A novel machine learning approach for assessing water quality using Daphnia swimming behavior

Increasing rate of water pollution and consequently waterborne diseases are the engrossing evidence towards danger to living organisms. It becomes a great challenge these days to preserve our flora and fauna by controlling various unexpected pollution activities. Although the invention of many schemes and programmes regarding water purification has done a tremendous job, but still there is something that has been lagging. With increase in population, industrialization and global warming situation is getting worse day by day. It becomes very difficult to get safe drinking water and appropriate quality water for other domestic usage and agriculture purpose. Major reasons for water pollution include undesirable increase in impurities. These may cause eutrophication of the water body, change in taste, discolouration and odour of water, water borne diseases, increase in water toxic nature etc. For water to be serviceable it should be aesthetically acceptable, chemically safe, bacteria free; organic substances and radioactive elements should be absent. So, there is an urgent need to look into this situation and take the corrective and necessary actions to overcome this situation. The government is paying an attention to this problem and finding the ways to control the situation. However, major areas are not developed to the point and water quality estimation is totally dependent upon sampling at location and testing in laboratories. Manual sampling and measurements are prone to human errors and these techniques may create ambiguities in predicted output. In this paper we have presented Machine Learning (ML) approach for calculating the Water Quality Index (WQI) and classification of water quality to estimate water characteristics for usage. For analysis, decision tree method is used to estimate water quality information. The standard values of parameters are selected as per guidelines provided by World Health organization (WHO). Results calculated using ML techniques showed prominent accuracy over traditional methods. Accuracy achieved is also significant, i.e. 98 %. Likewise, projection of gathered data was done utilizing web interface and web app to alert the authorities about contamination.

Optimization of water quality index models using machine learning approaches.

An improved machine learning approach for predicting granular flows

The actuality of this article is in the implementation of a systematic approach to the study of natural water quality of the river Lopan (within Kharkiv region).Assessment of water quality in the rivers has been studied by such scholars as O.O. Alexin, A.M. Gorev, V.M. Zhukynsky, F.F. Kirkov, A.M. Nikanorov, A.V. Ogievsky, O.P. Oksijuk, N.P. Puzyrevsky, V.D.Romanenko, V.K. Khilchevsky, A.P. Yatsyk, et al. But they all studied mainly large river basins, and we propose to investigate changes in the chemical composition of an average river that flows in the industrialized region.The research has been conducted on the methodology of environmental assessment of surface water quality according to the respective categories, in three blocks: salt, trophy-saprobiological, and the block of specific toxic action substances.The results of the research have shown that according to the salt block water in the river is saline; according to the trophy-saprobiological block water in the rivers is the most heavily polluted with phosphate phosphorus, which often leads to significant eutrophication of the reservoirs, nitrite and nitrate nitrogen, low water clarity; according to the block of specific substances – with phenols; according to the environmental index surface water quality of the river Lopan virtually did not change during 1980-2014, 2-3 grade (water is quite clean, slightly contaminated), but in recent years there has been no improvement in water quality of the river.In previous years industry was the main source of water pollution of the river Lopan, but in recent years it is municipal services, industrial enterprises and agriculture. The river Lopan was the most polluted in 1990, the least - in 2010. The biggest pollutants in the river Lopan were nitrite nitrogen, nitrate nitrogen, phosphorus and phosphate phenols.The actuality of this article is in the implementation of a systematic approach to the study of natural water quality of the river Lopan (within Kharkiv region).Assessment of water quality in the rivers has been studied by such scholars as O.O. Alexin, A.M. Gorev, V.M. Zhukynsky, F.F. Kirkov, A.M. Nikanorov, A.V. Ogievsky, O.P. Oksijuk, N.P. Puzyrevsky, V.D.Romanenko, V.K. Khilchevsky, A.P. Yatsyk, et al. But they all studied mainly large river basins, and we propose to investigate changes in the chemical composition of an average river that flows in the industrialized region. The research has been conducted on the methodology of environmental assessment of surface water quality according to the respective categories, in three blocks: salt, trophy-saprobiological, and the block of specific toxic action substances.The results of the research have shown that according to the salt block water in the river is saline; according to the trophy-saprobiological block water in the rivers is the most heavily polluted with phosphate phosphorus, which often leads to significant eutrophication of the reservoirs, nitrite and nitrate nitrogen, low water clarity; according to the block of specific substances – with phenols; according to the environmental index surface water quality of the river Lopan virtually did not change during 1980-2014, 2-3 grade (water is quite clean, slightly contaminated), but in recent years there has been no improvement in water quality of the river. In previous years industry was the main source of water pollution of the river Lopan, but in recent years it is municipal services, industrial enterprises and agriculture. The river Lopan was the most polluted in 1990, the least - in 2010. The biggest pollutants in the river Lopan were nitrite nitrogen, nitrate nitrogen, phosphorus and phosphate phenols.

