Application of statistical techniques in environmental modelling

Abstract

The aim of statistical modeling is to predict with good accuracy the variable of interest and sometimes to determine the contribution of the independent variables towards the dependent variable. Over the years, researches have always tried to produce better models in order to increase the accuracy of their predictions. This paper will discuss some statistical techniques that were used frequently in environmental research as well as recent techniques and advances in statistical modelling. The first technique that will be discussed is the multiple linear regression (MLR) models. Some other regression models will also be given. Next probability distributions will be discussed. This is to obtain statistical distributions that best fit the variable of interest. This will enable the researcher to predict the return period of an event. The application of Bayesian statistics has increased over the years. The used of least squares method in MLR and hypothesis testing is known as classical statistical techniques. The Bayesian statistics is based on the probability distributions of the prior and likelihood of the Bayes theorem. The results of using classical and Bayesian statistics will be discussed. Machine learning method will be deliberated next. Machine learning is the application of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being programmed explicitly. Some machine learning methods that will be discussed are boosted regression trees, artificial neural networks and support vector machines. Finally, hybrid models that are the combination of two statistical models will be deliberated next. It is hoped that by combining two statistical models, a more accurate and improved model will be obtained.