Is employing BPNN for water quality management an overkill? [closed]

Question

I'm developing a device for Freshwater Quality Management which can be used for freshwater bodies such as lakes and rivers. The project is spread in three parts:

1. The first part deals with acquiring parameters such as pH, turbidity etc.
2. The second part deals with taking corrective measures based on the parameters. For instance, if the pH is too low, the device will inject basic solution to maintain a pH of 7-7.5.
3. Now the third part deals with predicting the health of the lake based on the parameters acquired (pH/Turbidity etc.). The predictive algorithm shall take in account of parameters and develop a correlation between them to explain for how long the lake will sustain. To achieve this, I'm currently biased toward using Back Propagation Neural Network (BPNN) as I have found that multiple other people/institutes prefer NN for water quality management.*

Now my concern is whether using BPNN would be an overkill for this project? If yes, which method/tool should I go for?

*1,2 and 3

Answer 1

Doing something the way "it used to be" is not always the best idea. In general, if you do not have strong, analytical reasons to choose neural network you should not ever start with it. Neural networks are tricky to train, have huge number of hyperparameters, are non-deterministic and computationaly expensive. Always start with the simpliest model, and only if it yields poor results - move to more complex ones. From theoretical perspecitive it is strongly justified by Vapnik theorems, and from practical it is similar to agile approach in programming.

So where to start?

1. Linear regression (Ridge regression, Lasso)
2. Polynomial regression
3. KNN regression
4. RBF Networks
5. Random Forest Regresor

If all of them fail - think about "classical" neural network. But chances are rather... small.