Discuss ideas about how machine learning algorithm can be used and to predict friction during unexpected climatic changes.
Using machine learning algorithm to predict friction
Climate change is becoming a major area of concern and the need of advanced methods to predict different changes is required. Deep machine learning approaches have been used for a while to predict tropical cyclones, atmospheric rivers as well as weather fronts (Basara and Williams, 2011). The deep learning systems are able to develop their own rules and thus the algorithms are able to enhance the effectiveness of the predictions. Machine learning algorithms are a key artificial intelligence sub-field, which can be use d to enhance the prediction of friction during unexpected climate changes.
It is important to enhance prediction of the friction during unexpected weather conditions. Machine learning algorithms first relies on collection of real time data. The algorithms are able to identify the conditions on the ground and this helps to predict the friction when unexpected weather conditions do happen (Horenko, Klein, Dolaptchiev, and Schutte, 2008). Defining a perfect function to define the climatic changes will be able to help in reduction of the friction experienced. The functions help to identify the different datasets and thus helps in predicting reduction of the friction when unexpected climatic conditions do happen (Mittelman, Kuipers, Savarese, and Lee, 2014). The machine learning algorithms is able to involve different algorithms which are able to form the function. Moreover, increasing the number of algorithms in order to ensure that the different situations are captured will be able to reduce the frictions. The presence of these different algorithms is able to enhance the prediction of frictional existence when these events happen. The algorithms are able to enhance the efficiency of the machine learning algorithms and their functionality. Most importantly, the use of the different algorithms is able to enhance the prediction of the different changes which are able to happen.
In addition, the machine learning algorithm has the spatial interpolation capability. This factor is able to influence the atmospheric laws on weather and therefore able to enhance the predictions. In conjunction with the algorithms, the spatial interpolations are able to come up with different changes and their levels of change (McGovern, John Gagne, Troutman, Brown, Basara, and Williams, 2011). Although the learning is able to rely on different changes which have been able to happen in the past, the spatial interpolation ensures that the prediction of the friction is well identified when unexpected changes do occur (Basara and Williams, 2011). Increasing the level of spatial interpolation is able to ensure that the capture of real time data and situation sis able to happen. This will help a lot in predicting the friction which happens due to the presence of the different climatic condition. Integrating the algorithms to capture the data from the available satellites will be able to enhance the frequency of high level data over a region.
In addition, the time of events is important to ensure that the prediction of the friction is enhanced. In cooperating the factor of time distant in the machine learning algorithm will be able to help in enhancing the frictional measurement (Sutskever, Hinton, and Taylor, 2009). This will be able to increase the knowledge of the happening of the abrupt weather in future and enhance the understanding of the events. In cooperating the use of sensors will also be important in understanding the unexpected climate changes in different regions. The machine learning algorithm need to have the key sensors which indicate the abrupt changed to help in the planning of the events.
Moreover, much of the friction raise from the inability to understand the working of the machine learning algorithms. At many times, many people are able to make their own decisions in the climate change without the consideration of the machine learning algorithms results. This is able to create a large gap of friction when predicting the climate changes (Chen, and Lai, 2011). To enhance the effectiveness of the frictional prediction of the unexpected climate change, it is important for the different people to understand the working of the machine learning algorithms. This will help in the rational decision making for the different people and thus improve the handling of the unexpected climate change.
Additionally, the connectivity and manipulation of the machine learning algorithms is one of the key challenges which leads to the failure of proper frictional prediction. The machine learning algorithm will detect what the human factor needs to be detected. The human manipulation of the machine need to have a high degree of control such that the machine has a specific factors of definition (G¨onen, and Alpayd?n, 2011). This means that the machine learning algorithm need to be able to identify the key information using the ground conditions of climate change in order to provide effective results without human control.
Basara, J. and Williams J. K. (2011). Using spatiotemporal relational random forests to improve our understanding of severe weather processes. Statistical Analysis and Data Mining: The ASA Data Science Journal, 4(4):407–429.
Chen, L. and Lai X. (2011). Comparison between ARIMA and ANN models used in short-term wind speed forecasting. In Power and Energy Engineering Conference (APPEEC), 2011 Asia-Pacific, pages 1–4.
G¨onen, M. and Alpayd?n E. (2011). Multiple kernel learning algorithms. The Journal of Machine Learning Research, 12:2211–2268.
Horenko, I. Klein, R. Dolaptchiev, S. and Schutte C. (2008). Automated Generation of Reduced Stochastic Weather Models I: simultaneous dimension and model reduction for time series analysis. Multiscale Modeling & Simulation, 6(4):1125–1145.
McGovern, A. John Gagne, D. Troutman, N. Brown, R. A.Basara, J. and Williams J. K. (2011). Using spatiotemporal relational random forests to improve our understanding of severe weather processes. Statistical Analysis and Data Mining: The ASA Data Science Journal, 4(4):407–429.
Mittelman, R. Kuipers, B. Savarese, S. and Lee H. (2014). Structured Recurrent Temporal Restricted Boltzmann Machines. In Proceedings of the 31st International Conference on Machine Learning (ICML), pages 1647–1655.
Sutskever, I. Hinton, G. E. and Taylor G. W. (2009). The Recurrent Temporal Restricted Boltzmann Machine. In Advances in Neural Information Processing Systems, pages 1601–1608.