Eduardo Caldeira Vicente

A lightweight neural‐network classifier was developed to assign surface‐water samples to the five quality classes established by Brazil's environmental standard CONAMA 357/2005. The study used a 2018 dataset from SEMASA‐Itajaí comprising 8928 hourly observations of pH, turbidity, apparent color, and local rainfall. After removing outliers, applying weighted k‐nearest‐neighbor imputation and performing min–max normalization, the data were evaluated using a 5‐fold Stratified Cross‐Validation approach to ensure robust generalization. The network, implemented in TensorFlow, consists of two fully connected layers with 32 and 16 rectified linear units, followed by a softmax output layer. Training for 100 epochs with the Adam optimizer (learning rate 0.001) achieved an average validation accuracy of 97.63% (±0.86%), with misclassifications restricted to adjacent regulatory classes. Adding rainfall as an auxiliary feature and fine‐tuning the learning rate further increased accuracy by 0.7 percentage points and reduced training variance.

This paper presents the results of a structured scoping review (SSR) that explores the integration of the Internet of Things (IoT) and embedded systems in creating a sustainable and interconnected technological ecosystem. The study focuses on water quality monitoring, an area where these technologies have demonstrated significant potential. The SSR follows a meticulous methodology, covering planning, execution, and documentation stages to ensure a comprehensive and unbiased review of the existing literature. Key research questions guide the review, focusing on extracting and analyzing water sample characteristics, using machine learning algorithms for classification, and the technologies utilized in these systems. The search process involved multiple databases, yielding 343 articles, of which 8 met the stringent inclusion and exclusion criteria. The review highlights the widespread use of IoT for real-time data collection and artificial intelligence (AI) for analyzing complex patterns in water quality data. Our findings underscore the significance of temperature, pH, turbidity, and conductivity, commonly utilized in water classification. In addition, prevalent machine learning techniques for analyzing water quality data include K-Nearest Neighbors (KNN) and artificial neural networks (ANN). Despite the advances, challenges such as implementation costs, connectivity in remote areas, and the interpretability of AI models remain. This review underscores the transformative potential of IoT and AI in water quality monitoring, with implications for ensuring safe drinking water and sustainable water resource management.

This work presents the creation of a neural network trained using a dataset from SEMASA (Serviço Municipal de Água, Saneamento Básico e Infraestrutura) from Itajaí, containing pH, turbidity, and apparent color data of water bodies from various neighborhoods in the municipality, capable of classifying water samples according to the water types defined by the CONAMA (Conselho Nacional do Meio Ambiente) 357/05 standard for embedding in an IoT device. The dataset is composed of 8,928 records from 2018 to 2023, covering neighborhoods São Roque I and II, Arapongas, and Limoeiro. The selected variables are critical for water quality assessment, where pH indicates the acidity or alkalinity of the water, turbidity measures the presence of suspended particles, and apparent color is a visual indicator of dissolved and suspended materials.