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I conducted a study for one-year intervals biweekly from January to December 202
I conducted a study for one-year intervals biweekly from January to December 2022. The data available is a reading of collected samples that were analyzed for the following chemicals: total dissolved solids (TDS), nutrients (Ammonia (NH3-N), total Kjeldahl nitrogen (TKN), total nitrogen (TN), and phosphate (PO4-P)), heavy metals parameters including arsenic (As), cadmium (Cd), chromium (Cr), nickel (Ni), mercury (Hg), iron (Fe), copper (Cu), manganese (Mn), zinc (Zn), lead (Pb) and silver (Ag), In this study also, five pharmaceutical compounds were analyzed including metronidazole, trimethoprim, sulfamethoxazole, paracetamol, and ranitidine. These compounds were analyzed in January, February, April, June, and August 2022.
I want to use these data to conduct a novel research paper that has a very high chance of being published in the Q1 Environmental Journal. This will be done if proper and high-quality scientific writing using these results to write novel research employing machine learning techniques such as R programming to conduct analysis such like
Detection of Anomalous Events
a. Time Series Analysis: Use R’s time series analysis packages, such as ts, forecast, and anomalies, to detect abnormal fluctuations or spikes in pollutant levels over time. Time series decomposition, seasonal decomposition of time series (STL), and autoregressive integrated moving average (ARIMA) modeling can help identify anomalous patterns and trends in environmental data.
b. Machine Learning Algorithms: Implement anomaly detection algorithms, such as isolation forests, one-class support vector machines (SVM), or autoencoders, using R packages like anomalyDetection, e1071, or keras. These algorithms can identify deviations from normal behavior in multivariate datasets and flag potential pollution incidents or outliers.
c. Statistical Process Control (SPC): Apply SPC methods, such as control charts (e.g., Shewhart charts, cumulative sum charts, or exponentially weighted moving average charts), to monitor pollutant levels and detect unusual variations from expected norms. R packages like qcc and qualityTools provide functions for implementing SPC techniques in environmental monitoring.
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