A Machine Learning Approach to Analyze Marine Life Sustainability

Jain, Danish; Shah, Shanay; Mehta, Heeket; Lodaria, Ayushi; Kurup, Lakshmi

doi:10.1007/978-981-15-8443-5_53

Cited by 5 publications

(3 citation statements)

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“…Based on the parameters and equations listed below, a tree structure is built to categorize the features [1]. The Gini Index may be used to create the decision tree for any tuple S and is determined using the formula:…”

Section: Random Forest (Rf)mentioning

confidence: 99%

“…Water contamination degrades water quality, impacting the health of sea creatures and, by extension, humans that use them. This makes it critical to observe water quality and ensure the survival of nautical life [1]. Comprehension of water quality concerns and issues is also crucial for water pollution mitigation and control.…”

Section: Introductionmentioning

confidence: 99%

“…• C is the regularization parameter. Tested values are[1,2,3,4,5]. The best parameter is C = 2.• Epsilon is the margin of tolerance for errors.…”

mentioning

confidence: 99%

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Water quality prediction using machine learning models based on grid search method

Shams,

Elshewey,

El-kenawy

et al. 2023

Multimed Tools Appl

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Water quality is very dominant for humans, animals, plants, industries, and the environment. In the last decades, the quality of water has been impacted by contamination and pollution. In this paper, the challenge is to anticipate Water Quality Index (WQI) and Water Quality Classification (WQC), such that WQI is a vital indicator for water validity. In this study, parameters optimization and tuning are utilized to improve the accuracy of several machine learning models, where the machine learning techniques are utilized for the process of predicting WQI and WQC. Grid search is a vital method used for optimizing and tuning the parameters for four classification models and also, for optimizing and tuning the parameters for four regression models. Random forest (RF) model, Extreme Gradient Boosting (Xgboost) model, Gradient Boosting (GB) model, and Adaptive Boosting (AdaBoost) model are used as classification models for predicting WQC. K-nearest neighbor (KNN) regressor model, decision tree (DT) regressor model, support vector regressor (SVR) model, and multi-layer perceptron (MLP) regressor model are used as regression models for predicting WQI. In addition, preprocessing step including, data imputation (mean imputation) and data normalization were performed to fit the data and make it convenient for any further processing. The dataset used in this study includes 7 features and 1991 instances. To examine the efficacy of the classification approaches, five assessment metrics were computed: accuracy, recall, precision, Matthews's Correlation Coefficient (MCC), and F1 score. To assess the effectiveness of the regression models, four assessment metrics were computed: Mean Absolute Error (MAE), Median Absolute Error (MedAE), Mean Square Error (MSE), and coefficient of determination (R2). In terms of classification, the testing findings showed that the GB model produced the best results, with an accuracy of 99.50% when predicting WQC values. According to the experimental results, the MLP regressor model outperformed other models in regression and achieved an R2 value of 99.8% while predicting WQI values.

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Section: Random Forest (Rf)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%