Enhancing monthly lake levels forecasting using heuristic regression techniques with periodicity data component: application of Lake Michigan

Vahdettin Demir

doi:10.1007/s00704-022-03982-0

Abstract

This study investigates the accuracy of three different techniques with the periodicity component for estimating monthly lake levels. The three techniques are multivariate adaptive regression splines (MARS), least-square support vector regression (LSSVR), and M5 model tree (M5-tree). Data from Lake Michigan, located in the USA, is used in the analysis. In the first stage of modeling, three techniques were applied to forecast monthly lake level fluctuations up to 8 months ahead of time intervals. In the second stage, the influence of the periodicity component was applied (month number of the year, e.g., 1, 2, 3, …12) as an external subset in modeling monthly lake levels. The root-mean-square error, mean absolute error, and coefficient of determination were used for evaluating the accuracy of the models. In both stages, the comparison results indicate that MARS generally outperforms LSSVR and M5-tree. Further, it has been discovered that including periodicity as an input to the models improves their accuracy in projecting monthly lake levels.

Highlights

Lake level fluctuations are significant for lakeshore structure planning, design, building, and operation, as well as for the management of fresh water lakes for water supply purposes
The three heuristic regression techniques evaluated (MARS, M5 Tree, and Least Square Support Vector Regression (LSSVR)) were created using MATLAB subroutines to estimate the compressive strength of foamed concrete
The results of heuristic regression techniques in term of Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and R2 are given in Table 2 with input combinations

Summary

Introduction

Lake level fluctuations are significant for lakeshore structure planning, design, building, and operation, as well as for the management of fresh water lakes for water supply purposes. In order to regulate future lake level changes, models for modeling of high or abnormal level variations must be developed. The level measurements, or their future likely reproductions acquired by a simulation model, are a straightforward manner of getting lake management decision variables. Comprehensive models incorporating hydrological and hydrometeorological variables such as precipitation, runoff, temperature, and evaporation can be found, it is more economically advantageous to use a model that simulates level variations based on past level records [1]. Forecasting lake water levels is important for water resource planning and management, lake navigation, tidal irrigation, and agricultural drainage canal management, among other things. A model that predicts water-level changes based on previously measured levels is required [4]

Objectives

Methods

Results

Conclusion