# Air Temperature Forecasting Using Machine Learning Techniques: A Review

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## Abstract

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## 1. Introduction

## 2. Overview of Machine Learning Based Strategies and Forecast Performance Factors

- Supervised Learning, which has information of the predicted outputs to label the training set and is used for the model training.
- Unsupervised Learning, which does not have information about the desired output to label the training data. Consequently, the learning algorithm must find patterns to cluster the input data.
- Semi-supervised Learning, which uses labeled and unlabeled data in the training process.
- Reinforcement Learning, which uses the maximization of a scalar reward or reinforcement signal to perform the learning process, being positive or negative based on the system goal. Positive ones are known as “rewards” while negative ones are known as “punishments”.

#### 2.1. Artificial Neural Networks

- Tangent Hyperbolic Function: $f\left(x\right)=\frac{({e}^{x}-{e}^{-x})}{({e}^{x}+{e}^{-x})}$,
- Sigmoid Function: $f\left(x\right)=\frac{1}{1+{e}^{-x}}$,
- Rectified Linear Unit (ReLU) Function: $f\left(x\right)=$max$(0,x)$,
- Gaussian Function: $f\left(x\right)={e}^{-{x}^{2}}$,
- Linear Function: $f\left(x\right)=x$,

#### 2.2. Support Vector Machines

- A Linear kernel: $K({x}_{i},{x}_{j})={x}_{i}^{T}{x}_{j}$,
- A Polynomial kernel: $K({x}_{i},{x}_{j})={(\gamma {x}_{i}^{T}{x}_{j}+r)}^{d}$,
- A Radial kernel: $K({x}_{i},{x}_{j})=\mathrm{exp}(-\gamma ||{x}_{i}-{x}_{j}{\left|\right|}^{2})$,
- A Sigmoid kernel: $K({x}_{i},{x}_{j})=\mathrm{tanh}(\gamma {x}_{i}^{T}{x}_{j}+r)$,

#### 2.3. Evaluation Measures

- Mean Absolute Error (MAE): This measure is an error statistic that averages the distances between the estimated and the observed data for N samples:$$MAE=\frac{1}{N}\sum _{i=1}^{N}|{y}_{i}-\widehat{{y}_{i}}|$$
- Median Absolute Error (MdAE): This measure is defined as the median of the absolute differences $|y-\widehat{y}|$ for any N pairs of forecasts and measurements:$$MdAE=Median\left(\right|y-\widehat{y}\left|\right)$$
- Mean Square Error (MSE): This measure is defined as the average squared difference between the predicted and the observed temperature data, for N samples:$$MSE=\frac{1}{N}\sum _{i=1}^{N}{|{y}_{i}-\widehat{{y}_{i}}|}^{2}$$
- Root Mean Square Error (RMSE): This measure is the standard deviation of the difference between the estimation and the true observed data (See Equation (10)). This measure is more sensitive to big prediction errors:$$RMSE=\sqrt{\frac{1}{N}\sum _{i=1}^{N}{|{y}_{i}-\widehat{{y}_{i}}|}^{2}}$$

- Mean Absolute Percentage Error (MAPE): This measure offers a proportionate nature of error with respect to the input data. It is defined as:$$MAPE=\frac{1}{N}\sum _{i=1}^{N}\frac{|{y}_{i}-\widehat{{y}_{i}}|}{{y}_{i}}\times 100$$
- Root Mean Square Percentage Error (RMSPE) RMSPE is calculated according to:$$RMSPE=\sqrt{\frac{1}{N}\sum _{i=1}^{N}\frac{|{y}_{i}-\widehat{{y}_{i}}{|}^{2}}{{y}_{i}}\times 100}$$

- Relative Mean Absolute Error (RMAE): This measure is computed as:$$RMAE=\frac{MAE}{MA{E}^{*}}$$
- Relative Root Mean Square Error (RRMSE): This measure is calculated in a similar way to the RMAE, but in this case using the error defined in Equation (10):$$RMAE=\frac{RMSE}{RMS{E}^{*}}$$

#### 2.4. Input Features, Time Horizon, and Spatial Scale

- The model is based on other meteorological or geographical variables (e.g., solar radiation, rain, relative humidity measurements, among others).
- The model only takes into account the historically observed data of temperature as system input.
- The model takes a combination of both temperature values and other parameters, to perform the prediction.

