Self-scheduling approach to coordinating wind power producers with energy storage and demand response. IEEE transactions on sustainable energy.

No abstract

Land use land cover change mapping has been used for monitoring environmental changes as an essential factor to study on the earth's surface land cover in the field of climate change phenomena such as floods and droughts. Remote sensing images have been suggested to present inexpensive and fine-scale data offering multi-temporal coverage. This tool is useful in the field of environmental monitoring, land-cover mapping, and urban planning. This study aims to evaluate eight machine learning algorithms for image classification implemented in WEKA and R programming language. Firstly, Landsat 8 OLI/TIRS Level-2 images based on eight machine learning techniques including Random Forest, Decision Table, DTNB, J48, Lazy IBK, Multilayer Perceptron, Non-Nested Generalized Exemplars (NN ge), and Simple Logistic are classified. Then, obtained results are compared in term of Overall Accuracy (OA), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) for land use land cover mapping. Among the eight machine learning algorithms used for image classification based on the training and test dataset, NN ge classifier is ranked first with values of 100, 0, and 0 for Overall Accuracy, Mean Absolute Error and Root Mean Squared Error respectively. All machine learning algorithms had an Overall Accuracy of more than 99% for the training dataset. On the other hand, for the test dataset, J48 and DTNB algorithms had the worst performance with values of 88.1188 and 76.9802 respectively for the Overall Accuracy.

Marine debris is considered a threat to the inhabitants, as well as the marine environments. Accumulation of marine debris, besides climate change factors, including warming water, sea-level rise, and changes in oceans’ chemistry, are causing the potential collapse of the marine environment’s health. Due to the increase of marine debris, including plastics in coastlines, ocean and sea surfaces, and even in deep ocean layers, there is a need for developing new advanced technology for the detection of large-sized marine pollution (with sizes larger than 1 m) using state-of-the-art remote sensing and machine learning tools. Therefore, we developed a cloud-based framework for large-scale marine pollution detection with the integration of Sentinel-2 satellite imagery and advanced machine learning tools on the Sentinel Hub cloud application programming interface (API). Moreover, we evaluated the performance of two shallow machine learning algorithms of random forest (RF) and support vector machine (SVM), as well as the deep learning method of the generative adversarial network-random forest (GAN-RF) for the detection of ocean plastics in the pilot site of Mytilene Island, Greece. Based on the obtained results, the shallow algorithms of RF and SVM achieved an overall accuracy of 88% and 84%, respectively, with available training data of plastic debris. The GAN-RF classifier improved the detection of ocean plastics of the RF method by 8%, achieving an overall accuracy of 96% by generating several synthetic ocean plastic samples.

The use of machine learning algorithms to classify complex landscapes has been revolutionized by the introduction of deep learning techniques, particularly in remote sensing. Convolutional neural networks (CNNs) have shown great success in the classification of complex high-dimensional remote sensing imagery, specifically in wetland classification. On the other hand, the state-of-the-art natural language processing (NLP) algorithms are transformers. Although the transformers have been studied for a few remote sensing applications, the integration of deep CNNs and transformers has not been studied, particularly in wetland mapping. As such, in this study, we explore the potential and possible limitations to be overcome regarding the use of a multi-model deep learning network with the integration of a modified version of the well-known deep CNN network of VGG-16, a 3D CNN network, and Swin transformer for complex coastal wetland classification. Moreover, we discuss the potential and limitation of the proposed multi-model technique over several solo models, including a random forest (RF), support vector machine (SVM), VGG-16, 3D CNN, and Swin transformer in the pilot site of Saint John city located in New Brunswick, Canada. In terms of F-1 score, the multi-model network obtained values of 0.87, 0.88, 0.89, 0.91, 0.93, 0.93, and 0.93 for the recognition of shrub wetland, fen, bog, aquatic bed, coastal marsh, forested wetland, and freshwater marsh, respectively. The results suggest that the multi-model network is superior to other solo classifiers from 3.36% to 33.35% in terms of average accuracy. Results achieved in this study suggest the high potential for integrating and using CNN networks with the cutting-edge transformers for the classification of complex landscapes in remote sensing.