{"id":3462,"date":"2023-08-31T11:12:59","date_gmt":"2023-08-31T11:12:59","guid":{"rendered":"https:\/\/neuraldesigner.com\/learning\/aquatic-toxicity\/"},"modified":"2025-09-18T12:28:36","modified_gmt":"2025-09-18T10:28:36","slug":"aquatic-toxicity","status":"publish","type":"learning","link":"https:\/\/www.neuraldesigner.com\/learning\/examples\/aquatic-toxicity\/","title":{"rendered":"Model water toxicity using machine learning"},"content":{"rendered":"

In this example, we build a machine learning model to calculate water toxicity using actual data from a water body.<\/p>\n

Many chemicals partition into water and can exert adverse effects on aquatic systems, threatening the survival of the organisms that inhabit these ecosystems.<\/p>\n

Calculating a standard measure of aquatic toxicity is a lengthy and costly procedure.<\/p>\n

As a result, we do not need to measure it in the laboratory.<\/p>\n

This example is solved with Neural Designer<\/a>. You can use the\u00a0free trial<\/a> to understand how the solution is achieved step by step.<\/p>\n

Contents<\/h2>\n
    \n
  1. Application type<\/a><\/li>\n
  2. Data set<\/a><\/li>\n
  3. Neural network<\/a><\/li>\n
  4. Training strategy<\/a><\/li>\n
  5. Testing analysis<\/a><\/li>\n
  6. Model deployment<\/a><\/li>\n<\/ol>\n
    \n

    1. Application type<\/h2>\n

    This is an approximation<\/a>\u00a0project since the variable to be predicted is continuous.<\/p>\n

    The primary objective is to model the LC50 (the standard measure of toxicity) as a function of the sample’s molecular properties.<\/p>\n<\/section>\n

    \n

    2. Data set<\/h2>\n

    The first step is to prepare the\u00a0data set<\/a>. This is the source of information for the approximation problem. It is composed of:<\/p>\n