{"id":3374,"date":"2023-06-29T12:07:33","date_gmt":"2023-06-29T12:07:33","guid":{"rendered":"https:\/\/neuraldesigner.com\/blog\/beach-water-quality\/"},"modified":"2025-09-15T15:35:03","modified_gmt":"2025-09-15T13:35:03","slug":"beach-water-quality","status":"publish","type":"blog","link":"https:\/\/www.neuraldesigner.com\/blog\/beach-water-quality\/","title":{"rendered":"Detect anomalies in beach water quality using machine learning"},"content":{"rendered":"<p>In this example, we will detect anomalies in beach water quality by using machine learning and auto-association techniques. This approach can potentially improve the accuracy of beach water quality forecasts, allowing for more informed decision-making by beach managers and the public.<\/p>\n<p>Beaches are a popular destination for many people, whether for swimming, surfing, sunbathing, or simply enjoying the scenery.<\/p>\n<section>However, the quality of the water at a beach can have a significant impact on the health and safety of beachgoers.\u00a0A neural network can learn the patterns and correlations within a dataset.<\/p>\n<h3>Contents<\/h3>\n<ol>\n<li><a href=\"#ApplicationType\">Application type<\/a>.<\/li>\n<li><a href=\"#DataSet\">Data set<\/a>.<\/li>\n<li><a href=\"#NeuralNetwork\">Neural network<\/a>.<\/li>\n<li><a href=\"#ModelDeployment\">Model deployment<\/a>.<\/li>\n<\/ol>\n<p>This example is solved with <a href=\"https:\/\/www.neuraldesigner.com\/\">Neural Designer<\/a>. To follow it step by step, you can use the <a href=\"https:\/\/www.neuraldesigner.com\/free-trial\">free trial<\/a>.<\/p>\n<\/section>\n<section id=\"ApplicationType\">\n<h2>Application type<\/h2>\n<p>In this application type, the outputs are the same as the inputs. Therefore, this is an <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-networks-applications#AutoAssociation\">auto-association<\/a> problem.<\/p>\n<p>This auto association problem aims to determine water quality levels by inputting historical data on these environmental factors and corresponding water quality measurements.<\/p>\n<\/section>\n<section id=\"DataSet\">\n<h2>Data set<\/h2>\n<p>The first step is to prepare the <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set\">data set<\/a>, which is the source of information for this auto association problem. It comprises:<\/p>\n<ul>\n<li><a href=\"#DataSource\">Data source<\/a>.<\/li>\n<li><a href=\"#Statistics\">Statistics<\/a>.<\/li>\n<\/ul>\n<h3 id=\"DataSource\">Data source<\/h3>\n<p>The <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set#DataSource\">data source<\/a> is the file <a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/10\/beach-water-quality.csv\">beach-water-quality.csv<\/a>. It contains the data for this example in comma-separated values (CSV) format. The number of columns is 6, and the number of rows is 11568.<\/p>\n<p>The columns in the data set are this example&#8217;s variables. Because it is an auto association problem, all of the variables are <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set#InputVariables\">input variables<\/a><br \/>\nas well as output variables or <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set#TargetVariables\">target variables<\/a>.<\/p>\n<p>All the variables in this example are numerical, and they are the following:<\/p>\n<ul>\n<li><b>water_temperature<\/b>.<\/li>\n<li><b>turbidity<\/b>.<\/li>\n<li><b>transducer_depth<\/b>.<\/li>\n<li><b>wave_height<\/b>.<\/li>\n<li><b>wave_period<\/b>.<\/li>\n<li><b>battery_life<\/b>.<\/li>\n<\/ul>\n<p>The data set rows are the samples we use for training and testing. <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set#TrainingSamples\">Training samples<\/a> build different models with different topologies, and <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set#TestingSamples\">testing samples<\/a> validate the model&#8217;s performance.<\/p>\n<p>The following pie chart details the uses of all cases in the data set.<\/p>\n<p><img fetchpriority=\"high\" decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/beach-water-quality-Samples-pie chart.webp\" alt=\"The following pie chart details the uses of all cases in the data set used in the machine learning model to detect anomalies in beach water quality. We have a 69.4% for training, 17.4% for testing and 13.3% for unused.