Proceedings MDPI’s Post

Alhamdulillah! I'm absolutely thrilled to share the wonderful news that our paper titled "𝐄𝐦𝐩𝐨𝐰𝐞𝐫𝐞𝐝 𝐦𝐚𝐜𝐡𝐢𝐧𝐞 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐚𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦 𝐭𝐨 𝐢𝐝𝐞𝐧𝐭𝐢𝐟𝐲 𝐬𝐮𝐬𝐭𝐚𝐢𝐧𝐚𝐛𝐥𝐞 𝐠𝐫𝐨𝐮𝐧𝐝𝐰𝐚𝐭𝐞𝐫 𝐩𝐨𝐭𝐞𝐧𝐭𝐢𝐚𝐥 𝐳𝐨𝐧𝐞 𝐦𝐚𝐩 𝐢𝐧 𝐉𝐚𝐬𝐡𝐨𝐫𝐞 𝐃𝐢𝐬𝐭𝐫𝐢𝐜𝐭, 𝐁𝐚𝐧𝐠𝐥𝐚𝐝𝐞𝐬𝐡" has been successfully published in the prestigious Journal, 𝐆𝐫𝐨𝐮𝐧𝐝𝐰𝐚𝐭𝐞𝐫 𝐟𝐨𝐫 𝐒𝐮𝐬𝐭𝐚𝐢𝐧𝐚𝐛𝐥𝐞 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 (𝑄𝟏, 𝐶𝑖𝑡𝑒 𝑆𝑐𝑜𝑟𝑒: 1𝟎.𝟒, 𝐼𝑚𝑝𝑎𝑐𝑡 𝐹𝑎𝑐𝑡𝑜𝑟: 𝟓.𝟗). I extend my heartfelt gratitude to Sujit Roy vaia for providing me this remarkable opportunity to contribute to the scientific and research community. Additionally, I would like to express my sincere appreciation to all the co-authors for their unwavering dedication and tireless efforts throughout this research journey. This study aimed to delineate groundwater prospect zones in Jashore, Bangladesh, employing 5 machine learning algorithms i.e Random Forest (RF) and Neural Networks (NNET), K-Nearest Neighbor (KNN), Gradient Boosting Machines (GBM), and Support Vector Machines (SVM). Through our rigorous analysis, we identified five distinct areas with varying levels of potential zones. It's noteworthy that RF and NNET models demonstrated superior accuracy compared to KNN, GBM, and SVM models. Moreover, our study utilized Explainable Artificial Intelligence (XAI), particularly leveraging the Partial Dependence Plots (PDP) technique, to comprehensively analyze the influence of various factors on groundwater potential. The implications of our findings are substantial for sustainable groundwater management, offering invaluable guidance to policymakers and water resource managers in Jashore. You can find the paper link below: (https://lnkd.in/g8b9hnmv) Once again, I express my gratitude to all involved in this endeavor and extend my best wishes for future and continued success in advancing sustainable water resource management practices. EID MUBARAK💥🎊🎉

🌊⚡ A novel approach in optimizing water and power networks A new paper titled “Conjunctive Optimal Operation of Water and Power Networks,” authored by Tomer Shmaya, Mashor Housh, Filippo Pecci, Joseph Kasprzyk, and Avi Ostfeld, has been published in the journal Heliyon. This research tackles the interdependencies between water distribution systems and power grids amidst increasing urbanization, climate change, and resource depletion. By presenting a novel mathematical formulation for conjunctive optimization, the study introduces a fresh approach to managing these vital infrastructure systems together. Key insights include: 🔗 A unique non-linear programming formulation for pump scheduling that avoids complex mixed-integer problems. 📉 Demonstrated cost reductions of over 10% in operational expenses through integrated management. 🌱 Enhanced understanding of the trade-offs between water and energy resources, paving the way for more sustainable infrastructure solutions. This work underscores the importance of holistic approaches in optimizing interconnected systems, highlighting that better management of water distribution systems and power grids could significantly improve resource efficiency and cost-effectiveness. Read the full study here: https://lnkd.in/eXJczFGW #WaterManagement #PowerGrids #Infrastructure #Sustainability #Optimization

