanced InSAR Technology

SKY Perfect JSAT, a Japanese satellite communications company, has developed an AI-driven InSAR service that provides precise, cost-effective, and extensive coverage, improving safety across urban, rural, and wilderness settings, and potentially replacing labor-intensive ground-based surveys. The service has the potential to reduce uncertainties and enhance safety regarding landslides and subsidence issues in Japan.

23 Oct 2023

Improving Lake Water Level Forecasting Using Deep Learning and Bayesian Model Averaging

Researchers apply three deep learning models and Bayesian Model Averaging (BMA) to enhance water level predictions at multiple stations around Poyang Lake. Their approach, combining DL models with BMA, demonstrated improved accuracy in forecasting and reduced uncertainty, offering valuable insights for disaster mitigation and resource management in the region.

16 Oct 2023

Enhancing Short-Term Solar Energy Forecasting with Deep Learning Models

Researchers used a combination of machine learning and deep learning models, including Bi-LSTM variants, to improve short-term solar energy predictions based on climatic factors in Amherst. Deep learning models consistently outperformed traditional machine learning techniques, highlighting their potential to enhance the accuracy and reliability of solar energy forecasts, crucial for efficient renewable energy utilization.

6 Oct 2023

AI-Powered Groundwater Prediction for Sustainable Irrigation in Water-Scarce Regions

Researchers harnessed artificial intelligence to predict groundwater levels in Ethiopia's Bilate watershed, a water-scarce region. Their study revealed that Gradient Boosting Regression (GBR) performed exceptionally well, offering a valuable tool for sustainable borehole drilling decisions, particularly for irrigation, in water-scarce regions.

3 Oct 2023

Comparing AI and Lexicon-Based Approaches for Short Text Classification in Social Sciences

A recent study delves into the automated classification of short texts from social media, crucial for social science research. The research compares lexicon-based and supervised machine learning approaches, highlighting the significance of traditional ML algorithms in short text classification and their efficiency compared to deep neural architectures, especially in cases with limited data resources.

3 Oct 2023

Espresso: Deep Learning Model for Global Precipitation Estimation from Satellite Data

Researchers introduce Espresso, a deep-learning model for global precipitation estimation using geostationary satellite input and calibrated with Global Precipitation Measurement Core Observatory (GPMCO) data. Espresso outperforms other products in storm localization and intensity estimation, making it an operational tool at Meteo-France for real-time global precipitation estimates every 30 minutes, with potential for further improvement in higher latitudes.

28 Sep 2023

Harnessing Machine Learning for Advancing Offshore Wind Energy

Researchers have conducted a comprehensive review of the offshore wind energy industry, emphasizing the role of machine learning (ML) and artificial intelligence (AI) in addressing challenges related to turbine size, efficiency, environmental impact, and deep-water deployment. ML applications include climate forecasting, environmental impact assessment, wind farm optimization, and more.

26 Sep 2023

AI Breaks New Ground in Weather Forecasting

This article explores the emerging role of Artificial Intelligence (AI) in weather forecasting, discussing the use of foundation models and advanced techniques like transformers, self-supervised learning, and neural operators. While still in its early stages, AI promises to revolutionize weather and climate prediction, providing more accurate forecasts and deeper insights into climate change's effects.

22 Sep 2023

Harnessing AI for Water Quality Prediction and Assessment

This article delves into the application of artificial intelligence (AI) techniques in predicting water quality indices and classifications. It highlights the advantages and challenges of implementing AI in water quality monitoring and modeling and explores advancements in machine learning for assessing various water quality parameters.

21 Sep 2023

Enhancing Cybersecurity in Agriculture 4.0: A Novel Intrusion Detection System Using Prairie Dog Optimization

Researchers have introduced an innovative Intrusion Detection System (IDS) model, IDSNet-PDO, built on one-dimensional convolutional neural networks (1D-CNN) and fine-tuned with the Prairie Dog Optimization (PDO) algorithm. This IDS model demonstrates high accuracy in predicting Distributed Denial of Service (DDoS) attacks in the context of Agriculture 4.0, addressing cybersecurity challenges in interconnected IoT devices used in modern agriculture.

19 Sep 2023

Bridging Nature and Technology: The Synergistic Future of AI and Ecology

This study advocates for a closer collaboration between artificial intelligence (AI) and ecological research to address pressing challenges such as climate change. The authors highlight the potential for AI to learn from ecological systems and propose a convergence that can lead to groundbreaking discoveries and more resilient AI systems.

14 Sep 2023

AI's Green Revolution: Reducing Carbon Emissions in Manufacturing

This study delves into the significant impact of artificial intelligence (AI) on reducing carbon emissions in the manufacturing sector. The research explores the correlation between AI adoption and carbon intensity, highlighting the role of green technological, management, and product innovation in strengthening AI's carbon reduction effect.

13 Sep 2023

AI-Powered Sustainability in Côte d'Ivoire's Agriculture

This research highlights the use of AI and open-source tools to address climate change challenges in Côte d'Ivoire's agriculture. It introduces AI models for cocoa plant health monitoring and water resource forecasting, emphasizing their potential in promoting sustainable practices and climate-resilient decision-making for farmers and policymakers.

6 Sep 2023

Harnessing Machine Learning for Drought-Resistant Crops

Researchers explore the power of machine learning models to predict effective microbial strains for combatting drought's impact on crop production. By comparing various models, the study reveals that gradient boosted trees (GBTs) offer high accuracy, though considerations of computational resources and application needs are vital when choosing a model for real-world implementation.

21 Aug 2023

Enhancing Winter Wheat Water Management with Machine Learning

Researchers devise interpretable and non-interpretable ML models optimized by particle swarm optimization to accurately estimate crop evapotranspiration for winter wheat. By utilizing limited meteorological data, these models offer insights into water usage and agricultural sustainability, aiding water management practices in the face of climate challenges.

17 Aug 2023