## 3 Materials and Methods
\begin{table}
\begin{tabular}{c c c c} \hline
**Literature** & **Methods** & **Data** & **Main Finding** \\ \hline
[24] & \begin{tabular}{c} Particle-swarm- \\ optimized multilayer \\ perceptron (PSO-MLP) model \\ \end{tabular} & \
\begin{tabular}{c} Landslides of Shicheng County in \\ Jiangxi Province of China \\ \end{tabular} &
\begin{tabular}{c} Proposed PSO-MLP model addresses the drawbacks of the \\ MLP-only model performs better than conventional \\ artificial neural networks (ANNs) and statistical models. \\ \end{tabular} \\ \hline
[25] & \begin{tabular}{c} MLP, linear \\ regression (LR) \\ \end{tabular} & \
\begin{tabular}{c} Covid -19 positive case from March to \\ mid-August 2020 in West Java \\ \end{tabular} &
\begin{tabular}{c} MLP reaches optimal if it used 13 hidden layers with \\ learning rate and momentum = 0.1. The MLP had a smaller \\ error than LR. \\ \end{tabular} \\ \hline
[26] & \begin{tabular}{c} random forest, MLP \\ \end{tabular} & \
\begin{tabular}{c} Electrical load data of six years from a \\ university campus \\ \end{tabular} &
\begin{tabular}{c} Hybrid forecast model performs better than other popular \\ single forecast models. \\ \end{tabular} \\ \hline
[27] & \begin{tabular}{c} MLP, Whale \\ optimization algorithm \\ \end{tabular} & \
\begin{tabular}{c} Read gold price \\ \end{tabular} &
\begin{tabular}{c} The proposed WOA-NN model demonstrates an \\ improvement in the forecasting accuracy obtained from the \\ classic NN, PSO-NN, GA-NN, GWO-NN, and \\ ARIMA model. \\ \end{tabular} \\ \hline
[28] & \begin{tabular}{c} Dynamic regional \\ combined short-term \\ rainfall forecasting \\ approach, MLP \\ \end{tabular} & \
\begin{tabular}{c} Actual height, temperature, temperaure \\ dew point difference, wind direction and \\ wind speed at 500 hPa height \\ \end{tabular} &
\begin{tabular}{c} DRCF outperforms existing approaches in both threat score \\ (TS) and root mean square error (RMSE). \\ \end{tabular} \\ \hline
[29] & \begin{tabular}{c} local MLP \\ \end{tabular} & \
\begin{tabular}{c} Simulated data \\ \end{tabular} &
\begin{tabular}{c} A greater degree of decomposition leads to the greater \\ reduction in forecast errors. \\ \end{tabular} \\ \hline
[34] & \begin{tabular}{c} GRU \\ \end{tabular} & \
\begin{tabular}{c} Vessels that travel on the inland \\ waterway \\ \end{tabular} &
\begin{tabular}{c} GRU provides the best prediction accuracy. \\ \end{tabular} \\ \hline
[35] & \begin{tabular}{c} Adam-GRU \\ \end{tabular} & \
\begin{tabular}{c} Guangzhou Port \\ \end{tabular} &
\begin{tabular}{c} Adam-GRU outperformed all other methods. \\ \end{tabular} \\ \hline
[36] & \begin{tabular}{c} LSTM \\ \end{tabular} & \
\begin{tabular}{c} Port of Singapore \\ \end{tabular} &
\begin{tabular}{c} LSTM outperformed all other benchmark methods. \\ \end{tabular} \\ \hline
[37] & \begin{tabular}{c} DeepTCN \\ \end{tabular} & \
\begin{tabular}{c} JD-demand, JD-shipment, electricity, \\ traffic and parts \\ \end{tabular} &
\begin{tabular}{c} The framework compares favorably to the state-of-the-art in \\ both point and probabilistic forecasting. \\ \end{tabular} \\ \hline
[38] & \begin{tabular}{c} CNN \\ \end{tabular} & \
\begin{tabular}{c} Bid and ask \\ \end{tabular} &
\begin{tabular}{c} CNNs are better suited for this kind of task. \\ \end{tabular} \\ \hline
[39] & \begin{tabular}{c} LSTM, CNN \\ \end{tabular} & \
\begin{tabular}{c} Electric load dataset in the \\ Italy-North Area \\ \end{tabular} &
\begin{tabular}{c} The experimental results demonstrate that the proposed \\ model can achieve better and stable performance in STLF. \\ \end{tabular} \\ \hline
[40] & \begin{tabular}{c} CNN \\ \end{tabular} & \
\begin{tabular}{c} Australian solar PV power data \\ \end{tabular} &
\begin{tabular}{c} Convolutional and multilayer perceptron neural networks \\ performed similarly in terms of accuracy and training time, \\ and outperformed the other models. \\ \end{tabular} \\ \hline
[41] & \begin{tabular}{c} Nonpooling CNN \\ \end{tabular} & \
\begin{tabular}{c} Simulated data, daily visits to website \\ \end{tabular} &
\begin{tabular}{c} Convolutional layers tend to improve the performance, \\ while pooling layers tend to introduce too many \\ negative effects. \\ \end{tabular} \\ \hline
[46] & \begin{tabular}{c} Transformer \\ \end{tabular} & \
\begin{tabular}{c} ILI data from the CDC \\ \end{tabular} &
\begin{tabular}{c} Transformer-based approach can model observed time \\ series data as well as phase space of state variables through \\ time delay embeddings. \\ \end{tabular} \\ \hline
[47] & \begin{tabular}{c} Enhancing the locality \\ of Transformer, breaking \\ the memory bottleneck \\ of Transformer \\ \end{tabular} & \
\begin{tabular}{c} Electricity-f (fine), electricity-c (coarse), \\ traffic-f (fine), traffic-c (coarse), wind \\ \end{tabular} &
\begin{tabular}{c} It compares favorably to the state o the art. \\ \end{tabular} \\ \hline
[48] & \begin{tabular}{c} Informer \\ \end{tabular} & \
\begin{tabular}{c} Electricity transformer temperature, \\ electricity consuming load, weather \\ \end{tabular} &
\begin{tabular}{c} The experiments demonstrated the effectiveness of \\ Informer for enhancing the prediction capacity in \\ LSTF problem. \\ \end{tabular} \\ \hline
[49] & \begin{tabular}{c} customized transformer \\ neural network \\ \end{tabular} & \
\begin{tabular}{c} Electricity consumption dataset, \\ traffic dataset \\ \end{tabular} &
\begin{tabular}{c} In terms of long-term estimation Up to eight times more \\ resistant and in terms of estimation accuracy about 20 \\ percent improvement, compare to other well-known \\ methods, is obtained. \\ \end{tabular} \\ \hline \end{tabular}
\end{table}