🌍 New Research Alert! 🌍 I'm excited to share our latest publication titled "SERT: A Transformer-Based Model for Multivariate Temporal Sensor Data with Missing Values for Environmental Monitoring", now available in the *Computers & Geosciences* journal. Key Highlights: - **Environmental Monitoring:** Crucial for understanding climate change, biodiversity loss, and pollution. - **Innovative Models:** We propose two models - SERT (Spatio-temporal Encoder Representations from Transformers) and SST-ANN (Sparse Spatio-Temporal Artificial Neural Network). - **Handling Missing Data:** Our models handle missing data naturally without the need for imputation, ensuring more accurate and reliable forecasts. - **Superior Performance:** Extensive experiments demonstrate that our models outperform state-of-the-art methods in multivariate spatio-temporal forecasting. - **Interpretable Results:** SST-ANN provides interpretable results, aiding in decision-making and policy formulation. Why It Matters: Environmental monitoring often deals with large-scale spatio-temporal data from sensors and satellites, which can have missing values due to equipment faults. Our models not only forecast environmental parameters accurately but also provide insights into the factors driving these predictions. Access the Full Paper: The paper is open access and can be read in full here: https://lnkd.in/eq82xu5B Explore the Python Package: Our implementation is available as an open-source Python package. Check it out here: https://lnkd.in/e4umfupp This research represents a significant step forward in environmental monitoring and predictive modeling. I encourage you to read the full paper and explore how these models can be applied to various environmental monitoring challenges. #EnvironmentalMonitoring #MachineLearning #Transformers #DataScience #OpenAccess #Research Authors: - Amin S.Nejad, PhD - Rocío Alaiz-Rodríguez - Gerard McCarthy - Brian Kelleher - Anthony Grey - Andrew Parnell Affiliations: Hamilton Institute, Insight Centre for Data Analytics, Maynooth University, Ireland Department of Electrical, Systems and Automation, University of León, Spain ICARUS, Department of Geography, Maynooth University, Ireland Organic Geochemical Research Laboratory, Dublin City University, Ireland Feel free to reach out if you have any questions or need further information about our research. Let's make our planet a better place with advanced data-driven insights! 🌱🌎

"Spatiotemporal Data Augmentation of MODIS-Landsat Water Bodies Using Adversarial Networks"- Key Points:"-Remote sensing data augmentation for improving the accuracy of environmental assessments can be achieved using adversarial networks -High spatiotemporal resolution of water bodies data enhances the precision of their areal forecasting -Shape and areal accuracies play an important role for efficient spatiotemporal data interpolation" Abstract: "With increasing demands for precise water resource management, there is a growing need for advanced techniques in mapping water bodies. The currently deployed satellites provide complementary data that are either of high spatial or high temporal resolutions. As a result, there is a clear trade-off between space and time when considering a single data source. For the efficient monitoring of multiple environmental resources, various Earth science applications need data at high spatial and temporal resolutions. To address this need, many data fusion methods have been described in the literature, that rely on combining data snapshots from multiple sources. Traditional methods face limitations due to sensitivity to atmospheric disturbances and other environmental factors, resulting in noise, outliers, and missing data. This paper introduces Hydrological Generative Adversarial Network (Hydro-GAN), a novel machine learning-based method that utilizes modified GANs to enhance boundary accuracy when mapping low-resolution MODIS data to high-resolution Landsat-8 images. We propose a new non-saturating loss function for the Hydro-GAN generator, which maximizes the log of discriminator probabilities to promote stable updates and aid convergence. By focusing on reducing squared differences between real and synthetic images, our approach enhances training stability and overall performance. We specifically focus on mapping water bodies using MODIS and Landsat-8 imagery due to their relevance in water resource management tasks. Our experimental results demonstrate the effectiveness of Hydro-GAN in generating high-resolution water body maps, outperforming traditional methods in terms of boundary accuracy and overall quality." https://lnkd.in/eMTvXE6t

NEW PUBLICATION. This study proves that data generated from probabilistic-based finite-element models can be used for structural health monitoring and damage identification in the context of bridges. Thanks Mihai Bud, Mihai Nedelcu, Ionut Dragos Moldovan.