## 3. Long-Term Global Temperature Forecasting

_{2}[63]. Then, there is an increasing involvement of science and scientists to characterize the impacts of global climate change on decadal [64] or longer time scales [65], in order to structure prospects for global policy actions. This variability has been studied in response to the Global mean Temperature rise that the earth has been experienced since pre-industrial times. Therefore, this section details the application of ML-based strategies in global temperature forecasting using a variety of meteorological variables.

_{2}emission) models. Among the multi-regressive and the nonparametric spectral estimation algorithms, commonly used in time-series forecasting, they analyze the Neural Network Performance using the GT data obtained from the Goddard Institute for Studies (GISS), and the CO

_{2}data from the Carbon Dioxide Information Analysis Centre. The ANN implemented for the analysis is a feed-forward neural network with a single hidden layer and one hidden node. The algorithm used for training is the rprop+ and the activation function is a sigmoid. RRMSE obtained in this paper is 0.67 average for 1 to 10 steps ahead, showing higher error values compared with other competing models.

## 4. Regional Temperature Forecasting

_{2}emissions, global temperature forecasting models have been proposed (e.g., General Circulation Models) in order to find strategies to mitigate the possible environmental and economic damages [78].

#### 4.1. Hourly Temperature Forecasting

#### 4.2. Daily Temperature Forecasting

#### 4.3. Monthly Temperature Forecasting

## 5. Discussion and Research Gaps Identification

_{2}emissions, latitude, longitude, and altitude. However, the maximum, minimum, and mean values of temperature are found to be the common parameter for all the research. In fact, a relevant amount of works use only these features as model inputs.

- Most of the research presented in this review is focused on the local analysis of the air temperature. However, there is not an extensive study about the anomalies prediction of temperature at a global level by means of these ML-based approaches. Taking into account the robust data currently available in diverse web sites, different ML-strategies and input features could be used to accurately predict temperature anomalies at the global level.
- Research reported at the regional level has not deeply analyzed the dependency of the temperature values of the surrounding area in the temperature estimation. A study oriented to analyze the impact of using temperature values of surrounding stations as inputs, based on the distance each other, could be of particular interest.
- A large number of the works described in this review do not include a time horizon analysis. The lack of these results makes it difficult to have a better idea of the accuracy of the method proposed. Likewise, a set of evaluation measures must be calculated in order to facilitate the comparison with other methods which may use the same data-set.
- Taking into account that accuracy results strongly depend on the data-set analyzed, a comprehensive study of the influence of the data-set size for training and testing should be done to offer a more fair comparison between strategies.
- A comparative analysis with all the available ANN-based techniques (MLPNN, RBFNN, ERNN, GRNN, JPSN, RCNN, and SDAE) and SVM variations (LS-SVM, PSVM, WT+SVM) should be carried out in order to determine the best strategy and algorithms to forecast air temperature for different time horizon. In this sense, as well as it is developed in other areas, a competition using a complete standard data-set could help in this objective.
- The effect analysis of each variable, such as maximum, minimum, and average temperature, precipitation, pressure, Mean Sea Level, Wind Speed and Direction, Relative Humidity, Sunshine, Evaporation, Daylight, Time (Hour, day or Month), Solar Radiation, geographical variables (latitude, longitude, and altitude), cloudiness, and CO
_{2}emissions, used in the prediction is required to be taken into account to increase the temperature prediction accuracy. - A further study about the feature selection, based on their relevance, should be performed. Different strategies, such as Automatic Relevance Determination, closely-related sparse Bayesian learning, or Niching genetic algorithm have not been taken into account.
- Recently, Deep Learning strategies have shown a great performance for classification tasks [97]. However, a few studies have proven, with promising results, that prediction could be accurately done by means of these techniques. More further analysis should be developed in this area.
- For the evaluation of RNN, the size of the time series required to accurately predict a single value of temperature should be studied. Likewise, a comprehensive study about the structure of the recurrent unit should be included.
- In-depth analysis using statistical significance tests is required in order to assess the forecasting model’s performance in terms of its ability to generate both unbiased and accurate forecasts. In these cases, the respective accuracy is evaluated by using both error magnitude and directional change error criteria.

