Application of NN-ARX Model to Predict Groundwater Levels in the Neishaboor Plain, Iran

Izady, Azizallah; Davary, Kamran; Alizadeh, Amin; Nia, Alireza Moghaddam; Ziaei, Ali Naghi; Hasheminia, Seyed Majid

doi:10.1007/s11269-013-0432-y

Cited by 38 publications

(12 citation statements)

References 33 publications

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“…The results of the NARX modeling approach obtained in this study generally agree with the previously conducted research in this field (e.g., [11][12][13][14][41][42][43][44]). In particular, the results confirm the suitability of NARX in efficiently modeling groundwater level fluctuations.…”

Section: Comparisons With Previous Studiessupporting

confidence: 90%

“…Further, numerous studies have focused on the use of ANNs to model groundwater level fluctuation. However, only a small number of studies emphasize utilizing ANNs for modeling groundwater time series using a non-linear autoregressive approach with exogenous input (NARX) [10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…Izady et al [11] investigated the application of NARXs in forecasting monthly groundwater levels in six observation wells in the arid Neishaboor plain, Iran. The selected study area lies on an agricultural watershed, where most groundwater extractions are used for irrigation.…”

Section: Introductionmentioning

confidence: 99%

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A Nonlinear Autoregressive Modeling Approach for Forecasting Groundwater Level Fluctuation in Urban Aquifers

Alsumaiei

2020

Water

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The application of a nonlinear autoregressive modeling approach with exogenous input (NARX) neural networks for modeling groundwater level fluctuation has been examined by several researchers. However, the suitability of NARX in modeling groundwater level dynamics in urbanized and arid aquifer systems has not been comprehensively investigated. In this study, a NARX-based modeling approach is presented to establish a robust water management tool to aid urban water managers in controlling the development of shallow water tables induced by artificial recharge activity. Temperature data series are used as exogenous inputs for the NARX network, as they better reflect the intensity of artificial recharge activities, such as excessive lawns irrigation. Input delays and feedback delays for the NARX networks are determined based on the autocorrelation and cross-correlation analyses of detrended groundwater levels and monthly temperature averages. The validation of the proposed approach is assessed through a rolling validation procedure. Four observation wells in Kuwait City are selected to test the applicability of the proposed approach. The results showed the superiority of the NARX-based approach in modeling groundwater levels in such an urbanized and arid aquifer system, with coefficient of determination (R2) values ranging between 0.762 and 0.994 in the validation period. Comparison with other statistical models applied to the same study area shows that NARX models presented here reduced the mean absolute error (MAE) of groundwater levels forecasts by 50%. The findings of this paper are promising and provide a valuable tool for the urban city planner to assist in controlling the problem of shallow water tables for similar climatic and aquifer systems.

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Section: Comparisons With Previous Studiessupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

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A Nonlinear Autoregressive Modeling Approach for Forecasting Groundwater Level Fluctuation in Urban Aquifers

Alsumaiei

2020

Water

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“…Also, Fig. 2(b) shows the structure of the integrated model of NN-ARX which has been successfully used by some researchers (Keshavarz and Roopaei 2006;Izady et al 2013). Clearly, the structure of Fig.…”

Section: Neural Network Auto-regressive Model With Exogenous Inputsmentioning

confidence: 99%

RETRACTED ARTICLE: Day of the year-based prediction of horizontal global solar radiation by a neural network auto-regressive model

