Non-optically active water quality parameters prediction by using remote sensing and machine learning

Surface water pollution become a nuisance for humankind as river water fulfill requirement of a major population and traditional method of water quality assessment and evaluation is inadequate in this era. So using advance method of machine learning in prediction of surface water proves to be helpful to prevent future water accident. As we seen many recent studies of water quality prediction and river water assessment using machine learning approach for better accuracy and less labor and to optimize its overall results. It’s become essential to review the recent studies which used Machine Learning algorithms for prediction, analysis, evaluation and assessment of river water quality and different models used in these studies for different environmental conditions. Machine learning models are superior to handle such complex and non linear data such as water quality parameters with greater accuracy, reliability, cost-effectiveness and efficiency as considered as great tool for surface Water Quality monitoring, prediction, future projects and help lawmakers in policy. In this report we reviewed around 17 research papers which uses machine learning approach from different journal and concise it to covers the structure of study, datasets used, methodology analysis, models performance, environment susceptibility, comparative analysis and assessments of Machine Learning models progress in river water quality research. For better management and control of surface water quality and its treatment, this study will help in understanding and analyzing the studies reviewed in this paper and its future application. We can conclude that research on Water Quality prediction using Machine Learning model are inadequate in the context future vulnerability, observing increasing pollution in recent years we require more research in this field. Finally, this study provides breakthrough in Surface Water Engineering and Management to give a new direction to fore coming studies and fortified it scope also gives a comparative approach for its implementation in new studies.

Machine learning-based water quality prediction using octennial in-situ Daphnia magna biological early warning system data

The global decline of water quality in rivers and streams has resulted in a pressing need to design new watershed management strategies. Water quality can be affected by multiple stressors including population growth, land use change, global warming, and extreme events, with repercussions on human and ecosystem health. A scientific understanding of factors affecting riverine water quality and predictions at local to regional scales, and at sub‐daily to decadal timescales are needed for optimal management of watersheds and river basins. Here, we discuss how machine learning (ML) can enable development of more accurate, computationally tractable, and scalable models for analysis and predictions of river water quality. We review relevant state‐of‐the art applications of ML for water quality models and discuss opportunities to improve the use of ML with emerging computational and mathematical methods for model selection, hyperparameter optimization, incorporating process knowledge into ML models, improving explainablity, uncertainty quantification, and model‐data integration. We then present considerations for using ML to address water quality problems given their scale and complexity, available data and computational resources, and stakeholder needs. When combined with decades of process understanding, interdisciplinary advances in knowledge‐guided ML, information theory, data integration, and analytics can help address fundamental science questions and enable decision‐relevant predictions of riverine water quality.

Optimisation and interpretation of machine and deep learning models for improved water quality management in Lake Loktak

For effective management of water quantity and quality, it is absolutely essential to estimate the pollution level of the existing surface water. This case study aims to evaluate the performance of twelve machine learning (ML) models, including five boosting-based algorithms (adaptive boosting, gradient boosting, histogram-based gradient boosting, light gradient boosting, and extreme gradient boosting), three decision tree-based algorithms (decision tree, extra trees, and random forest), and four ANN-based algorithms (multilayer perceptron, radial basis function, deep feed-forward neural network, and convolutional neural network), in estimating the surface water quality of the La Buong River in Vietnam. Water quality data at four monitoring stations alongside the La Buong River for the period 2010–2017 were utilized to calculate the water quality index (WQI). Prediction performance of the ML models was evaluated by using two efficiency statistics (i.e., R2 and RMSE). The results indicated that all twelve ML models have good performance in predicting the WQI but that extreme gradient boosting (XGBoost) has the best performance with the highest accuracy (R2 = 0.989 and RMSE = 0.107). The findings strengthen the argument that ML models, especially XGBoost, may be employed for WQI prediction with a high level of accuracy, which will further improve water quality management.

