Comparison study of artificial intelligence method for short term groundwater level prediction in the northeast Gachsaran unconfined aquifer

Abstract

Accurate and reliable groundwater level prediction is an important issue in groundwater resource management. The objective of this research is to compare groundwater level prediction of several data-driven models for different prediction periods. Five different data-driven methods are compared to evaluate their performances to predict groundwater levels with 1-, 2- and 3-month lead times. The four quantitative standard statistical performance evaluation measures showed that while all models could provide acceptable predictions of groundwater level, the least square support vector machine (LSSVM) model was the most accurate. We developed a set of input combinations based on different levels of groundwater, total precipitation, average temperature and total evapotranspiration at monthly intervals. For each model, the antecedent inputs that included Ht-1, Ht-2, Ht-3, Tt, ETt, Pt, Pt-1 produced the best-fit model for 1-month lead time. The coefficient of determination (R2) and the root mean square error (RMSE) were calculated as 0.99%, 1.05 meters for the train data set, and 95%, 2.3 meters for the test data set, respectively. It was also demonstrated that many combinations the above-mentioned approaches could model groundwater levels for 1 and 2 months ahead appropriately, but for 3 months ahead the performance of the models was not satisfactory.

Evaluating the Impacts of Pumping on Aquifer Depletion in Arid Regions Using MODFLOW, ANFIS and ANN

“…This is further strengthened by Figure 11d that shows a very low mean square error in the range of 0.02-0.03. Similar results were observed by previous studies [30,41,42,49]. This is most probably because a stronger optimization scheme was used in the scaled conjugate gradient to get a better global minimum with the minimum possible number of trials [16,54].…”

“…Hence, three ANN and an ANFIS models were investigated in this study and the best one was used for long-term predictions of groundwater-levels. It is worth mentioning here that most of the modelling based on ANN/ANFIS in past research dealt with only the short-term predictions of groundwater-levels [34][35][36][37]41,42]. Khedri et al [42].…”

Imputation of Contiguous Gaps and Extremes of Subhourly Groundwater Time Series Using Random Forests

“…More recently, machine-learning-based approaches have gained momentum for imputing the GW time series because of their flexibility and versatility in dealing with diverse data. Khedri et al (2020) compared several machine-learning (ML) approaches, including artificial neural networks, fuzzy logic, adaptive neuro-fuzzy inference system, neural net group method of data handling, and support vector machines, for short-term (one to three months) groundwater level predictions. They considered precipitation, temperature, and evapotranspiration as input features for estimating monthly missing GW data.…”

Application of Machine Learning and Process-Based Models for Rainfall-Runoff Simulation in DuPage River Basin, Illinois

“…ML provides adequate computation power [29,30] and is used in a wide variety of research and applications in hydrology. Some examples of ML applications in the hydrology domain are rainfall-runoff prediction [31][32][33], flood forecasting [34][35][36], sedimentation studies [37][38][39], water quality prediction [40][41][42][43], groundwater prediction [44,45], river temperature prediction [46][47][48][49], and rainfall estimation [50,51]. In recent years, ML algorithms have significantly improved and are also widely used for rainfall-runoff simulation [52,53] thanks to the rapid advancement of computer technology.…”