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Optimization of an adaptive neuro-fuzzy inference system for groundwater potential mapping

Optimisation d’un système d’inférence de type neurone et logique floue pour cartographier le potentiel des eaux souterraines

Optimización de un sistema de inferencia neuro-fuzzy adaptable para el mapeo del potencial de aguas subterráneas

地下水潜力图绘制的自适应神经模糊推理系统优化

Otimização de um sistema de inferência adaptativo neuro-fuzzy para o mapeamento do potencial das águas subterrâneas

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Abstract

The main goal of this study was to optimize an adaptive neuro-fuzzy inference system (ANFIS) using three meta-heuristic optimization algorithms—genetic algorithm (GA), biogeography-based optimization (BBO) and simulated annealing (SA)—to prepare groundwater potential maps. The methodology was applied to the Booshehr plain, Iran. The results of optimized models were compared with ANFIS individually and three bivariate models: frequency ratio (FR), evidential belief function (EBF), and the entropy model. First, 339 wells with groundwater yield higher than 11 m3/h were selected and randomly divided into two groups. In all, 238 wells (70%) were used for training the models and 101 wells (30%) were used for testing and validating the models. Fifteen conditioning factors were selected as input parameters for the modeling. The accuracy of the groundwater potential maps for the study area was determined using root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and standard deviation of error (SD), as well as the area under the receiver operating characteristic (ROC) curve (AUC). Overall, the results demonstrated that ANFIS-GA had the highest prediction capability (AUC = 0.915) for groundwater potential mapping followed by ANFIS-BBO (0.903), entropy (0.862), FR (0.86), ANFIS-SA (0.83), ANFIS (0.82) and EBF (0.80). According to the entropy model, land-use, soil order and rainfall factors had the highest impact on groundwater potential in the study area. The results of this research show that the ANFIS models combined with meta-heuristic optimization algorithms can be a useful decision-making tool for assessment and management of groundwater resources.

Résumé

L’objectif principal de cette étude et d’optimiser un système d’inférence de type neurone et logique floue adaptif (ANFIS) utilisant des algorithmes d’optimisation méta-heuristiques—algorithme génétique (GA), optimisation basée sur la biogéographie (BBO) et recuit simulé (SA)—pour préparer des cartes de potentiel en eau souterraine. La méthode a été appliquée sur la plaine de Booshehr en Iran. Les résultats de modèles optimisés ont été comparés à l’ANFIS de manière individuelle et à trois modèles bivariés: le rapport de fréquence (FR), la fonction de preuve évidente (EBF) et un modèle d’entropie. D’abord, 339 puits avec des rendements de plus de 11 m3/h ont été sélectionnés et divisés de manière aléatoire en deux groupes. 238 puits ont été utilisés pour la phase d’apprentissage du modèle et 101 puits (30%) pour les phases de test et de validation. Quinze facteurs de conditions ont été sélectionnés comme paramètres d’entrée pour la modélisation. La précision des cartes de potentialité en eau souterraine pour le site d’étude a été déterminée par la racine de l’erreur quadratique moyenne (RMSE), l’erreur absolue moyenne (MAE), l’erreur sur la moyenne absolue du pourcentage (MAPE) et l’erreur de la déviation standard (SD), ainsi que sur l’aire sous la fonction d’efficacité du récepteur (ROC) ou courbe (AUC). Dans l’ensemble, les résultats montrent que le ANFIS-GA a la meilleure capacité de prédiction (AUC = 0.915) pour cartographier le potentiel en eau souterraine suivi par le ANFIS-BBO (0.903), l’entropie (0.862), le FR (0.86), le ANFIS-le SA (0.83), l’ANFIS (0.82) et l’EBF (0.80). Selon le modèle d’entropie, l’utilisation des sols, les facteurs d’ordre des sols et précipitations ont les plus forts impacts sur le potentiel en eau souterraine pour le site étudié. Les résultats de cette recherche montrent que le modèle ANFIS combiné avec des algorithmes d’optimisation méta-heuristiques peuvent être des outils de prise de décisions utiles pour l’évaluation et la gestion des ressources en eaux souterraines.

Resumen

El objetivo principal de este estudio es optimizar un sistema de inferencia neuro-fuzzy adaptativo (ANFIS) usando tres algoritmos de optimización meta-heurística (algoritmo genético (GA), optimización basada en biogeografía (BBO) y recocido simulado (SA)) para preparar mapas de potencial de aguas subterráneas. La metodología se aplicó en la llanura de Booshehr, Irán. Los resultados de los modelos optimizados se compararon con ANFIS individualmente y con tres modelos bivariados: el cociente de frecuencias (FR), la función de creencia probatoria (EBF) y el modelo de entropía. En primer lugar, se seleccionaron 339 pozos con un rendimiento de agua subterránea superior a 11 m3/h y se dividieron aleatoriamente en dos grupos. Se utilizaron 238 pozos (70%) para la capacitación de los modelos y 101 pozos (30%) para probar y validar los modelos. Se seleccionaron quince factores condicionantes como parámetros de entrada para el modelado. La exactitud de los mapas de potencial de agua subterránea para el área de estudio se determinó usando el error cuadrado medio de la raíz (RMSE), el error absoluto medio (MAE), el error porcentual absoluto medio (MAPE) y la desviación estándar del error (SD), así como el área bajo la curva de la característica (AUC) de operación del receptor (ROC). En general, los resultados demostraron que ANFIS-GA tenía la mayor capacidad de predicción (AUC = 0.915) para el mapeo del potencial de aguas subterráneas, seguido por ANFIS-BBO (0.903), entropía (0.862), FR (0.86), ANFIS-SA (0.83), ANFIS (0.82) y EBF (0.80). De acuerdo con el modelo de entropía, el uso de la tierra, el orden del suelo y los factores de precipitación tuvieron el mayor impacto en el potencial del agua subterránea en el área de estudio. Los resultados de esta investigación muestran que los modelos ANFIS combinados con algoritmos de optimización meta-heurística pueden ser una herramienta útil para la toma de decisiones en la evaluación y gestión de los recursos hídricos subterráneos.

