## 1 Introduction
Since the leakage current on the surface of the insulator is affected by the material, surface contamination, and surrounding environment, as well as its nonlinear characteristics, artificial intelligence algorithms such as machine learning technology have been applied to analyze the leakage current or estimate contamination on the surface of insulators in some related studies [10; 11; 13; 17; 18; 19; 20; 21; 22]. For instance, artificial neural networks (ANNs) have been used to build a leakage current model [18; 19], support vector machines (SVMs) to evaluate contamination degree [20; 21], and random forests to predict equivalent salt deposit density (ESDD) based on parameters such as pollution and weather [22]. Although there have been numerous studies using machine learning and other algorithms for the leakage current of insulators, there is limited study on modeling analysis using long-term measurement data to build predictive models and evaluate the most effective prediction methods. \begin{table}
\begin{tabular}{c c} \hline
**Leakage Current Range** & **Surface Discharge Phenomena** \\ \hline
10 \(\mu\)A\(\sim\)100 \(\mu\)A & The surface is dry with a light corona \\
100 \(\mu\)A\(\sim\)1 mA & The surface is dry with a light corona \\
1 mA\(\sim\)10 mA & The surface is wet with a light spark \\
10 mA\(\sim\)100 mA & Spark and arc occur in a partial string \\
100 mA\(\sim\)1 A & Extended spark and arc occur \\
1 A\(\sim\)10 A & Flashovers occur \\
10 A above & Fault and outages occur \\ \hline \end{tabular}
\end{table} 
Table 1: Relationship between leakage current and surface discharge phenomena of the insulator. The study was conducted at a 161 kV test station located in a severely polluted industrial area on the western coast of Taiwan. In the test station, a data acquisition system has been built to measure the leakage current of insulators and atmospheric parameters (including temperature, relative humidity, pressure, wind speed, and ultraviolet) around the insulator. One insulator string was monitored under real operational conditions for 30 consecutive months. This paper proposes a novel method to predict the leakage current using artificial intelligence algorithms and establishes a real-time salt contamination monitoring system. Firstly, this study takes silicon-grease-coated insulators into account and installs them in the test station. The leakage current and weather parameters are collected for an extended period (30 months). Then, artificial intelligence and machine learning techniques (such as support vector regression, gradient boosting regression, and long short-term memory neural networks) are applied to establish a prediction model for the leakage current of the insulators. The established model can accurately predict the leakage current of insulators based on weather parameters. Subsequently, the most effective prediction method is evaluated in terms of Mean Squared Error (_MSE_), Mean Absolute Error (_MAE_), and Explained Variance Score (_EVS_). Additionally, to observe the real-time status of the insulator, this study establishes a monitoring platform that integrates the predicted leakage current, pollution level of the insulator, and weather parameters. It allows users or maintenance personnel to connect to the server through the network to observe the predicted results and weather parameters. The results can establish a real-time salt contamination monitoring system for insulators on transmission lines, enabling operation and maintenance personnel to realize the actual insulation situation of insulators in real time. This system not only helps prevent power outages due to salt contamination or pollution but also reduces the workload for maintenance personnel.