Good News!!!! Publication Alert!!!! Title: Nitrate concentrations tracking from multi-aquifer groundwater vulnerability zones: Insight from machine learning and spatial mapping Journal: Process Safety and Environmental Protection Impact Factor: 7.8 Citescore: 10.8 Overview Nitrate contamination in groundwater is a significant environmental concern that poses risks to human health and ecosystems. Several goals and targets of Sustainable Development Goals (SDGs) are related to water quality, pollution, and sustainable management of water resources, which can encompass nitrate contamination. This study conducted real fieldwork of groundwater samples at several locations in Al-Hassa, Saudi Arabia. Subsequently, experimental based on chromatography (IC) and inductively coupled plasma mass spectrometry (ICPMS) to analyze several groundwater hydro-geochemical elements. The study aimed to employ spatial mapping and advanced standalone optimization learning, including Elman Neural Network (ELNN), Gaussian Process Regression (GPR) models as nonparametric kernel-based probabilistic, and Random Forests (RFs) for tracking and modelling the nitrate (NO3) (mg/L) concentration. Paper Link: https://lnkd.in/dfeXJr8D Enjoy it.

"Two-stage meta-ensembling machine learning model for enhanced water quality forecasting" A personalized URL providing 50 days' free access to the article: https://lnkd.in/dg4nE4AB I am thrilled to share a significant milestone achieved by our research team, as our latest paper has been published in the prestigious Journal of Hydrology (Q1, H-index: 274, IF: 5.9). This paper introduces a novel approach to predicting water quality variables. In collaboration with my exceptional research assistant at the University of Tehran, Sepideh Heydari, we have developed a groundbreaking method for predicting chlorophyll-a (Chl-a) and dissolved oxygen (DO) concentrations using advanced machine learning (ML) techniques and optimization algorithms. Key Highlights: 1.    Dual Optimization Framework: We introduce a novel two-stage optimization framework that integrates two powerful algorithms, Grey Wolf Optimization (GWO) and Non-Dominated Sorting Genetic Algorithm II (NSGA-II). This dual optimization strategy significantly enhances the performance of ML models, demonstrating its effectiveness in addressing complex aquatic quality prediction challenges. 2.    Comprehensive Analysis: Our research offers a comprehensive comparative analysis of two distinct types of ML algorithms. Utilizing GWO for hyperparameter tuning, we identify Gradient Boosting Machine (GBM) variants as superior, offering valuable insights into optimizing diverse ML models for water quality (WQ) prediction in Small Prespa Lake, Greece. 3.    Innovative Hybrid Meta-Ensembling: We present an innovative hybrid meta-ensembling approach, where four complementary ML algorithms—MLP, XGB, LightGBM, and GBR—are optimized using GWO and further enhanced with the NSGA-II multi-objective optimization technique. 4.    Superior Performance: The GWO-optimized LightGBM model demonstrated superior performance for DO, rendering further NSGA-II optimization unnecessary. For Chl-a, the NSGA-II ensembling increased testing KGE by 7% compared to the best individual model. 5.    Rigorous Data Preprocessing: Our robust data preprocessing methodology, which includes lag time feature engineering and PCA dimensionality reduction, significantly improves model performance. This research marks a significant advancement in water resources management, offering a robust and cost-effective tool for sustainable planning of inland reservoirs. I am especially grateful to Ali Mohammadi for his support and to my esteemed colleague, Dr. Rahim Barzegar, for his invaluable guidance throughout the publishing process. #Research #WaterQuality #MachineLearning #Optimization #JournalOfHydrology #DataScience #EnvironmentalScience #UniversityOfTehran #WaterResources #Sustainability #ChlorophyllPrediction #DissolvedOxygen #MachineLearningModels #GreyWolfOptimization #NSGAII #LightGBM #GradientBoosting #PrespaLake #EnvironmentalResearch #AdvancedAnalytics #Hydrology #AI #ML

