As shown in Stogryn et al. (1994), Krasnopolsky et al. (1995), Thiria et al. (1993), Cornford et al. (2001), and many other studies summarized in Chapter 9, neural networks (NN) can be used to emulate the physically-based retrieval algorithms traditionally used to estimate geophysical parameters from satellite measurements. The tradeoff involved is a minor sacrifice in accuracy for a major gain in speed, an important factor in operational data analysis. This chapter will cover the design and development of such networks, illustrating the process by means of an extended example. The focus will be on the practical issues of network design and troubleshooting. Two topics in particular are of concern to the NN developer: computational complexity and performance shortfalls. This chapter will explore how to determine the computational complexity required for solving a particular problem, how to determine if the network design being validated supports that degree of complexity, and how to catch and correct problems in the network design and developmental data set.
As discussed in Chapter 9, geophysical remote sensing satellites measure either radiances using passive radiometers or backscatter using a transmitter/receiver pair. The challenge is then to estimate the geophysical parameters of interest from these measured quantities. The physics-based forward problem (equation 9.4) captures the cause and effect relationship between the geophysical parameters and the satellite-measured quantities. Thus, the forward problem must be a single-valued function (i.e. have a single possible output value for each set of input values) if we have access to all of its input parameters. As a result, the forward problem is generally well-posed, i.e. variations in the input parameters are not grossly amplified in the output. One could, however, imagine some geophysical processes for which the forward problem was ill-posed for some parameter values as a result of a sudden transition from one regime of behavior to another (e.g. the onset of fog formation producing a sharp change in shortwave albedo in response to a minor change in air temperature).
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References
Alpsan, D., Towsey, M., Ozdamar, O., Tsoi, A., & Ghista, D. (1995). Are modified back-propagation algorithms worth the effort? IEEE International Conference on Neural Networks, Orlando, USA, 1, 567–571
Cornford, D., Nabney, I. T., & Ramage, G. (2001). Improved neural network scatterometer forward models. Journal of Geophysical Research 106, 22331–22338
Dodd, N. (1990). Optimization of network structure using genetic techniques. Proceedings of the International Joint Conference on Neural Networks, Washington D.C., USA, 1, 965–970
Haupt, R., & Haupt, S. (2004). Practical genetic algorithms (2nd ed., 253 pp.). Hoboken, NJ: Wiley
Jones, A. (1993). Genetic algorithms and their applications to the design of neural networks. Neural Computing and Applications, 1, 32–45
Knuth, D. (1997). The art of computer programming, volume 3: Sorting and searching (3rd ed., 780 pp.). Reading, MA: Addison-Wesley
Krasnopolsky, V., Breaker, L., & Gemmill, W. H. (1995). A neural network as a nonlinear transfer function model for retrieving surface wind speeds from the special sensor microwave imager. Journal of Geophysical Research 100, 11033–11045
Monaldo, F., Thompson, D., Beal, R., Pichel, W., & Clemente-Colón, P. (2001). Comparison of SAR-derived wind speed with model predictions and ocean buoy measurements. IEEE Transactions on Geoscience and Remote Sensing, 39, 2587– 2600
Munro, P. (1993). Genetic search for optimal representations in neural networks. In R. Albrecht, C. Reeves, & N. Steele (Eds.),Artificial neural nets and genetic algorithms. Proceedings of the international conference (pp. 628–634). Innsbruck, Austria: Springer
Nelder, J., & Mead, R. (1965). A simplex method for function minimization. Computer Journal, 7, 308–313
Reed, R., & Marks, R. (1999). Neural smithing: Supervised learning in feedforward artificial neural networks (346 pp.). Cambridge, MA: MIT Press
Stoffelen, A., & Anderson, D. (1997a). Scatterometer data interpretation: Measurement space and inversion. Journal of Atmospheric and Oceanic Technology, 14, 1298–1313
Stoffelen, A., & Anderson, D. (1997b). Scatterometer data interpretation: Estimation and validation of the transfer function CMOD4. Journal of Geophysical Research, 102, 5767– 5780
Stogryn, A. P., Butler, C. T., & Bartolac, T. J. (1994). Ocean surface wind retrievals from special sensor microwave imager data with neural networks. Journal of Geophysical Research, 90, 981–984
Thiria, S., Mejia, C., Badran, F., & Crepon, M. (1993). A neural network approach for modeling nonlinear transfer functions: Application for wind retrieval from spaceborne scatterometer data. Journal of Geophysical Research, 98, 22827–22841
Witten, I., & Frank, E. (2005). Data mining: Practical machine learning tools and techniques (2nd ed., 525 pp.). San Francisco: Morgan Kaufmann
Yamada, T., & Yabuta, T. (1993). Remarks on neural network controller which uses genetic algorithm. Proceedings of International Joint Conference on Neural Networks (pp. 2783–2786). Japan: Nagoya
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Young, G.S. (2009). Implementing a Neural Network Emulation of a Satellite Retrieval Algorithm. In: Haupt, S.E., Pasini, A., Marzban, C. (eds) Artificial Intelligence Methods in the Environmental Sciences. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-9119-3_10
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