High concentrations of chlorophyll-a (Chl-a) in aquatic systems pose serious environmental and public health concerns. Chl-a, a primary marker of phytoplankton biomass, is often associated with the proliferation of harmful algal blooms (HABs). These blooms produce toxins that not only threaten marine organisms but also have far-reaching impacts on human health and aquatic ecosystems. These toxins can degrade water quality, disrupt food webs, and result in significant fish mortality. When these harmful substances contaminate drinking water sources, they can cause a range of health problems, from short-term illnesses to chronic diseases.Despite the importance of predicting Chl-a levels, earlier research has largely focused on water quality parameters without adequately considering the dynamic nature of river hydrology. This study bridges that gap by leveraging satellite data to enhance predictive accuracy. Sentinel-2 imagery was utilized to monitor water quality, while Sentinel-1 data captured the hydrological characteristics of rivers. To forecast Chl-a, four machine learning models were deployed, with their performance evaluated through Nash-Sutcliffe Efficiency (NSE) and Root Mean Square Error (RMSE) metrics. Additionally, the study used Shapley Additive Explanations (SHAP) to unravel the contribution of individual water quality variables and satellite-derived data to the prediction process.By integrating hydrological factors with water quality predictions, this research provides a more holistic understanding of river systems. Such insights are vital for optimizing the operation of water management structures like dams and weirs. Moreover, the incorporation of retention time analysis offers a proactive approach to monitoring and preventing HABs, enabling more effective management of aquatic ecosystems under varying environmental conditions worldwide.

Access to clean drinking water is crucial for any region’s social and economic growth. However, rapid urbanization and industrialization have significantly deteriorated water quality, posing severe pollution threats from domestic, agricultural, and industrial sources. This study presents an innovative framework for assessing water quality in North Bhubaneswar, integrating the Water Quality Index (WQI) with statistical analysis, geospatial technologies, and machine learning models. The WQI, calculated using the Weighted Arithmetic Index method, provides a single composite value representing overall water quality based on several key physicochemical parameters. To evaluate potable water quality across 21 wards in the northern zone, several key parameters were monitored, including pH, electrical conductivity (EC), dissolved oxygen (DO), hardness, chloride, total dissolved solids (TDSs), and biochemical oxygen demand (BOD). The Weighted Arithmetic WQI method was employed to determine overall water quality, which ranged from excellent to good. Furthermore, Principal Component Analysis (PCA) revealed a strong positive correlation (r > 0.6) between pH, conductivity, hardness, and alkalinity. To enhance the accuracy and reliability of water quality assessment, multiple machine learning models Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naïve Bayes (NB) were applied to classify water quality based on these parameters. Among them, the Decision Tree (DT) and Random Forest (RF) models demonstrated the highest precision (91.8% and 92.7%, respectively) and overall accuracy (91.7%), making them the most effective in predicting water quality and integrating WQI, machine learning, and statistics to analyze water quality. The study emphasizes the importance of continuous water quality monitoring and offers data-driven recommendations to ensure sustainable access to clean drinking water in North Bhubaneswar.

With the fast growth of aquatic data, machine learning is essential for data analysis, categorization, and prediction. Data-driven models using machine learning may effectively handle complicated nonlinear problems in water research, unlike conventional approaches. Machine learning models and findings have been used to build, monitor, simulate, evaluate, and optimize water treatment and management systems in water environment research. Machine learning may also enhance water quality, pollution control, and watershed ecosystem security. This chapter discusses how ML approaches were used to assess water quality in surface, ground, drinking, sewage, and ocean. The authors also suggest potential machine learning applications in aquatic situations.

With the fast growth of aquatic data, machine learning is essential for data analysis, categorization, and prediction. Data-driven models using machine learning may effectively handle complicated nonlinear problems in water research, unlike conventional approaches. Machine learning models and findings have been used to build, monitor, simulate, evaluate, and optimize water treatment and management systems in water environment research. Machine learning may also enhance water quality, pollution control, and watershed ecosystem security. This chapter discusses how ML approaches were used to assess water quality in surface, ground, drinking, sewage, and ocean water. The authors also suggest potential machine learning applications in aquatic situations.