## Author Contributions

## Funding

## Conflicts of Interest

## Abbreviations

AANN | Abductive Artificial Neural Network |

ANFIS | Adaptive Neuro-Fuzzy Inference System |

ANN | Artificial Neural Network |

AR | Auto-Regressive |

DT | Daily Temperature |

EN | Elastic Net |

ENSO | El Niño Southern Oscillation |

ERNN | Elman Recurrent Neural Network |

GLOT | Global Land-Ocean Temperature |

GSVM | Generalized Support Vector Machine |

GT | Global Temperature |

HFM | Hopfield model |

HT | Hourly Temperature |

JPSN | Jordan Pi-Sigma Network |

LASSO | Least Absolute Shrinkage and Selection Operator |

LS-SVM | Least Squares-Support Vector Machine |

MAE | Mean Absolute Error |

MAPE | Mean Absolute Percentage Error |

MdAE | Median Absolute Error |

ML | Machine Learning |

MLPNN | MultiLayer Perceptron Neural Network |

MSE | Mean Squared Error |

PNN | Probabilistic Neural Network |

PSO | Particle Swarm Optimization |

RBFNN | Radial Basis Functions Neural Network |

RCNN | Recurrent Convolutional Neural Network |

RMSE | Root Mean Squared Error |

RNN | Recurrent Neural Network |

SDAE | Stacked Denoising Auto-Encoders |

SI | Solar Irradiance |

SOD | Stratospheric Optical Depth |

SOFM | Self-Organizing Feature Map |

SVM | Support Vector Machine |

WNN | Wavelet Neural Network |

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Reference | Input | Dataset | Hidden Neurons | Training Algorithm | Activation Function | Evaluation Criteria/Time Horizon |
---|---|---|---|---|---|---|

[20] | GT | [71] | 4 | Generalized Delta Rule | Sigmoid | 1-step RMSE = 0.12 °C |

[66] | Surface Warming, Global Ocean Heat Uptake | [72] [73] | 10 | Levenberg-Marquardt | Sigmoid-Linear | MSE ≈ 0.5 °K |

[67] | SI-SOD CO _{2}-SulfateENSO | [73], [74], [75], [76] | 4.5 | Widrow-Hoff Rule | Normalized Sigmoid | R = 0.877 |

[69] | GT-CO${}_{2}$ | [77] | 11.8 | Levenberg-Marquardt | Tanh | 1-4-step MAEs(MdAEs) = 0.088 (0.70) °C 10-step MAEs(MdAEs) = 0.078 (0.053) °C 20-step MAEs(MdAEs) = 0.078 (0.053) °C |

[39] | Rain, Pressure Wind Speed, GT, Relative Humidity | [77] | 11 | Levenberg-Marquardt | Sigmoid-Linear | 1-step MSE(RMSE) = 0.0891(1.6571) °C |

[70] | GT-CO_{2} | [77] | 1 | rprop+ | Sigmoid | 1-step RRMSE = 0.67 °C |

Reference | Input | Region | ML Algorithm | Configuration | Evaluation Criteria/Time Horizon |
---|---|---|---|---|---|

[21] | HT | Brazil | ARMA+MLPNN | Hidden nodes = 10, Algorithm = Levenberg–Marquardt, Activation Function = Tanh-Linear | 1-step MAPE = 2.66% |

[22] | T$(d-1$, $h-1)$ | Saudi Arabia | MLPNN | Hidden nodes = 4, Algorithm = Batch learning | 1-step MPD = 3.16%, 4.17%, 2.83% |

[23] | coded h, T $(h-1)$ | Texas | RBFNN | RBF = Multi-quadratic, Model Selection = Bayesian Size (hyperrectangles, RBF centres) = 10 | 1-step MAE = 0.4466 °C |

[3] | (${T}_{\{1,2,\dots ,24\}}(d-1)$), Tmax, Tmin, ETmax, ETmin ($N{T}_{\{1,2,\dots ,h-1\}}\left(d\right)$ | Seattle | AANN | Models range from (single element-single layer) to (Five-input, two-element, two-layer) Complexity Penalty Multiplier = 1 | Next,-h MAE (MAPE) = 1.68 F (3.49%) Next,-d MAE (MAPE) = 1.05 F (2.14%) |