Exploring the Power of NetCDF for Scientific Data Management I'd like to introduce you to Network Common Data Form (NetCDF), a powerful set of software libraries and self-describing, machine-independent data formats that is transforming scientific data management and its growing applications in deep learning. Its hierarchical structure, featuring dimensions, variables, and attributes, provides a flexible framework for storing and organizing complex scientific datasets like meteorological data used in climate modeling, oceanographic data for studying marine ecosystems, and astronomical data from telescopes. NetCDF's self-describing nature ensures data comprehensibility over time, facilitating effortless data sharing and collaboration across diverse platforms. In deep learning, NetCDF's structured format and multidimensional array support have made it a popular choice for storing and processing large-scale observational datasets, particularly in Earth science and remote sensing. By offering a standardized representation of spatial and temporal data, NetCDF simplifies the development of deep learning models capable of extracting insights from these complex datasets. Libraries like TensorFlow and PyTorch seamlessly integrate with NetCDF, enabling researchers to easily load and preprocess data for training and evaluation. NetCDF's versatility extends beyond storage and organization, enabling efficient access to subsets of massive datasets, adapting to evolving research needs, and supporting long-term data archiving initiatives. Furthermore, NetCDF's concurrent read/write access capabilities empower collaborative workflows, allowing multiple users to seamlessly interact with and analyze the same dataset. Compared to other data formats like HDF5 or CSV, NetCDF strikes a balance between flexibility and ease of use. It's not the easiest thing to learn, and you definitely need some tech skills to use it properly, but its part of our fundamental toolkit for various research domains. How do you think that the Network Common Data Form address the challenges of managing complex, multi-dimensional scientific data, and how does its design contribute to its widespread adoption in diverse fields, including climate science, oceanography, astronomy, and deep learning? #NetCDF #DataFormats #DataScience #NetCDF #DataManagement #ScientificData #FAIRData #DataSharing #CollaborativeResearch #BigData #DataArchiving #ResearchTools #DataStorage #DataInteroperability #Tech #SoftwareEngineer

I am thrilled to share that our paper, "Application of Machine Learning Models for Improving Discharge Prediction in Ungauged Watershed: A Case Study in East DuPage, Illinois," has been published in Geographies. This research is a significant step forward in utilizing machine learning to enhance discharge prediction in ungauged watersheds, which is crucial for effective water resource management. It was a pleasure to collaborate with such a talented team of researchers. You can read the full paper here: https://lnkd.in/eNnAZ8QR #Research #MachineLearning #WaterManagement #EnvironmentalScience #Geographies Amin Asadollahi, Asyeh Sohrabifar, Binod Ale

Such an important contribution! Remote Sensing & Spatial Analytics's latest research reveals 92 oil spill incidents on our seashores from 2017 to 2023. A cumulative 1,166 sq. km of surface area has been covered in oil. This alarming data highlights the critical need to examine the #legal, #governance, and #policy aspects related to these incidents. Effective #EnvironmentalLegislation, robust governance frameworks, and comprehensive policies are essential to mitigate the socio-economic and #ecological impact of such spills, ensure #accountability, and protect our #marine #ecosystems for the future. #oceangovernance #SEZ #EEZ #ArabianSea #OilSpills #AIforEnvironment Many congratulations! Muhammad Adnan Siddique EHTASHAM NASEER M. Salman AbdulBasit Remote Sensing & Spatial Analytics Information Technology University