PD27-01 DEVELOPMENT OF A MACHINE LEARNING (ML) MODEL TO AUTOMATICALLY AND PRECISELY IDENTIFY KIDNEY STONES FROM URETEROSCOPY VIDEO RECORDINGS

Water quality prediction based on sparse dataset using enhanced machine learning

The prediction and management of water quality are critical to ensure sustainable water resources, particularly in regions like Malaysia, where rivers face increasing pollution from industrialisation, agriculture, and urban expansion. This review aims to provide a comprehensive analysis of machine learning (ML) models and statistical methods applied in forecasting and classification of water quality. A particular focus is given to hybrid models that integrate multiple approaches to improve predictive accuracy and robustness. This study also reviews water quality standards and highlights the environmental context that necessitates advanced predictive tools. Statistical techniques such as residual analysis, principal component analysis (PCA), and feature importance assessment are also explored to enhance model interpretability and reliability. Comparative tables of model performance, strengths, and limitations are presented alongside real-world applications. Despite recent advancements, challenges remain in data quality, model interpretability, and integration of spatio-temporal and fuzzy logic techniques. This review identifies key research gaps and proposes future directions for developing transparent, adaptive, and accurate models. The findings can also guide researchers and policymakers towards the development of smart water quality management systems that enhance decision-making and ecological sustainability.

Introduction: Management of non-small cell lung cancer (NSCLC) depends on the presence and site of distant metastasis. Machine learning (ML) models have shown promise in analyzing clinical and molecular data to predict cancer outcomes and prognosis. Advancements in single-cell RNA sequencing (scRNA) provide deeper understanding of the tumor microenvironment (TME) and in metastasis. We aim to analyze NSCLC metastasis across different organs, investigate the clinical and molecular predictors of distant organ metastasis, and the single-cell landscape driving tumor metastasis. Methods: We used the cBioportal and the Gene Expression Omnibus (GEO) databases to retrieve clinical and scRNA data from the MSKCC Metastasis Cohort and GSE131907. The MSKCC dataset included 4, 601 NSCLC patients with distant organ metastasis to adrenal, bone, CNS/brain, liver, lung, mediastinum, and pleura. Seven random forest classifiers (RFC) were built for each organ metastasis and trained on clinical and genomic features like age, sex, race, histology, overall survival, fraction genome altered (FGA), microsatellite instability (MSI), mutation count, tumor mutational burden (TMB), and tumor purity. Models were evaluated using area under the receiver operating characteristics curve (AUC/ROC), 10-fold cross-validation (CV), accuracy score, and feature importance. Single-cell RNA data was filtered to include primary tumor samples (n=65, 702) and brain metastasis (n=28, 429) for cell distribution analysis. Results: Distant metastasis occurred in 3, 784 (82%) patients. The most common metastatic sites were lung (41%), bone (38%), pleura (35%), CNS (26%), liver (18%), adrenal gland (12%), and mediastinum (8.7%). The RFC-Mediastinum model showed the highest accuracy (0.89), AUC (0.61), and 10-fold CV (0.90), followed by RFC-Adrenal (0.86, AUC=0.62, 10-fold CV=0.86). The lowest performance was seen in RFC-Lung (0.57, AUC=0.56, 10-fold CV=0.52). The most important predictive features across all models were FGA, MSI, TMB, and mutation count, while histology was the least important. Single-cell analysis revealed that brain metastasis had a predominance of stromal cells, particularly epithelial, compared to primary tumor samples. Primary tumor samples consisted mostly of CD8+ T-cells and macrophages. COL1A1/2 genes were significantly expressed in brain metastatic stromal cells, suggesting extracellular matrix remodeling in the TME. Conclusion: Our study highlights the potential of ML models in accurately predicting distant organ metastasis in NSCLC. Variations in TME cellular compositions may be driven by complex intercellular communications in metastatic disease. Citation Format: Zaid Nassar Abu Rjai’, Ahmad Majid Bani-Ata, Ayah N. AL-Bzour. Machine learning prediction and single-cell landscape of distant organ metastasis in non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5086.

