Summary Dust, or particulate matter (PM2.5), is among the most harmful pollutants negatively affecting human health. Predicting indoor PM2.5 concentrations is essential to achieve acceptable indoor air quality. This study aims to investigate data‐driven models to accurately predict PM 2.5 pollution. Notably, a comparative study has been conducted between twenty‐one machine learning and deep learning models to predict PM2.5 levels. Specifically, we investigate the performance of machine learning and deep learning models to predict ambient PM2.5 concentrations based on other ambient pollutants, including SO, NO, O, CO, and PM10. Here, we applied Bayesian optimization to optimally tune hyperparameters of the Gaussian process regression with different kernels and ensemble learning models (i.e., boosted trees and bagged trees) and investigated their prediction performance. Furthermore, to further enhance the forecasting performance of the investigated models, dynamic information has been incorporated by introducing lagged measurements in the construction of the considered models. Results show a significant improvement in the prediction performance when considering dynamic information from past data. Moreover, three methods, namely, random forest (RF), decision tree, and extreme gradient boosting, are applied to assess variables contribution and revealed that lagged PM2.5 data contribute significantly to the prediction performance and enables the construction of parsimonious models. Hourly concentration levels of ambient air pollution from the air quality monitoring network located in Seoul are employed to verify the prediction effectiveness of the studied models. Six measurements of effectiveness are used for assessing the prediction quality. Results showed that deep learning models are more efficient than the other investigated machine learning models (i.e., SVR, GPR, bagged and boosted trees, RF, and XGBoost). Also, the results showed that the bidirectional long short term memory (BiLSTM) and bidirectional gated recurrent units (BiGRU) networks produce higher performance than the investigated machine learning models (i.e., SVR, GPR, bagged and boosted trees, RF, and XGBoost) and deep learning models (i.e., LSTM, GRU, and convolutional neural network).

Summary Precisely detecting land cover changes aids in improving the analysis of the dynamics of the landscape and plays an essential role in mitigating the effects of desertification. Mainly, sensing desertification is challenging due to the high correlation between desertification and like‐desertification events (e.g., deforestation). An efficient and flexible deep learning approach is introduced to address desertification detection through Landsat imagery. Essentially, a generative adversarial network (GAN)‐based desertification detector is designed and for uncovering the pixels influenced by land cover changes. In this study, the adopted features have been derived from multi‐temporal images and incorporate multispectral information without considering image segmentation preprocessing. Furthermore, to address desertification detection challenges, the GAN‐based detector is constructed based on desertification‐free features and then employed to identify atypical events associated with desertification changes. The GAN‐detection algorithm flexibly learns relevant information from linear and nonlinear processes without prior assumption on data distribution and significantly enhances the detection's accuracy. The GAN‐based desertification detector's performance has been assessed via multi‐temporal Landsat optical images from the arid area nearby Biskra in Algeria. This region is selected in this work because desertification phenomena heavily impact it. Compared to some state‐of‐the‐art methods, including deep Boltzmann machine (DBM), deep belief network (DBN), convolutional neural network (CNN), as well as two ensemble models, namely, random forests and AdaBoost, the proposed GAN‐based detector offers superior discrimination performance of deserted regions. Results show the promising potential of the proposed GAN‐based method for the analysis and detection of desertification changes. Results also revealed that the GAN‐driven desertification detection approach outperforms the state‐of‐the‐art methods.

Accurately forecasting solar irradiance is indispensable in optimally managing and designing photovoltaic systems. It enables the efficient integration of photovoltaic systems in the smart grid. This paper introduces an innovative deep attention-driven model for solar irradiance forecasting. Notably, an extended version of the variational autoencoder (VAE) is introduced by amalgamating the desirable characteristics of the bidirectional LSTM (BiLSTM) and attention mechanism with the VAE model. Specifically, the introduced approach enables the conventional VAE’s ability to model temporal dependencies by incorporating BiLSTM at the VAE’s encoder side to better extract and learn temporal dependencies embed on the solar irradiance concentration measurements. In addition, the self-attention mechanism is embedded in the VAE’s encoder side following the BiLSTM to highlight pertinent features. The performance of the proposed model is evaluated through comparisons with the recurrent neural network (RNN), gated recurrent unit (GRU), LSTM, and BiLSTM. Measurements of solar irradiance in the US and Turkey are used to evaluate the investigated models. Results confirm the superior performance of the proposed model for solar irradiance forecasting over the other models (i.e., RNN, GRU, LSTM, and BiLSTM).