BOOK-CHAPTER

Spatio-Temporal Prediction Using Data Mining Tools

Abstract

The spatio-temporal prediction problem requires that one or more future values be predicted for time series input data obtained from sensors at multiple physical locations. Examples of this type of problem include weather prediction, flood prediction, network traffic flow, and so forth. In this chapter we provide an overview of this problem, highlighting the principles and issues that come to play in spatio-temporal prediction problems. We describe some recent work in the area of flood prediction to illustrate the use of sophisticated data mining techniques that have been examined as possible solutions. We argue the need for further data mining research to attack this difficult problem. This chapter is directed toward professionals and researchers who may wish to engage in spatio-temporal prediction.

Keywords:
Computer science Data mining Flood myth Data science Time series Machine learning Geography

Metrics

2
Cited By
0.50
FWCI (Field Weighted Citation Impact)
0
Refs
0.66
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Topics

Hydrological Forecasting Using AI
Physical Sciences →  Environmental Science →  Environmental Engineering
Data Mining Algorithms and Applications
Physical Sciences →  Computer Science →  Information Systems
Time Series Analysis and Forecasting
Physical Sciences →  Computer Science →  Signal Processing

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