\" width=\"600\" height=\"350\" \/><\/p>\n<p>The dataset comprises 11,568 samples (rows), with 8,023 samples (69.4%) allocated for training and 2,011 samples (17.4%) designated for testing. The remaining 1534 samples are unused variables.<\/p>\n<h3 id=\"Statistics\">Statistics<\/h3>\n<p>The bare statistics provide valuable information when designing a model. The table below shows the minimums, maximums, means, and standard deviations of all variables in our data set.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/beach-water-quality-Data statistics table.webp\" \/><\/p>\n<\/section>\n<section>\n<h2 id=\"NeuralNetwork\">Neural network<\/h2>\n<p>The neural network will output all the target variables as a function of the input variables described in the previous section.<\/p>\n<p>In this auto association example, the neural network comprises:<\/p>\n<ul>\n<li>A scaling layer.<\/li>\n<li>A mapping layer.<\/li>\n<li>A bottleneck layer.<\/li>\n<li>A demapping layer.<\/li>\n<li>An output layer.<\/li>\n<li>An unscaling layer.<\/li>\n<\/ul>\n<h3>Scaling layer<\/h3>\n<p>The <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network#ScalingLayer\">scaling layer<\/a> contains the statistics on the input calculated from the data file and the method for scaling the input variables.<\/p>\n<p>Here, the <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network#MeanStandardDeviationScalingMethod\">mean and standard deviation<\/a> scaling methods are set.<\/p>\n<h3>Mapping layer<\/h3>\n<p>In the neural network architecture, a mapping layer refers to a layer of neurons that performs a nonlinear transformation on the input data.<\/p>\n<p>The mapping layer is responsible for taking the input data and mapping it to a new space where it can be more easily separated by subsequent layers.<\/p>\n<h3>Bottleneck layer<\/h3>\n<p>The mapping layer is followed by a bottleneck layer. It is typically located in the middle of the network and is used to reduce the dimensionality of the feature maps, thereby compressing the information contained in the data.<\/p>\n<h3>Demapping layer<\/h3>\n<p>The demapping layer performs an inverse operation to the mapping layer.<\/p>\n<p>In this problem, the <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network#HyperbolicTangentActivationFunction\">hyperbolic tangent activation function<\/a> activates the mapping and demapping layers, and the <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network#LinearActivationFunction\">linear activation function<\/a>\u00a0in the bottleneck and output layers.<\/p>\n<p>The following image depicts the <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/neural-network#NetworkArchitecture\">neural network architecture<\/a> of this auto-association problem.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/beach-water-quality-Network architecture.webp\" alt=\"Neural network for the machine learning model used to dectect anomalies in the beach water quality.\" width=\"1170\" height=\"525\" \/><\/p>\n<p>The yellow circles represent scaling neurons. The blue circles represent the mapping layer, the bottleneck layer, the demapping layer, and the output layer in that order. The red circles represent unscaling neurons.<\/p>\n<p>As we can see, there are 6 neurons in the scaling, output, and unscaling layers. This is because, in auto-associative neural networks, we want to learn to replicate the input data as closely as possible. There are 10 neurons in the mapping and demapping layers and 3 neurons in the bottleneck layer.<\/p>\n<\/section>\n<h2>Model deployment<\/h2>\n<p id=\"ModelDeployment\">Once we have tested the neural network&#8217;s generalization performance, we can save it for future use with the <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/model-deployment\">model deployment<\/a> function.