A critical consequence of agriculturally managed lands is the transport of nutrients and sediment to fresh water systems, which is ultimately responsible for a range of adverse impacts on human and environmental health. In the U.S. alone, over half of streams and rivers are classified as impaired, with agriculture as the primary contributor. To address deterioration of water quality, there is a need for reliable tools and mathematical models to monitor and predict impacts to water quantity and quality. Soil water content is a key variable in representing environmental systems, linking and driving hydrologic, climate, and biogeochemical cycles; however, the influence of soil water simulations on model predictions is not well characterized, particularly for water quality. Moreover, while soil moisture estimation is the focus of multiple remote sensing missions, defining its potential for use in water quality models remains an open question. The goal of this research is to test whether updating model soil water process representation or model soil water estimates can provide better overall predictive confidence in estimates of both soil moisture and water quality. A widely-used ecohydrologic model, the Soil and Water Assessment Tool (SWAT), was used to evaluate four objectives: 1) investigate the potential of a gridded version of the SWAT model for use with similarly gridded, remote sensing data products, 2) determine the sensitivity of model predictions to changes in soil water content, 3) implement and test a more physically representative soil water percolation algorithm, and 4) perform practical data assimilation experiments using remote sensing data products, focusing on the effects of soil water updates on water quality predictions. With the exception of the first objective, model source code was modified to investigate the relative influence and effect of soil water on overall model predictions. Results suggested that use of the SWAT grid model was currently not viable given practical computational constraints. While the advantages provided by the gridded approach are likely useful for small scale watersheds (< 500 km2), the spatial resolution necessary to run the simulation was too coarse, such that many of the benefits of the gridded approach are negated. Sensitivity tests demonstrated a strong response of model predictions to perturbations in soil moisture. Effects were highly process dependent, where water quality was particularly sensitive to changes in both transport and transformation processes. Model response was reliant upon a default thresholding behavior that restricts subsurface flow and redistribution processes below field capacity. An alternative approach that removed this threshold and keyed processes to relative saturation showed improvement by allowing a more realistic range of soil moisture and a reduction of flushing behavior. This approach was further extended to test against baseline satellite data assimilation experiments; however, did not conclusively outperform the original model simulations. Nevertheless, overall, data assimilation experiments using a remote sensing surface soil moisture data product from the NASA Soil Moisture Active/Passive (SMAP) mission were able to correct for a dry bias in the model simulations and reduce error. Data assimilation updates significantly impacted flow predictions, generally by increasing the dominant contributing flow process. This led to substantial differences between two test sites, where landscape and seasonal characteristics moderated the impact of data assimilation updates to hydrologic, water quality, and crop yield predictions. While the findings illustrate the potential to improve predictions, continued future efforts to refine soil water process representation and optimize data assimilation with longer time series are needed. The dependence of ecohydrologic model predictions on soil moisture highlighted by this research underscores the importance and challenge of effectively representing a complex, physically-based process. As essential decision support systems rely on modeling analyses, improving prediction accuracy is vital.

This study delves into the environmental impact of inland aquaculture on estuarine ecosystems by examining the water quality of four estuarine streams within the key inland aquaculture zone of South India. In this region, extensive and intensive aquaculture practices are common, posing potential challenges to estuarine health. The research explores the predictive capabilities of the Gaussian elimination method (GEM) and machine learning techniques, specifically multi-linear regression (MLR) and support vector regressor (SVR), in forecasting the water quality index of these streams. Through comprehensive evaluation using performance metrics such as coefficient of determination (R2) and average mean absolute percentage error (MAPE), MLR and SVR demonstrate higher prediction efficiency. Notably, employing key water parameters as inputs in machine learning models is also more effective. Biochemical oxygen demand (BOD) emerges as a critical water parameter, identified by both MLR and SVR, exhibiting high specificity in predicting water quality. This suggests that MLR and SVR, incorporating key water parameters, should be prioritized for future water quality monitoring in intensive aquaculture zones, facilitating timely warnings and interventions to safeguard water quality.