摘要

本研究的主要目的是使用三种元启发式优化算法(遗传算法(GA),基于生物地理学的优化(BBO)和模拟退火(SA))来优化自适应神经模糊推理系统(ANFIS)以绘制地下水潜力区。该方法应用于伊朗的Booshehr平原。优化模型的结果与单独的ANFIS和三个双变量模型(频率比(FR),证据信念函数(EBF)和熵模型)进行了比较。首先,选择了地下水出水量高于11 m3/h的339口井,将其随机分为两组。 238孔(70%)用于模型训练,101孔(30%)用于模型测试和验证。选择15个调节因子作为建模的输入参数。使用均方根误差(RMSE),平均绝对误差(MAE),平均绝对百分误差(MAPE)和误差的标准偏差(SD),以及接收器操作特性(ROC)曲线的面积(AUC),来量化研究区地下水潜力图的准确度。总体而言,结果表明ANFIS-GA具有最高的地下水潜力预测能力(AUC = 0.915),其次是ANFIS-BBO(0.903),熵(0.862),FR(0.86),ANFIS-SA(0.83),ANFIS (0.82)和EBF(0.80)。根据熵模型,土地利用,土壤顺序和降雨因子对研究区地下水潜力制图的影响最大。研究结果表明,ANFIS模型结合元启发式优化算法可以成为评估和管理地下水资源的有用决策工具。

Resumo

O principal objetivo deste estudo foi otimizar um sistema de inferência adaptativa neuro-fuzzy (SIANF) usando três algoritmos de otimização meta-heurística (algoritmo genético (AG), otimização baseada em biogeografia (OBB) e simulação annealing (SA)) para mapear o potencial da águas subterrâneas. A metodologia foi aplicada na planície de Booshehr, no Irã. Os resultados dos modelos otimizados foram comparados com o SIANF individualmente e três modelos bivariados: razão de frequência (RF), função de crença evidencial (FCE) e o modelo de entropia. Primeiramente, 339 poços com produção de água subterrânea superior a 11 m3/h foram selecionados e divididos aleatoriamente em dois grupos. Foram utilizados 238 poços (70%) para treinamento dos modelos e 101 poços (30%) para testar e validar os modelos. Quinze fatores condicionantes foram selecionados como parâmetros de entrada para a modelagem. A precisão dos mapas de potencial das águas subterrâneas para a área de estudo foi determinada usando o erro médio quadrático (EMQ), erro absoluto médio (EAM), erro percentual absoluto médio (EPAM) e desvio-padrão do erro (DPE), bem como a área sob a curva característica de operação do receptor (CCOR). No geral, os resultados demonstraram que SIANF-AG teve a maior capacidade de predição (CCOR = 0.915) para mapeamento do potencial das águas subterrâneas seguido por SIANF-OBB (0.903), entropia (0.862), RF (0.86), SIANF-SA (0.83), SIANF (0.82) e FCE (0.80). De acordo com o modelo de entropia, o uso da terra, a ordem do solo e os fatores de precipitação tiveram o maior impacto no potencial das águas subterrâneas na área de estudo. Os resultados desta pesquisa mostram que os modelos SIANF combinados com algoritmos de otimização meta-heurística podem ser uma ferramenta útil de tomada de decisão para avaliação e gerenciamento de recursos hídricos subterrâneos.

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Authors and Affiliations

  1. Faculty of Geodesy & Geomatics Engineering, K.N. Toosi University of Technology, Tehran, Iran

    Seyed Vahid Razavi Termeh

  2. Department of Watershed Management Engineering, Faculty of Natural Resources, Sari Agricultural Science and Natural Resources University (SANRU), Sari, Iran

    Khabat Khosravi

  3. Civil Engineering Department, University of Ottawa, Ottawa, K1N6N5, Canada

    Majid Sartaj

  4. Soil Physics and Land Management Group, Wageningen University, Droevendaalsesteeg 4, 6708 PB, Wageningen, Netherlands

    Saskia Deborah Keesstra

  5. Civil, Surveying and Environmental Engineering, The University of Newcastle, Callaghan, 2308, Australia

    Saskia Deborah Keesstra

  6. Department of Civil and Environmental Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA

    Frank T.-C. Tsai

  7. Department of Hydrology and Quantitative Water Management, Wageningen University, Droevendaalsesteeg 4, 6708 PB, Wageningen, Netherlands

    Roel Dijksma

  8. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

    Binh Thai Pham

Authors
  1. Seyed Vahid Razavi Termeh
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  2. Khabat Khosravi
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  3. Majid Sartaj
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  4. Saskia Deborah Keesstra
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  5. Frank T.-C. Tsai
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  6. Roel Dijksma
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  7. Binh Thai Pham
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Corresponding authors

Correspondence to Majid Sartaj or Binh Thai Pham.

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Termeh, S.V.R., Khosravi, K., Sartaj, M. et al. Optimization of an adaptive neuro-fuzzy inference system for groundwater potential mapping. Hydrogeol J 27, 2511–2534 (2019). https://doi.org/10.1007/s10040-019-02017-9

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  • DOI: https://doi.org/10.1007/s10040-019-02017-9

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