Machine learning approaches are increasingly used to extract patterns and insights from the ever-increasing stream of geospatial data, but current approaches may not be optimal when system behaviour is dominated by spatial or temporal context. Here, rather than amending classical machine learning, we argue that these contextual cues should be used as part of deep learning (an approach that is able to extract spatio-temporal features automatically) to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales, for example. The next step will be a hybrid modelling approach, coupling physical process models with the versatility of data-driven machine learning.

/pdf/deep-learning-and-process-understanding-for-data-driven-3so9cx99my.pdf

Deep learning and process understanding for data-driven Earth system science

Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks

https://escholarship.org/content/qt48x0t0b1/qt48x0t0b1.pdf?t=q8he9y

Modeling and simulating of reservoir operation using the artificial neural network, support vector regression, deep learning algorithm.

Use Long Short-Term Memory to Enhance Internet of Things for Combined Sewer Overflow Monitoring

Time-series forecasting is applied to many areas of smart factories, including machine health monitoring, predictive maintenance, and production scheduling. In smart factories, machine speed prediction can be used to dynamically adjust production processes based on different system conditions, optimize production throughput, and minimize energy consumption. However, making accurate data-driven machine speed forecasts is challenging. Given the complex nature of industrial manufacturing process data, predictive models that are robust to noise and can capture the temporal and spatial distributions of input time-series signals are prerequisites for accurate forecasting. Motivated by recent deep learning studies in smart manufacturing, in this article, we propose an end-to-end model for multistep machine speed prediction. The model comprises a deep convolutional LSTM encoder–decoder architecture. Extensive empirical analyses using real-world data obtained from a metal packaging plant in the United Kingdom demonstrate the value of the proposed method when compared with the state-of-the-art predictive models.

/pdf/a-deep-learning-model-for-smart-manufacturing-using-3zqpl67wdb.pdf

A Deep Learning Model for Smart Manufacturing Using Convolutional LSTM Neural Network Autoencoders

The aim of this paper is to present a deep neural network architecture and use it in time series weather prediction. It uses multi stacked LSTMs to map sequences of weather values of the same length. The final goal is to produce two types of models per city (for 9 cities in Morocco) to forecast 24 and 72 hours worth of weather data (for Temperature, Humidity and Wind Speed). Approximately 15 years (2000-2015) of hourly meteorological data was used to train the model. The results show that LSTM based neural networks are competitive with the traditional methods and can be considered a better alternative to forecast general weather conditions.

/pdf/sequence-to-sequence-weather-forecasting-with-long-short-4jp0ep5ntt.pdf

Sequence to Sequence Weather Forecasting with Long Short-Term Memory Recurrent Neural Networks

This project brings together a group of world class experts from research partner institutions in three countries: Turkey, Morocco and the USA, to plan and implement the North Atlantic Treaty Organization (NATO) Science for Peace sponsored Mediterranean Dialogue Earth Observatory (MDEO).

Development of a real-time urban remote sensing initiative in the mediterranean region for early warning and mitigation of disasters

Cloud computing is an emerging IT technology that is being used increasingly in industry, government and academia. There are several Cloud Computing middleware solutions available in the market. This paper proposes an approach and set of characteristics and metrics for comparing Cloud computing middleware based on functionality. Three popular open source middleware: Nimbus, Eucalyptus and Open Nebula, are also analyzed and evaluated on the basis of the proposed parameters. Future work will include more systems and an experimental benchmarking to study the relative performance.

A Compartive Study of Cloud Computing Middleware

A Mediterranean Dialogue Earth Observatory project is under implementation in Morocco. It is led by a group of experts from Turkey, Morocco and the USA. The objective of the project is to facilitate early warning and mitigation of a wide range of biogenic and anthropogenic disasters using remote sensing techniques. Some examples are flooding, storms, forest fires, climate change, recent public health incidents, such as malaria and avian influenza. The observatory comprises a network of real-time satellite remote sensing ground stations, installed at two universities in Morocco, Abdelmalek Essadi and AlAkhawayn, with a geostationary system to collect data from Meteosat geostationary satellite and a tracking station for polar orbiting satellites. The infrastructure includes as well the post processing computer clusters and relevant storage, software and distributional network. Archival and real-time remote sensing utilizing high performance computing clusters, are planned throughout the life cycle of disaster management. In this paper, a description of ground stations will be given, as well as the antennas connectivity and the access to data from partnering universities and collaborating end-users.

Chaker El Amrani

Papers

Sequence to Sequence Weather Forecasting with Long Short-Term Memory Recurrent Neural Networks

Development of a real-time urban remote sensing initiative in the mediterranean region for early warning and mitigation of disasters

A Compartive Study of Cloud Computing Middleware

System architecture of the Mediterranean Dialogue Earth Observatory

Approaches for High-Performance Big Data Processing: Applications and Challenges.