[24] | HT, Wind Speed and Relative Humidity | Canada | MLPNN+RBFN +ERNN+HFM | (MLPNN, ERNN): Hidden nodes = 45 Algorithm = one-step secant Activation Function: Tanh, sigmoid (RBFN): 2 hidden layers, 180 nodes Activation Function: Gaussian | 24-step Winter MAE = 0.0783 °C 24-step Summer MAE = 0.1127 °C 24-step Spring MAE = 0.0912 °C 24-step Fall MAE = 0.2958 °C |

[25] | Up to prior 24 h: HT, Wind Speed Rain,Relative Humidity Solar Radiation (10 k–400 k) | Georgia | Ward MLPNN | Hidden Layer = 3 parallel slabs Hidden nodes: (2–75) nodes per slab Activation Function = Gaussian, Tanh, Sigmoid | 1-step MAE = 0.53 ${}^{\xb0}$C 4-step MAE = 1.34 ${}^{\xb0}$C 8-step MAE = 2.01 ${}^{\xb0}$C 12-step MAE = 2.33 ${}^{\xb0}$C |

[33] | Radial-basis function kernel $\u03f5$ = 0.05, $C=25$, $\gamma $ = 0.0104 | 1-step MAE = 0.514 ${}^{\xb0}$C 4-step MAE = 1.329 ${}^{\xb0}$C 8-step MAE = 1.964 ${}^{\xb0}$C 12-step MAE = 2.303 ${}^{\xb0}$C | |||

[26] | Up to prior 24 h: HT,Wind Speed Rain, Relative Humidity Solar Radiation (1.25 million) | Ward MLPNN | Hidden Layer = 3 parallel slabs Hidden nodes: 120 nodes per slab Activation Function = Tanh | 1-step MAE = 0.516 ${}^{\xb0}$C 4-step MAE = 1.187 ${}^{\xb0}$C 8-step MAE = 1.623 ${}^{\xb0}$C 12-step MAE = 1.873 ${}^{\xb0}$C | |

[33] | SVM | Radial-basis function kernel $\u03f5$ = 0.05, $C=25$, $\gamma $ = 0.0104 | 1-step MAE = 0.513 ${}^{\xb0}$C 4-step MAE = 1.203 ${}^{\xb0}$C 8-step MAE = 1.664 ${}^{\xb0}$C 12-step MAE = 1.922 ${}^{\xb0}$C | ||

[27] | Global Solar Radiation | Morocco | AR + MLPNN | 2 hidden layers (5 and 8 neurons) Activation function = tanh | 1-step MSE = 0.272 ${}^{\xb0}$C |

[34] | Relative humidity, Precipitation Pressure, Global Radiation HT, Wind Speed and Direction | Spain | SVM Banks | 4 SVMs for: zonal, mixed, meridional, transition Gaussian Function Kernels | 1-step RMSE = 0.61 ${}^{\xb0}$C 2-step RMSE = 0.94 ${}^{\xb0}$C 4-step RMSE = 1.21 ${}^{\xb0}$C 6-step RMSE = 1.34 ${}^{\xb0}$C |

[35] | ${T}_{h-1},{T}_{h-2},{T}_{h-3},{T}_{h-4}$ | Saudi Arabia | LS-SVM | Radial-basis function kernel Optimal combination (C,$\gamma $) for a MSE = 0.0001 | 1-step MAPE = 1.20% |

MLPNN | Hidden layers = 2, Hidden Nodes = 24, 19 | 1-step MAPE = 2.36% | |||

RBFNN | Hidden layers = 1, Hidden Nodes = 22 | 1-step MAPE = 1.98% | |||

RNN | Hidden layers = 1, Hidden Nodes = 17 | 1-step MAPE = 1.62% | |||

PNN | Hidden layers = 3, Hidden Nodes = 4, 3, 2 | 1-step MAPE = 1.58% | |||

[80] | Previous 24 h values of HT, barometric pressure, humidity and wind speed | Nevada | SDAE | Hidden Layers = 3, Hidden nodes = 384 Learning Rate = 0.0005, Noise = 0.25 | 1-step RMSE = 1.38% |

MLPNN | Hidden Layers = 3, Hidden nodes = 384 Learning Rate = 0.1 | 1-step RMSE = 4.19% | |||

[40] | Surface temperature and pressure, wind, rain, humidity snow, and soil temperature | Simulated Data | LSTM | 5 layers, activation functions: linear, tanh Learning Rate = 0.01, Adam Optimizer | 1-step MSE = 0.002041361${}^{\xb0}$K |

