Machine Learning-Based Sine-Cosine Algorithm for Wastewater Quality Assessment Using Activated Carbon
Algorithm 1
VELMSA.
Input: sample of training data set , with label of
is the water quality indicator of wastewater plant ; is the total number of organic materials in the wastewater at ; is the crossover probability and its mutation.
Output: prediction of carbon in the wastewater plant using max voting decision.
Step 1: randomly select the water quality indicator parameter
Step 2: evaluate fitness value of using
(2)
Step 3: choose the next parameter of water quality indicator . Crossover and mutation on are used. Repeat the following steps until the criteria of genetic algorithm (GA) is met.
(1) Probability of crossover is determined by members of , and its fitness value is selected probabilistically to .
(2) Probability of chromosomes is in for the mutation, and store the weights of which are the outputs, and predict its label of chemical component in the wastewater plant.
Step 4: voting-based ELM is the produced output taken as decision making of multiple ELMs by using