Gani

Mohammadi

Shamshirband

et al. 2015

Theor Appl Climatol

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The availability of accurate solar radiation data is essential for designing as well as simulating the solar energy systems. In this study, by employing the long-term daily measured solar data, a neural network auto-regressive model with exogenous inputs (NN-ARX) is applied to predict daily horizontal global solar radiation using day of the year as the sole input. The prime aim is to provide a convenient and precise way for rapid daily global solar radiation prediction, for the stations and their immediate surroundings with such an observation, without utilizing any meteorological-based inputs. To fulfill this, seven Iranian cities with different geographical locations and solar radiation characteristics are considered as case studies. The performance of NN-ARX is compared against the adaptive neurofuzzy inference system (ANFIS). The achieved results prove that day of the year-based prediction of daily global solar radiation by both NN-ARX and ANFIS models would be highly feasible owing to the accurate predictions attained. Nevertheless, the statistical analysis indicates the superiority of NN-ARX over ANFIS. In fact, the NN-ARX model represents high potential to follow the measured data favorably for all cities. For the considered cities, the attained statistical indicators of mean absolute bias error, root mean square error, and coefficient of determination for the NN-ARX models are in the ranges of 0.44-0.61 kWh/m 2 , 0.50-0.71 kWh/m 2 , and 0.78-0.91, respectively.

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“…During the last years several authors have shown the ability of NARX to successfully model and forecast groundwater levels (Alsumaiei, 2020;Chang et al, 2016;Di Nunno and Granata, 2020;Guzman et al, 2017Guzman et al, , 2019Hasda et al, 2020;Izady et al, 2013;Jeihouni et al, 2019;Jeong and Park, 2019;Wunsch et al, 2018;Zhang et al, 2019). Although LSTMs and CNNs are state-of-the-art DL techniques and commonly applied in many disciplines, they are not yet widely adopted in groundwater level prediction applications, except within the last 2 years.…”

mentioning

confidence: 99%

Groundwater level forecasting with artificial neural networks: a comparison of long short-term memory (LSTM), convolutional neural networks (CNNs), and non-linear autoregressive networks with exogenous input (NARX)

Wünsch

Liesch

Broda

2021

Hydrol. Earth Syst. Sci.

159

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Abstract. It is now well established to use shallow artificial neural networks (ANNs) to obtain accurate and reliable groundwater level forecasts, which are an important tool for sustainable groundwater management. However, we observe an increasing shift from conventional shallow ANNs to state-of-the-art deep-learning (DL) techniques, but a direct comparison of the performance is often lacking. Although they have already clearly proven their suitability, shallow recurrent networks frequently seem to be excluded from the study design due to the euphoria about new DL techniques and its successes in various disciplines. Therefore, we aim to provide an overview on the predictive ability in terms of groundwater levels of shallow conventional recurrent ANNs, namely non-linear autoregressive networks with exogenous input (NARX) and popular state-of-the-art DL techniques such as long short-term memory (LSTM) and convolutional neural networks (CNNs). We compare the performance on both sequence-to-value (seq2val) and sequence-to-sequence (seq2seq) forecasting on a 4-year period while using only few, widely available and easy to measure meteorological input parameters, which makes our approach widely applicable. Further, we also investigate the data dependency in terms of time series length of the different ANN architectures. For seq2val forecasts, NARX models on average perform best; however, CNNs are much faster and only slightly worse in terms of accuracy. For seq2seq forecasts, mostly NARX outperform both DL models and even almost reach the speed of CNNs. However, NARX are the least robust against initialization effects, which nevertheless can be handled easily using ensemble forecasting. We showed that shallow neural networks, such as NARX, should not be neglected in comparison to DL techniques especially when only small amounts of training data are available, where they can clearly outperform LSTMs and CNNs; however, LSTMs and CNNs might perform substantially better with a larger dataset, where DL really can demonstrate its strengths, which is rarely available in the groundwater domain though.

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Application of NN-ARX Model to Predict Groundwater Levels in the Neishaboor Plain, Iran

Cited by 38 publications

References 33 publications

A Nonlinear Autoregressive Modeling Approach for Forecasting Groundwater Level Fluctuation in Urban Aquifers

A Nonlinear Autoregressive Modeling Approach for Forecasting Groundwater Level Fluctuation in Urban Aquifers

RETRACTED ARTICLE: Day of the year-based prediction of horizontal global solar radiation by a neural network auto-regressive model

Groundwater level forecasting with artificial neural networks: a comparison of long short-term memory (LSTM), convolutional neural networks (CNNs), and non-linear autoregressive networks with exogenous input (NARX)

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