CRNN | 5 layers (Filter size: 32, 64, 128, 256,512) Learning Rate = 0.01, Adam Optimizer | 1-step MSE = 0.001738656${}^{\xb0}$K |

Ref. | Input | Region | Algorithm | Configuration | Evaluation Criteria/Time Horizon |
---|---|---|---|---|---|

SOFM+MLPNN | Hidden layer = 1, Hidden Nodes = 10 | Error (Max DT) ≤ 2 °C in 88.6% cases | |||

For 3 previous days: 2 measures | Activation Function= Sigmoid | Error (Min DT) ≤ 2 °C in 87.3% cases | |||

[28] | of mean sea level and vapor | Calcutta | MLPNN | Hidden layer = 1, Hidden Nodes = 15 | Error (Max DT) ≤ 2 °C in 83.8% cases |

pressures, and relative humidity, | Activation Function= Sigmoid | Error (Min DT) ≤ 2 °C in 85.2% cases | |||

Max DT, Min DT, Rainfall | RBFNN | Size (RBF centres) = 50 | Error (Max DT) ≤ 2 °C in 80.65% cases | ||

Error (Min DT) ≤ 2 °C in 81.66% cases | |||||

MLPNN | Hidden layer = 1, Hidden Nodes = 45 | MAPE = 6.05% RMSE = 0.6664 °C | |||

Levenberg–Marquardt Algorithm | MAE = 0.5561 °C | ||||

ERNN | Hidden layer = 1, Hidden Nodes = 45 | MAPE = 5.52% RMSE = 0.5945 °C | |||

[29] | Average DT, Wind Speed and | Canada | Levenberg–Marquardt Algorithm | MAE = 0.5058 °C | |

Relative Humidity | RBFNN | Hidden = 2, RBF Nodes = 180 | MAPE = 2.49% RMSE = 0.2765 °C | ||

Gaussian Activation Function | MAE = 0.2278 °C | ||||

Ensemble | Arithmetic mean and weighted | MAPE = 2.14% RMSE = 0.2416 °C | |||

average of all the results | MAE = 0.1978 °C | ||||

MLPNN | Levenberg–Marquardt Algorithm | Mean RMSE (Tmean) = 1.7767 °C | |||

Daily mean, maximum | Hidden Layers = 1, Hidden Nodes = 5 | Mean RMSE (Tmin,Tmax) = 2.21, 2.86 °C | |||

[30] | and minimum temperature | Turkey | RBFNN | RBF Nodes = 5–13 | Mean RMSE (Tmean) = 1.79 °C |

Spread parameter = 0.99 | Mean RMSE (Tmin,Tmax) = 2.20, 2.75 °C | ||||

GRNN | Spread Parameter = 0.05 | Mean RMSE (Tmean) = 1.817 °C | |||

Mean RMSE (Tmin,Tmax) = 2.24, 2.87 °C | |||||

Daily Gust Wind, mean, minimum and maximum DT, | Hidden layer = 1, Hidden Nodes = 6 | ||||

[31] | precipitation, mean humidity, mean pressure, | Iran | MLPNN | Scaled Conjugate Gradient | MAE ≈ 1.7 °C |

sunshine, radiation and evaporation | Activation Function (Hidden/Output) = Tanh-Sig /Pure Linear | ||||

Month of the year, day of the month | Hidden layer = 1, Hidden Nodes = 6, Levenberg–Marquardt | RMSE (train) = 1.85240 °C | |||

[7] | and Mean DT of the previous day | Turkey | MLPNN | Algorithm, Activation Function = Tanh-Sig | RMSE (test) = 1.96550 °C |

[32] | Previous 365 DT | Toronto | MLPNN | Hidden layer (nodes) = 5 (10–16), Levenberg–Marquardt | MSE = 0.201 °C |

Algorithm, Activation Function = Tanh-Sig | |||||

ERNN | Hidden Layers = 1, Hidden Nodes = 15 | MSE (Max DT) = 0.008 °C | |||

Previous Mean, Maximum and | Levenberg-Marqardt Algorithm | MAE (Max DT) = 0.064 °C | |||

[83] | Minimum DT | Iran | MLPNN | Activation Function (hidden) = Tanh-Sig | MSE (Max DT) = 0.008 °C |