<\/p>\n<p>It is interesting to find anomalies in the model to check its performance. For example, for inputs whose values are in the following table, we obtain the outputs also in the table below:<\/p>\n<table>\n<tbody>\n<tr>\n<th><\/th>\n<th>INPUTS<\/th>\n<th>OUTPUTS<\/th>\n<\/tr>\n<tr>\n<th>Water Temperature<\/th>\n<td style=\"text-align: right;\">13.55<\/td>\n<td style=\"text-align: right;\">19.8605<\/td>\n<\/tr>\n<tr>\n<th>Turbidity<\/th>\n<td style=\"text-align: right;\">595.01<\/td>\n<td style=\"text-align: right;\">590.1977<\/td>\n<\/tr>\n<tr>\n<th>Tranducer Depth<\/th>\n<td style=\"text-align: right;\">1066<\/td>\n<td style=\"text-align: right;\">1431.3051<\/td>\n<\/tr>\n<tr>\n<th>Wave Height<\/th>\n<td style=\"text-align: right;\">303.025<\/td>\n<td style=\"text-align: right;\">375.1188<\/td>\n<\/tr>\n<tr>\n<th>Wave Period<\/th>\n<td style=\"text-align: right;\">5.5<\/td>\n<td style=\"text-align: right;\">5.4176<\/td>\n<\/tr>\n<tr>\n<th>Battery Life<\/th>\n<td style=\"text-align: right;\">10.65<\/td>\n<td style=\"text-align: right;\">11.4966<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>The following table shows the distance between the input sample and its state predicted by the neural network.<br \/>\nDepending on this value, the sample is classified as one of the following types: stable, warning, or outlying.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/beach-water-quality-Sample distance prediction.webp\" \/><\/p>\n<p>In this case, the distance for this input sample is <b>0.543<\/b>, which is too high, meaning the sample is classified as <b>outlying<\/b>.<br \/>\nThis is likely due to an anomaly in the value of the variable turbidity. The mean value of the variable turbidity is 7.66, and in this input sample, it is 595.01. Considering the minimum and maximum turbidity values, the input value strays too far away from the realistic value of this variable.<\/p>\n<p>Next is an example of a stable sample:<\/p>\n<table>\n<tbody>\n<tr>\n<th><\/th>\n<th>INPUTS<\/th>\n<th>OUTPUTS<\/th>\n<\/tr>\n<tr>\n<th>Water Temperature<\/th>\n<td style=\"text-align: right;\">19.0755<\/td>\n<td style=\"text-align: right;\">19.0594<\/td>\n<\/tr>\n<tr>\n<th>Turbidity<\/th>\n<td style=\"text-align: right;\">7.66049<\/td>\n<td style=\"text-align: right;\">-0.2836<\/td>\n<\/tr>\n<tr>\n<th>Tranducer Depth<\/th>\n<td style=\"text-align: right;\">1417.46<\/td>\n<td style=\"text-align: right;\">1575.0985<\/td>\n<\/tr>\n<tr>\n<th>Wave Height<\/th>\n<td style=\"text-align: right;\">148.687<\/td>\n<td style=\"text-align: right;\">152.2600<\/td>\n<\/tr>\n<tr>\n<th>Wave Period<\/th>\n<td style=\"text-align: right;\">3.86446<\/td>\n<td style=\"text-align: right;\">3.9238<\/td>\n<\/tr>\n<tr>\n<th>Battery Life<\/th>\n<td style=\"text-align: right;\">11.075<\/td>\n<td style=\"text-align: right;\">11.0102<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>This input sample has a much lower distance than the input sample above this one, with a value of <b>0.062<\/b>.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/beach-water-quality-Sample distance prediction 2.webp\" \/><\/p>\n<p>Therefore, this sample is <b>stable<\/b>.<\/p>\n<section id=\"Conclusions\">\n<h2>Conclusions<\/h2>\n<p>In conclusion, finding anomalies in beach water using neural networks and auto-association techniques can provide valuable insights to ensure the safety and well-being of beachgoers.<\/p>\n<p>This search for an anomaly in the state of the beach can be used for hazard prevention. By utilizing historical data and identifying patterns in environmental factors, we can improve the accuracy of water quality forecasts, allowing for better-informed decisions by beach managers and the public.<\/p>\n<\/section>\n<section>\n<h2>Related post<\/h2>\n<\/section>\n","protected":false},"author":12,"featured_media":2574,"template":"","categories":[],"tags":[46],"class_list":["post-3374","blog","type-blog","status-publish","has-post-thumbnail","hentry","tag-environment"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Detect anomalies in beach water quality using machine learning<\/title>\n<meta name=\"description\" content=\"Build a machine learning model to detect anomalies in beach water quality levels by inputting historical data.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.neuraldesigner.com\/blog\/beach-water-quality\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Anomaly detection in beach water quality using neural networks\" \/>\n<meta property=\"og:description\" content=\"This article discusses the use of neural networks and auto association techniques to predict beach water quality. 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