Soil salinization poses a significant threat to both ecosystems and agriculture because it limits plants' ability to absorb water and, in doing so, reduces crop productivity. This phenomenon alters the soil's spectral properties, creating a measurable relationship between salinity and light reflectance that enables remote monitoring. While laboratory spectroscopy provides precise measurements, its reliance on in-situ sampling limits scalability to regional or global levels. Conversely, hyperspectral satellite imagery enables wide-area observation but lacks the fine-grained interpretability of laboratory instruments. To bridge this gap, we introduce DeepSalt, a deep-learning-based spectral transfer framework that leverages knowledge distillation and a novel Spectral Adaptation Unit to transfer high-resolution spectral insights from laboratory-based spectroscopy to satellite-based hyperspectral sensing. Our approach eliminates the need for extensive ground sampling while enabling accurate, large-scale salinity estimation, as demonstrated through comprehensive empirical benchmarks. DeepSalt achieves significant performance gains over methods without explicit domain adaptation, underscoring the impact of the proposed Spectral Adaptation Unit and the knowledge distillation strategy. The model also effectively generalized to unseen geographic regions, explaining a substantial portion of the salinity variance.

Accurate ocean forecasting is essential for supporting a wide range of marine applications. Recent advances in artificial intelligence have highlighted the potential of data-driven models to outperform traditional numerical approaches, particularly in atmospheric weather forecasting. However, extending these methods to ocean systems remains challenging due to their inherently slower dynamics and complex boundary conditions. In this work, we present MedFormer, a fully data-driven deep learning model specifically designed for medium-range ocean forecasting in the Mediterranean Sea. MedFormer is based on a U-Net architecture augmented with 3D attention mechanisms and operates at a high horizontal resolution of 1/24°. The model is trained on 20 years of daily ocean reanalysis data and fine-tuned with high-resolution operational analyses. It generates 9-day forecasts using an autoregressive strategy. The model leverages both historical ocean states and atmospheric forcings, making it well-suited for operational use. We benchmark MedFormer against the state-of-the-art Mediterranean Forecasting System (MedFS), developed at Euro-Mediterranean Center on Climate Change (CMCC), using both analysis data and independent observations. The forecast skills, evaluated with the Root Mean Squared Difference and the Anomaly Correlation Coefficient, indicate that MedFormer consistently outperforms MedFS across key 3D ocean variables. These findings underscore the potential of data-driven approaches like MedFormer to complement, or even surpass, traditional numerical ocean forecasting systems in both accuracy and computational efficiency.

Ocean salinity plays a vital role in circulation, climate, and marine ecosystems, yet its measurement is often sparse, irregular, and noisy, especially in drifter-based datasets. Traditional approaches, such as remote sensing and optimal interpolation, rely on linearity and stationarity, and are limited by cloud cover, sensor drift, and low satellite revisit rates. While machine learning models offer flexibility, they often fail under severe sparsity and lack principled ways to incorporate physical covariates without specialized sensors. In this paper, we introduce the OceAn Salinity Imputation System (OASIS), a novel diffusion adversarial framework designed to address these challenges.

Ocean forecasting is crucial for both scientific research and societal benefits. Currently, the most accurate forecasting systems are global ocean forecasting systems (GOFSs), which represent the ocean state variables (OSVs) as discrete grids and solve partial differential equations (PDEs) governing the transitions of oceanic state variables using numerical methods. However, GOFSs processes are computationally expensive and prone to cumulative errors. Recently, large artificial intelligence (AI)-based models significantly boosted forecasting speed and accuracy. Unfortunately, building a large AI ocean forecasting system that can be considered cross-spatiotemporal and air-sea coupled forecasts remains a significant challenge. Here, we introduce LangYa, a cross-spatiotemporal and air-sea coupled ocean forecasting system. Results demonstrate that the time embedding module in LangYa enables a single model to make forecasts with lead times ranging from 1 to 7 days. The air-sea coupled module effectively simulates air-sea interactions. The ocean self-attention module improves network stability and accelerates convergence during training, and the adaptive thermocline loss function improves the accuracy of thermocline forecasting. Compared to existing numerical and AI-based ocean forecasting systems, LangYa uses 27 years of global ocean data from the Global Ocean Reanalysis and Simulation version 12 (GLORYS12) for training and achieves more reliable deterministic forecasting results for OSVs. LangYa forecasting system provides global ocean researchers with access to a powerful software tool for accurate ocean forecasting and opens a new paradigm for ocean science.

