Monthly groundwater level prediction using ANN and neuro-fuzzy models: a case study on Kerman plain, Iran

Jalalkamali, Amir; Sedghi, Hossein; Manshouri, Mohammad

doi:10.2166/hydro.2010.034

Cited by 61 publications

(23 citation statements)

References 26 publications

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“…The latter represents the explained variance by the models; its best value is 1. These two different metrics are widely used in models evaluation in different applications, comprising several engineering fields such as hydrology [38], hydraulics [39,40] and consumption forecasting [41].…”

Section: Evaluation Metricsmentioning

confidence: 99%

Tuning ANN Hyperparameters for Forecasting Drinking Water Demand

2021

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The evolution of smart water grids leads to new Big Data challenges boosting the development and application of Machine Learning techniques to support efficient and sustainable drinking water management. These powerful techniques rely on hyperparameters making the models’ tuning a tricky and crucial task. We hence propose an insightful analysis of the tuning of Artificial Neural Networks for drinking water demand forecasting. This study focuses on layers and nodes’ hyperparameters fitting of different Neural Network architectures through a grid search method by varying dataset, prediction horizon and set of inputs. In particular, the architectures involved are the Feed Forward Neural Network, the Long Short Term Memory, the Simple Recurrent Neural Network and the Gated Recurrent Unit, while the prediction interval ranges from 1 h to 1 week. To avoid the problem of the Neural Networks tuning stochasticity, we propose the selection of the median model among several repetitions for each hyperparameter’s configurations. The proposed iterative tuning procedure highlights the change of the required number of layers and nodes depending on Neural Network architectures, prediction horizon and dataset. Significant trends and considerations are pointed out to support Neural Network application in drinking water prediction.

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Section: Evaluation Metricsmentioning

confidence: 99%

Tuning ANN Hyperparameters for Forecasting Drinking Water Demand

2021

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“…The results of their research also reflected the superiority of ANFIS on the model of fuzzy logic. [3] Used a fuzzy inference system and neural networks perceptron multilayers to predict the level of groundwater resources in Kerman Plain. The results of this research have proved the superiority of ANFIS on the perceptible network of multilayers.…”

Section: Introductionmentioning

confidence: 99%

Application of Several Artificial Intelligence Models for Forecasting Meteorological Drought Using the Standardized Precipitation Index in the Saïss Plain (Northern Morocco)

Ibrahimi¹,

Baali²

2018

IJIES

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Drought is one of the main natural factors influencing different aspects of human life. Over the decades, intelligent techniques have proven to be very capable of modeling and predicting nonlinear and dynamic time series. Therefore, the present study aims to predict drought by using and comparing neuro-fuzzy adaptive inference systems (ANFIS), artificial neural network of multilayered perceptron (ANN-MLP) and the support vector model (SVR). For this purpose, the precipitation data obtained from the Ain Bittit station were used for a statistical period of 34 years. In addition, the short-term (3 and 6 months) and long-term (9 and 12 months) time scales were calculated using the standardized precipitation index (SPI). Then, depending on the results of the calculations, the period 1979-2000 was selected as a control group and the period 2003-2012 was selected as an experimental group. In order to predict the SPI for the (t + 1) period, SPI values, precipitation from previous months were used. The results indicated that, in the majority of time scales, the ANFIS model gives SPI values and predictive dryness more accurately than the SVR, and ANN models.

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“…Because of the complexity of hydrogeological systems, modeling groundwater levels using data-driven methods has been an attractive option to researchers lately. Previous studies have focused on predicting groundwater levels at monthly (Nayak et al 2006;Nourani et al 2008;Jalalkamali et al 2011;Shirmohammadi et al 2013), weekly (Mohanty et al 2010;Karthikeyan et al 2013;Mohanty et al 2013), daily (Sahoo and Jha 2013;Shiri and Kisi 2011;Shiri et al 2013), and 6-h time intervals (Yoon et al 2011). To the best of the authors' knowledge, the only study on predicting hourly groundwater levels using ANN models is Taormina et al (2012).…”

Section: Introductionmentioning

confidence: 99%

Wetland Water-Level Prediction Using ANN in Conjunction with Base-Flow Recession Analysis

2017

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This study introduces two artificial neural network (ANN)-based methodologies to predict hourly water levels (WLs) in wetlands characterized by water tables at or near the surface that respond rapidly to precipitation. The first method makes use of hourly precipitation data and WL data from nearby sites. The second method is a combination of ANN, recursive digital filter, and recession curve method and does not require any nearby site. The proposed methods were tested at two headwater wetlands in coastal Alabama. Site 17 had two nearby sites whose WLs were highly correlated with Site 17's. The root-mean-square error and Nash-Sutcliffe efficiencies were 2.9 cm and 0.98, respectively, when the first method was applied to Site 17. The second method was tested at Site 32. For this, the WL time series was separated into quick-and slow-response components. A combination of ANN and base-flow separation methods proved to be very efficient for WL prediction at this site, especially when the duration of quick-response components of individual events was less than 6 h. The proposed methodologies, therefore, proved useful in predicting WLs in wetlands dominated by both surface water and groundwater. individual papers. This paper is part of the Journal of Hydrologic Engineering, © ASCE, ISSN 1084-0699/D4015003 (11)/$25.00. © ASCE D4015003-1 J. Hydrol. Eng. J. Hydrol. Eng. Downloaded from ascelibrary.org by Auburn University on 09/24/15. Copyright ASCE. For personal use only; all rights reserved. © ASCE D4015003-5 J. Hydrol. Eng. J. Hydrol. Eng. Downloaded from ascelibrary.org by Auburn University on 09/24/15. Copyright ASCE. For personal use only; all rights reserved. © ASCE D4015003-8 J. Hydrol. Eng. J. Hydrol. Eng. Downloaded from ascelibrary.org by Auburn University on 09/24/15. Copyright ASCE. For personal use only; all rights reserved. © ASCE D4015003-9 J. Hydrol. Eng. J. Hydrol. Eng. Downloaded from ascelibrary.org by Auburn University on 09/24/15. Copyright ASCE. For personal use only; all rights reserved. © ASCE D4015003-11 J. Hydrol. Eng. J. Hydrol. Eng. Downloaded from ascelibrary.org by Auburn University on 09/24/15.

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Monthly groundwater level prediction using ANN and neuro-fuzzy models: a case study on Kerman plain, Iran

Cited by 61 publications

References 26 publications

Tuning ANN Hyperparameters for Forecasting Drinking Water Demand

Tuning ANN Hyperparameters for Forecasting Drinking Water Demand

Application of Several Artificial Intelligence Models for Forecasting Meteorological Drought Using the Standardized Precipitation Index in the Saïss Plain (Northern Morocco)

Wetland Water-Level Prediction Using ANN in Conjunction with Base-Flow Recession Analysis

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