Estimation of Groundwater Level Fluctuations Using Neuro-Fuzzy and Support Vector Regression Models

Abstract

Estimation of Ground Water Level (GWL) is important in the determination of the sustainable use of water resources and Ground Water resources. Groundwater level fluctuations were investigated using the variable of groundwater level, precipitation, temperature. In the present study, GWL estimation studies were conducted via Neuro-Fuzzy (NF), Support Vector Regression with radial basis functions (SVR-RBF) and Support Vector Regression with poly kernel (SVR-PK) models. The daily data of the precipitation, temperature and groundwater level are used which is taken from Minnesota, United States of America. The results were compared with NF and SVR methods. According to this comparison, it was observed that the NF and SVR models gave similar results for observation.

Forecasting of River Sediment Amount using Machine Model

“…For these reasons, the estimation of sediment amount is needed in the design of water structures. In the last years, the artificial intelligence approaches are a technique widely used in water resources engineering and hydrology [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. Thangaraj and Kalaivani [18] estimated the water level in the river using support vector machines.…”

Ground Water Level Estimation for Dörtyol region in HATAY

“…it becomes a valuable tool for complex scenarios, which are difficult to define by methods.. Recently, artificial intelligence methods have begun to be frequently used in modeling the rainfallrunoff [1][2], suspended sediment [3][4][5][6], dam reservoir level [7][8][9][10], density flow plunging [11], dam reservoir volume [12][13][14][15], sand bar crest [16], evaporation [17][18], and groundwater level [19][20][21][22][23][24][25].…”

Artificial Neural Networks Method for Prediction of Rainfall-Runoff Relation: Regional Practice

“…Thus, the basin, which will be measured, is examined with some equations with these accumulation systems and other effective parameters. Recently, artificial intelligence method is a Black Box model that is frequently used in modeling the groundwater level (Demirci et al (2017(Demirci et al ( , 2018a, Üneş et al (2018a), Kaya et al (2018)), suspended sediment (Demirci and Baltaci (2013), , Tasar et al ( 2017)), rainfallrunoff relationship (Ünes et al (2018b), Tasar et al ( 2019)), dam reservoir and lake level (Ünes and Demirci (2015), Ünes et al (2015, 2019a), Demirci and Kaya (2019)), density flow plunging (Üneş (2010)), dam reservoir volume (Unes et al (2017), Demirci et al (2018b), Üneş et al (2019b)), sand bar crest ), evaporation (Üneş et al (2018c(Üneş et al ( ), Tasar et al (2018, Kaya and Tasar (2019)), many different disciplines-areas (Cansiz et al (2009(Cansiz et al ( , 2017a(Cansiz et al ( , 2017b(Cansiz et al ( , 2017c, Dogan et al (2017), Cansiz (2007Cansiz ( , 2011, Cansiz and Easa (2011), Cansız and Askar (2018), Dal et al (2019)).…”