AI emulators for forecasting have emerged as powerful tools that can outperform conventional numerical predictions. The next frontier is to build emulators for long climate simulations with skill across a range of spatiotemporal scales, a particularly important goal for the ocean. Our work builds a skillful global emulator of the ocean component of a state-of-the-art climate model. We emulate key ocean variables, sea surface height, horizontal velocities, temperature, and salinity, across their full depth. We use a modified ConvNeXt UNet architecture trained on multidepth levels of ocean data. We show that the ocean emulator - Samudra - which exhibits no drift relative to the truth, can reproduce the depth structure of ocean variables and their interannual variability. Samudra is stable for centuries and 150 times faster than the original ocean model. Samudra struggles to capture the correct magnitude of the forcing trends and simultaneously remains stable, requiring further work.

In the modern agricultural industry, technology plays a crucial role in the advancement of cultivation. To increase crop productivity, soil require some specific characteristics. For watermelon cultivation, soil needs to be sandy and of high temperature with proper irrigation. This research aims to design and implement an intelligent IoT-based soil characterization system for the watermelon field to measure the soil characteristics. IoT based developed system measures moisture, temperature, and pH of soil using different sensors, and the sensor data is uploaded to the cloud via Arduino and Raspberry Pi, from where users can obtain the data using mobile application and webpage developed for this system. To ensure the precision of the framework, this study includes the comparison between the readings of the soil parameters by the existing field soil meters, the values obtained from the sensors integrated IoT system, and data obtained from soil science laboratory. Excessive salinity in soil affects the watermelon yield. This paper proposes a model for the measurement of soil salinity based on soil resistivity. It establishes a relationship between soil salinity and soil resistivity from the data obtained in the laboratory using artificial neural network (ANN).

In this study, explainable machine learning techniques are applied to predict the toxicity of mussels in the Gulf of Trieste (Adriatic Sea) caused by harmful algal blooms. By analysing a newly created 28-year dataset containing records of toxic phytoplankton in mussel farming areas and toxin concentrations in mussels (Mytilus galloprovincialis), we train and evaluate the performance of ML models to accurately predict diarrhetic shellfish poisoning (DSP) events. The random forest model provided the best prediction of positive toxicity results based on the F1 score. Explainability methods such as permutation importance and SHAP identified key species (Dinophysis fortii and D. caudata) and environmental factors (salinity, river discharge and precipitation) as the best predictors of DSP outbreaks. These findings are important for improving early warning systems and supporting sustainable aquaculture practices.

Posidonia oceanica is a protected endemic seagrass of Mediterranean sea that fosters biodiversity, stores carbon, releases oxygen, and provides habitat to numerous sea organisms. Leveraging augmented research, we collected a comprehensive dataset of 174 features compiled from diverse data sources. Through machine learning analysis, we discovered the existence of a robust correlation between the exact location of P. oceanica and water biogeochemical properties. The model's feature importance, showed that carbon-related variables as net biomass production and downward surface mass flux of carbon dioxide have their values altered in the areas with P. oceanica, which in turn can be used for indirect location of P. oceanica meadows. The study provides the evidence of the plant's ability to exert a global impact on the environment and underscores the crucial role of this plant in sea ecosystems, emphasizing the need for its conservation and management.