Activation Function (output) = Pure Linear | MAE (Max DT) = 0.067 °C | ||||

[84] | Mean DT | Malaysia | JPSN | Hidden Nodes = 2–5, Gradient Descent Algorithm | MSE, MAE = 0.006462, 0.063458 °C |

MLPNN | Activation Function (Hidden/Output) = Sigmoid/Pure Linear | MSE, MAE = 0.006549, 0.063646 °C | |||

[85] | Previous DT | Window Size = 3, Hidden Layers = 2 | MAE = 0.7–0.9 °C | ||

[86] | Previous DT and cloud Density | Taipei | WNN | Feed Forward Back Propagation, Learning Rate = 0.01 | MAE = 0.25–0.62 °C |

Maximum, minimum and average DT, Average and | SVM | Mahalanobis Kernel, $\u03f5=0.1$, $\gamma =0.1$ | MAPE = 2.6% | ||

[36] | Minimum Daily Humidity, Maximum Daily Wind Speed, | Tokyo | MLPNN | Hidden layer = 1, Hidden Nodes = 12, Learning Rate = 0.2 | MAPE = 3.4% |

Daily Wind Direction and Daylight, Daily Isolation | RBFNN | RBF Nodes = 12, Learning Rate = 0.05 | MAPE = 2.7% | ||

[37] | 5 previous values of DT | Cambridge | SVM | Radial Basis Function, Grid Search for optimal $C,\gamma ,\u03f5$ | MSE = 7.15 |

MLPNN | Hidden layer = 1, Hidden Nodes = 2*num_inputs+1 | MSE = 8.07 | |||

Maximum, minimum DT, global radiation, | 10 stations in Europe | SVM | Gaussian Kernel | RMSE (Norway) = 1.5483 °C | |

[38] | precipitation, sea level pressure, relative humidity, | Grid Search for optimal $C,\gamma ,\u03f5$ | |||

synoptic situation and monthly cycle | MLPNN | Levenberg–Marquardt algorithm, Sigmoid Activation Function | RMSE (Norway) = 1.5711 °C | ||

[87] | Previous Minimum DT | Beijing | PSVM | Gaussian Kernel, $\sigma =$ 12.2658, $\gamma =$ 5.5987, ${P}_{size}=$100 | MSE = 1.1026 °C |

SVM | Gaussian Kernel, $\sigma =$ 9.2568,$\gamma =8.9874$ | MSE = 1.3058 °C | |||

K-M+EN | $k\in \{10,17,27\}$ | 1-step MAE(MaxT) = 1.07, (MinT) = 1.15 °C | |||

[88] | +LS-SVM | $v\in \{0.2,0.5,0.8\}$ | 6-step MAE(MaxT) = 1.73, (MinT) = 1.50 °C | ||

Minimum and maximum DT, | LS-SVM | Radial Function Base Kernel | 1-step MAE(MaxT) = 1.35, (MinT) = 1.38 °C | ||

precipitation, humidity, wind | Brussels | Parameter Tuning: Cross-Validation | 6-step MAE(MaxT) = 2.03, (MinT) = 2.34 °C | ||

speed and sea level pressure | ST-LASSO | ${L}_{1}$ Penalization | 1-step MAE(MaxT)=2.11, (MinT)=1.33 °C | ||

+LS-SVM | $v\in \{0.2,0.5,0.8\}$ | 3-step MAE(MaxT) = 2.44, (MinT) = 2.01 °C | |||

[89] | LS-SVM | Radial Function Base Kernel | 1-step MAE(MaxT) = 2.21, (MinT) = 1.38 °C | ||

Parameter Tuning: Cross-Validation | 3-step MAE(MaxT) = 2.40, (MinT) = 2.02 °C | ||||

[41] | Temperature, Wind and Surface Pressure | Zurich | RCNN | 8 Convolutional Filters ($3\times 3$) + | MAE = 0.88 °K |

Max Pooling ($2\times 2$) + 2 LSTM RNN |

Ref. | Input | Output | Region | Algorithm | Configuration | Evaluation Criteria/Time Horizon |
---|---|---|---|---|---|---|

Hidden Layer = 1, Hidden Nodes = 32 | ||||||

[91] | Turkey | MLPNN | Levenberg–Marquardt algorithm | 1-step MAE = 0.508 °C | ||