Assessing the algal population dynamics using multiple machine learning approaches: Application to Macao reservoirs

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.

Optimized machine learning approaches for identifying vertical temperature gradient on ballastless track in natural environments

Do Machine Learning Approaches Perform Better Than Regression Models in Mapping Studies? A Systematic Review.

Machine learning (ML) approaches have emerged as promising tools for improving seizure-onset zone (SOZ) prediction in patients with drug-resistant epilepsy (DRE). This systematic review aimed to evaluate the application and performance of ML approaches for SOZ prediction in patients with DRE. A comprehensive search was conducted across PubMed/MEDLINE, the Cochrane Database of Systematic Reviews, and Epistemonikos databases for studies employing ML algorithms for SOZ prediction in patients with DRE. The Quality Assessment of Diagnostic Accuracy Studies version 2 (QUADAS-2) tool was adopted to assess the methodological quality and risk of bias of included studies. Data on patient demographics, data acquisition methods, ML algorithms, and performance metrics were extracted and systematically synthesized. Out of a total of 38 studies, 15 studies met the inclusion criteria, encompassing 352 patients (mean age: 28 years, 34% female population). The studies employed various ML techniques, including traditional methods such as support vector machines and advanced deep learning architectures. Performance metrics varied widely across studies, with some approaches achieving accuracy, sensitivity, and specificity values above 90%. Deep learning models generally outperformed traditional methods, particularly in handling complex, multimodal data. Notably, personalized models demonstrated superior performance in reducing localization error and spatial dispersion. However, heterogeneity in data acquisition methods, patient populations, and reporting standards complicated direct comparisons between studies. This review highlighted the potential of ML approaches, particularly deep learning and personalized models, to enhance SOZ prediction accuracy in patients with DRE. However, several challenges were identified, including the need for standardized data collection protocols, larger prospective studies, and improved model interpretability. The findings underscore the importance of considering network-level changes in epilepsy when developing ML models for SOZ prediction. Although ML approaches show promise for improving surgical planning and outcomes in DRE, their clinical utility, particularly in complex epilepsy cases, requires further investigation. Addressing these challenges will be crucial in realizing the full potential of ML in enhancing epilepsy care.

<p>The advancement of big data and increased computational power have contributed to an increased use of Machine Learning (ML) approaches in hydrological modelling. These approaches are powerful tools for modeling non-linear systems. However, the applicability of ML in non-stationary conditions needs to be studied further. As climate change will change hydrological patterns, testing ML approaches for non-stationary conditions is essential. Here, we used the Differential Split-Sample Test (DSST) to test the climate transposability of ML approaches (e.g., calibrating in a wet period and validating in a dry one, and vice-versa).  We applied five ML approaches using daily precipitation and temperature as input for the prediction of the daily discharge in six snow-dominated Swiss catchments. Lower and upper benchmarks were used to evaluate performances through a relative performance measure. The lower benchmark is the average of the bucket-type HBV model runs from 1000 random parameter sets. The upper benchmark is the automatically calibrated HBV model. In comparison with the stationary condition, the models performed slightly poorer in the non-stationary condition. The performance of simple ML approaches was poor for non-stationary conditions with an underestimation of peak flows, as well as a poor representation of the snow-melting period. On the other hand, a more complex ML approach (deep learning), the Long Short -Term Memory (LSTM), showed a good performance when compared with the lower and upper benchmarks. This might be explained by the fact that the so-called memory cell allowed to simulate the storage effects. </p>