Artificial Intelligence Technologies and Sustainability of Our Environment Taniya Basu Wed, 07/07/2021 – 21:01 Log in or register to post comments Introduction: In recent years, the environmental issues have triggered debates, discussions, awareness programs and public outrage that have catapulted interest in new technologies, such as Artificial Intelligence. Artificial Intelligence finds application in environmental sectors, including natural resource conservation, wildlife protection, energy management, clean energy, waste management, pollution control and agriculture. Advancement in the AI in environmental protection market could be one of the solutions to solve the major environmental concerns. The application of AI in environment protection includes machine learning for protecting the oceans, monitoring shipping, ocean mining, fishing, coral bleaching or the outbreak of marine disease. The AI techniques are quite beneficial for environmental analysis, as they are able to process a huge amount of data quickly so as to draw conclusions that may have not been possible by humans. The AI techniques are quite beneficial for environmental analysis, as they are able to process a huge amount of data quickly so as to draw conclusions that may have not been possible by humans.  1.Weather Forecasting & Climate Changes: The traditional models of weather forecasting are based on statistical measures of numeric models, and it does not give answers in binary. The data collected can be from deep space satellites, weather balloons, radar systems, nowcasting weather warnings and environmental analytics and sometimes from IoT based sensors.  The AI predictions are primarily based on machine learning algorithms. By processing more complex data in a shorter span of time using linear regression principles, now meteorologists can make predictions with improved accuracy and thus saves lives and money. Machine learning can abet with other forecasts as well, including temperature, wave height, and precipitation. Google’s AI forecast tool that is based on the UNET convolutional neural network (CNN) allows researchers to generate accurate rainfall predictions six hours ahead of when the precipitation occurs. CNN is a sequence of layers of mathematical operations arranged in an encoding phase. It takes the input satellite imagery and then transforms them into output images. 2.Climate Changes: For instance, we can halt emissions in the energy sector by using AI technology to forecast the supply and demand of power in the grid, improve the scheduling renewables, and reduce the life-cycle fossil fuel emissions through predictive maintenance. AI applications in transportation can enable more accurate traffic predictions, the development of freight transportation, and better modelling of demand and shared mobility option. Other kinds of impacts include the waste that is disrupting ecosystems, pollutants that affect human and animal health and biodiversity loss. By harnessing the swaths of data from sensors and satellites, we can better predict climate change impacts and proactively steward these ecosystems.  AI applied in food systems can help better monitor crop yields, reduce the need for chemicals and excess water through precision agriculture and minimize food waste through forecasting demand and identifying spoiled produce. Lastly, AI systems used in buildings and cities can help automatically control heating and cooling as well as model energy used to decide which buildings to retrofit.  3.Biodiversity and Conservation: With the recent development of AI-powered devices for the conservation of animals, we can now prevent wildlife extinction. After the extinction of western African rhinoceros, African elephants are next on the verge of going extinct due to the involvement of extensive poaching. The AI-based technology system uses a camera that detects poachers planning to attack an animal and subsequently generates an alert to the park rangers in real Plants are very beneficial for human lives and greatly help in fulfilling our necessities. They help fulfill our basic necessities as they can provide us with food, shelter, and medicine. The more the number of trees present in an environment, the greater is the amount of oxygen produced. The AI-based platform allows its users to click and share photos of various species of plants in real time. It also allows the other community members to identify the photos of the specific plant and confirm the plant’s presence, whether if such a plant already exists. In this way, the AI-based networking platform can help discover new species of plants worldwide. 4.Ocean Health: In a recent research by two AI algorithm— Latent Variable Gaussian Process (LVGP) model and Probabilistic Principal Component Analysis (PPCA) were used to understand the sonar echoes in the ocean. The research aimed at observing the changes that can happen with sonar echoes at different depths, salinity, and temperature. The algorithms were capable of classifying underwater environments from simulated sonar measurements with an average accuracy of more than 90%.  The application of artificial intelligence, ML algorithms, and smart robots seems to be the perfect combination in the future to come. Deep-sea mining and deep-sea research without disturbing the life beneath seem difficult a few years before, but not anymore. With the application of these latest technologies, oceanographers can create accurate cartography, understand the impact of climate change, species status, salinity, and gather a large amount of data to explore the areas left behind. Conclusion: Researchers and scientists must ensure that the data provided through Artificial Intelligence systems are transparent, fair and trustworthy. With an increasing demand of automation solutions and higher precision data-study for environment related problems and challenges, more multinational companies, educational institutions and government sectors need to fund more R&D of such technologies and provide proper standardizations for producing and applying them. In addition, there is a necessity to bring in more technologists and developers to this technology. Artificial intelligence is steadily becoming a part in our daily lives, and its impact can be seen through the advancements made in the field of environmental sciences and environmental management. Attachment AI IN ENVIRONMENT TECH.pdf Cover Image Image Publish Location Tech for Good

Coastal water quality management is a public health concern, as poor coastal water quality can harbor pathogens that are dangerous to human health. Tourism-oriented countries need to actively monitor the condition of coastal water at tourist popular sites during the summer season. In this study, routine monitoring data of $Escherichia\ Coli$ and enterococci across 15 public beaches in the city of Rijeka, Croatia, were used to build machine learning models for predicting their levels based on environmental parameters as well as to investigate their relationships with environmental stressors. Gradient Boosting (Catboost, Xgboost), Random Forests, Support Vector Regression and Artificial Neural Networks were trained with measurements from all sampling sites and used to predict $E.\ Coli$ and enterococci values based on environmental features. The evaluation of stability and generalizability with 10-fold cross validation analysis of the machine learning models, showed that the Catboost algorithm performed best with R$^2$ values of 0.71 and 0.68 for predicting $E.\ Coli$ and enterococci, respectively, compared to other evaluated ML algorithms including Xgboost, Random Forests, Support Vector Regression and Artificial Neural Networks. We also use the SHapley Additive exPlanations technique to identify and interpret which features have the most predictive power. The results show that site salinity measured is the most important feature for forecasting both $E.\ Coli$ and enterococci levels. Finally, the spatial and temporal accuracy of both ML models were examined at sites with the lowest coastal water quality. The spatial $E. Coli$ and enterococci models achieved strong R$^2$ values of 0.85 and 0.83, while the temporal models achieved R$^2$ values of 0.74 and 0.67. The temporal model also achieved moderate R$^2$ values of 0.44 and 0.46 at a site with high coastal water quality.