This document presents a validation of the results obtained using the Method of Average Lengths (MAL) through neural networks, specifically MLPRegressor and Random Forest models. Developed by the author starting in 2021, the MAL method enables the detection of non-linear or time-lagged interactions in climate time series, which often remain undetected by classical statistical methods (e.g., correlation, R², Granger-Newbold cointegration).

The study focuses on three key interactions:

1. Between heat of Upper Ocean Stratum (UOS) and atmospheric temperature (T): Neural networks confirm a strong relationship, with a lagged effect of T on UOS, reflecting oceanic thermal inertia. 2. Between ocean stratification (strat) and UOS: Models validate strong thermal coupling but fail to capture the structural influence of ENSO on stratification, which is detected by MAL. 3. Between oceanic cloud cover (OC) and UOS: A feedback loop is confirmed, where UOS influences OC and vice versa, supporting the hypothesis of a "natural oceanic thermostat."

The results demonstrate that neural networks complement the MAL method by quantifying relationships, while MAL detects dynamic signals inaccessible to traditional methods. This hybrid approach opens new perspectives for modeling complex climate interactions.