Background: This paper attempts to identify suitable Machine Learning (ML) approach for image denoising of radiology based medical application. The Identification of ML approach is based on (i) Review of ML approach for denoising (ii) Review of suitable Medical Denoising approach.Discussion: The review focuses on six application of radiology: Medical Ultrasound (US) for fetus development, US Computer Aided Diagnosis (CAD) and detection for breast, skin lesions, brain tumor MRI diagnosis, X-Ray for chest analysis, Breast cancer using MRI imaging. This survey identifies the ML approach with better accuracy for medical diagnosis by radiologists. The image denoising approaches further includes basic filtering techniques, wavelet medical denoising, curvelet and optimization techniques. In most of the applications, the machine learning performance is better than the conventional image denoising techniques. For fast and computational results the radiologists are using the machine learning methods on MRI, US, X-Ray and Skin lesion images. The characteristics and contributions of different ML approaches are considered in this paper.Conclusion: The problem faced by the researchers during image denoising techniques and machine learning applications for clinical settings have also been discussed.

Recently, global climate change discussions have become more prominent, and forests are considered as the ecosystems most at risk by the consequences of climate change. Wildfires are among one of the main drivers leading to losses in forested areas. The increasing availability of free remotely sensed data has enabled the precise locations of wildfires to be reliably monitored. A wildfire data inventory was created by integrating global positioning system (GPS) polygons with data collected from the moderate resolution imaging spectroradiometer (MODIS) thermal anomalies product between 2012 and 2017 for Amol County, northern Iran. The GPS polygon dataset from the state wildlife organization was gathered through extensive field surveys. The integrated inventory dataset, along with sixteen conditioning factors (topographic, meteorological, vegetation, anthropological, and hydrological factors), was used to evaluate the potential of different machine learning (ML) approaches for the spatial prediction of wildfire susceptibility. The applied ML approaches included an artificial neural network (ANN), support vector machines (SVM), and random forest (RF). All ML approaches were trained using 75% of the wildfire inventory dataset and tested using the remaining 25% of the dataset in the four-fold cross-validation (CV) procedure. The CV method is used for dealing with the randomness effects of the training and testing dataset selection on the performance of applied ML approaches. To validate the resulting wildfire susceptibility maps based on three different ML approaches and four different folds of inventory datasets, the true positive and false positive rates were calculated. In the following, the accuracy of each of the twelve resulting maps was assessed through the receiver operating characteristics (ROC) curve. The resulting CV accuracies were 74%, 79% and 88% for the ANN, SVM and RF, respectively.

A comprehensive comparison of machine learning approaches with hyper-parameter tuning for smartphone sensor-based human activity recognition

Accurate water quality assessment is critical for sustainable water resources management under growing environmental pressures. The Water Quality Index (WQI) provides a practical framework for summarizing complex water quality data into a single indicator. This review examines recent advances in artificial intelligence and optimization techniques for WQI prediction, with a focus on machine learning, ensemble models, deep learning, and hybrid approaches. Existing studies demonstrate strong predictive capabilities but remain largely model-centric and limited by localized datasets and weak system integration. This review identifies methodological limitations and outlines key components required for future integrated monitoring frameworks, including data acquisition, model interpretability, and uncertainty-aware decision support. The findings provide guidance for advancing toward scalable and transparent water quality assessment systems.

As pharmaceutical development moves from early-stage in vitro experimentation to later in vivo and subsequent clinical trials, data and knowledge are acquired across multiple time and length scales, from the subcellular to whole patient cohort scale. Realizing the potential of this data for informing decision making in pharmaceutical development requires the individual and combined application of machine learning (ML) and mechanistic multiscale mathematical modeling approaches. Here we outline how these two approaches, both individually and in tandem, can be applied at different stages of the drug discovery and development pipeline to inform decision making compound development. The importance of discerning between knowledge and data are highlighted in informing the initial use of ML or mechanistic quantitative systems pharmacology (QSP) models. We discuss the application of sensitivity and structural identifiability analyses of QSP models in informing future experimental studies to which ML may be applied, as well as how ML approaches can be used to inform mechanistic model development. Relevant literature studies are highlighted and we close by discussing caveats regarding the application of each approach in an age of constant data acquisition. SIGNIFICANCE STATEMENT: We consider when best to apply machine learning (ML) and mechanistic quantitative systems pharmacology (QSP) approaches in the context of the drug discovery and development pipeline. We discuss the importance of prior knowledge and data available for the system of interest and how this informs the individual and combined application of ML and QSP approaches at each stage of the pipeline.