Application of machine learning algorithms in the investigation of groundwater quality parameters over YSR district, India

MOGARAJU, Jagadish Kumar

doi:10.31127/tuje.1032314

Cited by 6 publications

(2 citation statements)

References 30 publications

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“…ML techniques have been utilized for anomaly detection in water quality data in addition to prediction and categorization. In the work by [18], inconsistencies in water quality data gathered from a river in Pakistan were found using ML algorithms. The majority of the research points to ML algorithms as having enormous promise for monitoring water quality in agriculture.…”

Section: Review Of ML In Agricultural Qualitymentioning

confidence: 99%

An IoT and machine learning solutions for monitoring agricultural water quality: a robust framework

Rahu,

Shaikh,

Karim

et al. 2024

Mehran Univ. res. j. eng. technol.

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All living things, comprising animals, plants, and people require water to survive. The world is covered in water, just 1 percent of it is fresh and functional. The importance and value of freshwater have increased due to population growth and rising water demands. Approximately more than 70 percent of the world's freshwater is used for agriculture. Agricultural employees are the least productive, inefficient, and heavily subsidized water users in the world. They also utilize the most water overall. Irrigation consumes a considerable amount of water. The field's water supply needs to be safeguarded. A critical stage in estimating agricultural production is crop irrigation. The global shortage of fresh water is a serious issue, and it will only get worse in the years to come. Precision agriculture and intelligent irrigation are the only solutions that will solve the aforementioned issues. Smart irrigation systems and other modern technologies must be used to improve the quantity of high-quality water used for agricultural irrigation. Such a system has the potential to be quite accurate, but it requires data about the climate and water quality of the region where it will be used. This study examines the smart irrigation system using the Internet of Things (IoT) and cloud-based architecture. The water's temperature, pH, total dissolved solids (TDS), and turbidity are all measured by this device before the data is processed in a cloud using the range of machine learning (ML) approaches. Regarding water content limits, farmers are given accurate information. Farmers can increase production and water quality by using effective irrigation techniques. ML methods comprising support vector machines (SVM), random forests (RF), linear regression, Naive Bayes, and decision trees (DT) are used to categorize pre-processed data sets. Performance metrics like accuracy, precision, recall, and f1-score are used to calculate the performance of ML algorithms.

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Section: Review Of ML In Agricultural Qualitymentioning

confidence: 99%

An IoT and machine learning solutions for monitoring agricultural water quality: a robust framework

Rahu,

Shaikh,

Karim

et al. 2024

Mehran Univ. res. j. eng. technol.

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“…These two classes were considered essential classes, and the datasets were passed onto the machine learning frameworks. Three ML algorithms, i.e., Extra Trees classifier (ET) [41,42], Logistic regression (LR) [43,44], and Random Forest (RF) [43], were used in this study as they performed better than others.…”

Section: Datamentioning

confidence: 99%

Machine learning assisted prediction of land surface temperature (LST) based on major air pollutants over the Annamayya District of India

Mogaraju

2024

International Journal of Engineering and Geosciences

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Remote sensing (RS), Geographic information systems (GIS), and Machine learning can be integrated to predict land surface temperatures (LST) based on the data related to carbon monoxide (CO), Formaldehyde (HCHO), Nitrogen dioxide (NO2), Sulphur dioxide (SO2), absorbing aerosol index (AAI), and Aerosol optical depth (AOD). In this study, LST was predicted using machine learning classifiers, i.e., Extra trees classifier (ET), Logistic regressors (LR), and Random Forests (RF). The accuracy of the LR classifier (0.89 or 89%) is higher than ET (82%) and RF (82%) classifiers. Evaluation metrics for each classifier are presented in the form of accuracy, Area under the curve (AUC), Recall, Precision, F1 score, Kappa, and MCC (Matthew’s correlation coefficient). Based on the relative performance of the ML classifiers, it was concluded that the LR classifier performed better. Geographic information systems and RS tools were used to extract the data across spatial and temporal scales (2019 to 2022). In order to evaluate the model graphically, ROC (Receiver operating characteristic) curve, Confusion matrix, Validation curve, Classification report, Feature importance plot, and t- SNE (t-distributed stochastic neighbour embedding) plot were used. On validation of each ML classifier, it was observed that the RF classifier returned model complexity due to limited data availability and other factors yet to be studied post data availability. Sentinel-5-P and MODIS data are used in this study.

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A Comprehensive Review of Machine Learning Algorithms and Its Application in Groundwater Quality Prediction

Pandya,

Jaiswal,

Shah

2024

Arch Computat Methods Eng

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Application of machine learning algorithms in the investigation of groundwater quality parameters over YSR district, India

Cited by 6 publications

References 30 publications

An IoT and machine learning solutions for monitoring agricultural water quality: a robust framework

An IoT and machine learning solutions for monitoring agricultural water quality: a robust framework

Machine learning assisted prediction of land surface temperature (LST) based on major air pollutants over the Annamayya District of India

A Comprehensive Review of Machine Learning Algorithms and Its Application in Groundwater Quality Prediction

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