Latitud, Longitude, | Monthly | Activation Function (Hidden) = Log-Sig | ||||

Altitude, Month | Temperature | Hidden Layers = 1, Hidden Nodes = 15 | Station with Min RMSE = 1.53 °C | |||

[92] | Iran | MLPNN | Levenberg-Marqardt Algorithm | Station with Min MAE = 1.27 °C | ||

Activation Function = Tanh-Sig | ||||||

January to May | Max and Min | Hidden Layer = 1, Hidden Nodes = 2 | June MAE(Tmin, Tmax) = 0.0154, 0.0197 °C | |||

[93] | maximum and minimum | Monthly | India | MLPNN | Steepest Descent algorithm | July MAE (Tmin, Tmax) = 0.0107, 0.0162 °C |

temperature | Temperature | Learning rate = 0.9 | Aug MAE (Tmin, Tmax) = 0.01013, 0.0099 °C | |||

For 1, 6, 12 and 24 months before: | BP- | Not Specified | MSE (Testing) = 0.0196 °C | |||

Mean temperature, dew point | MLPNN | |||||

[90] | temperature, relative humidity, | Monthly | Iran | GA- | Not Specified | MSE (Testing) = 0.0224 °C |

wind speed, solar radiation, | Temperature | MLPNN | ||||

cloudiness, rainfall, station level | PSO- | Not Specified | MSE (Testing) = 0.0228 °C | |||

pressure and green house gases | MLPNN | |||||

Monthly | ERNN | Hidden Layers = 1, Hidden Nodes = 15 | 1-step MSE (Tmin, Tmax) = 0.081, 0.060 °C | |||

Previous Mean, Maximum and | Mean, | Levenberg-Marqardt Algorithm | 1-step MAE (Tmin, Tmax) = 0.228, 0.193 °C | |||

[83] | Minimum Temperature | Max, and | Iran | MLPNN | Activation Function (hidden) = Tanh-Sig | 1-step MSE (Tmin, Tmax) = 0.083, 0.064 °C |

Min Temperature | Activation Function (output) = Linear | 1-step MAE (Tmin, Tmax) = 0.223, 0.201 °C | ||||

WT+ | $C=$ 10–20, $\u03f5=$ 0.1–0.5 | Min. MSE = 0.0937 °C | ||||

SVM | $\sigma =$ 0.05–0.55, Radial basis Kernel | |||||

[94] | Mean Monthly Temperature | Monthly | Tangshan | $C=$ 10–20, $\u03f5=$ 0.1–0.5 | Min. MSE = 0.5451 °C | |

Temperature | SVM | σ 0.05–0.55, Radial basis Kernel | ||||

Not Specified | Min. MSE = 1.0076 °C | |||||

MLPNN | ||||||

SVM | Gaussian Kernel | Mean MAE = 1.0073 °C | ||||

Monthly | Australia | Grid Search for optimal $C,\gamma ,\u03f5$ | ||||

[95] | Mean Monthly Temperature | Temperature | and New | MLPNN | Levenberg– Marquardt algorithm | 1-step Mean MAE = 1.0662 °C |

Zealand | Activation Function = Logistic | |||||

SVM | Gaussian Kernel | 1-step Mean RMSE = 1.31 °C | ||||

Monthly | $C=1$ and $\u03f5=0.1$ | |||||

[96] | Mean Monthly Temperature | Temperature | Greece | MLPNN | Hidden Layers = 1, Hidden Nodes = 5 | Mean RMSE = 1.7 °C |

Activation Function = Logistic |

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**MDPI and ACS Style**

Cifuentes, J.; Marulanda, G.; Bello, A.; Reneses, J.
Air Temperature Forecasting Using Machine Learning Techniques: A Review. *Energies* **2020**, *13*, 4215.
https://doi.org/10.3390/en13164215

**AMA Style**

Cifuentes J, Marulanda G, Bello A, Reneses J.
Air Temperature Forecasting Using Machine Learning Techniques: A Review. *Energies*. 2020; 13(16):4215.
https://doi.org/10.3390/en13164215

**Chicago/Turabian Style**

Cifuentes, Jenny, Geovanny Marulanda, Antonio Bello, and Javier Reneses.
2020. "Air Temperature Forecasting Using Machine Learning Techniques: A Review" *Energies* 13, no. 16: 4215.
https://doi.org/10.3390/en13164215