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@article{148582,
        author = {Neil Rayala and Edson Alves},
        title = {Efficient Determination of Water Quality using Incremental Support Vector Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {6},
        number = {3},
        pages = {172-175},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=148582},
        abstract = {The water quality index (WQI) is a major metric for determining overall water quality. Unfortunately, measurement of WQI typically requires expensive and time-consuming methods. WQI can be determined solely with an affordable and relatively common ultraviolet-visible (UV-Vis) spectrophotometry. However, this determination is neither accurate nor efficient enough for high throughput analysis of large water sample collections. Here, we describe a powerful combination of incremental support vector machine (I-SVM) learning and UV-Vis spectrophotometry for WQI classification along with Monte Carlo methods for inexpensive verification. The combined approach greatly improves both testing accuracy and training efficiency. By modeling the UV-Vis spectral analysis data, Monte Carlo methods precisely simulated a total of 115,000 water samples to test for model overfitting and long-run accuracy and efficiency. In the I-SVM, the classifier quickly updates its hyperparameters based on new batches of training samples without substantially increasing the training time. Consequently, the I-SVM reached a lower average training time (1.17152 seconds) compared to the normal support vector machine (27,734.16493 seconds) and a higher classification accuracy rate (91.61043%) compared to the multilayer perceptron neural network (83.05739%). The continuous high accuracy and efficiency of the I-SVM allows water organizations such as the U.S. Geological Survey to compute the WQI at many water sites simultaneously.},
        keywords = {Support Vector Machine, Incremental Support Vector Machine, Multilayer Perceptron Neural Network, Ultraviolet-Visible Spectrophotometry, Overfitting, Water Quality Index},
        month = {},
        }