Modelling groundwater level variations by learning from multiple models using fuzzy logic

Nadiri, Ata Allah; Naderi, Keyvan; Khatibi, Rahman; Gharekhani, Maryam

doi:10.1080/02626667.2018.1554940

Cited by 88 publications

(12 citation statements)

References 38 publications

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“…Mamdani FL (MFL) models use (i) the output membership functions of the 'MIN' operation for their fuzzy implications (Mamdani 1976;Nadiri et al 2019), and (ii) rules are identified by the FCM clustering method (Lee 2004).…”

Section: Three Models Based On Fuzzy Logicmentioning

confidence: 99%

A study of subsidence hotspots by mapping vulnerability indices through innovatory ‘ALPRIFT’ using artificial intelligence at two levels

Nadiri

Khatibi²,

Khalifi

et al. 2020

Bull Eng Geol Environ

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Declining groundwater levels due to the absence of a planning system makes aquifers vulnerable to subsidence. This paper investigates possible hotspots in terms of Subsidence Vulnerability Indices (SVI) by applying the ALPRIFT framework, introduced recently by the authors by mirroring the procedure for the DRASTIC framework. ALPRIFT is suitable to cases, where data is sparse, and is the acronym of seven data layers to be presented in due course. It is a scoring technique, in which each data layer bears an aspect of land subsidence and is prescribed with rates to account for local variability, and with prescribed weights to account for relative significance of the data layer. The inherent subjectivity in prescribed weights is treated in this paper by learning their values from site-specific data by the strategy of using artificial intelligence to learn from multiple models (AIMM). The strategy has two levels: (i) at Level 1, three fuzzy models are used to learn weight values from the local data and from observed target data, and (ii) at Level 2, genetic expression algorithm (GEP) is used to learn further, in which the outputs of the models at Level 1 are reused as its inputs and observed data as its target values. The results show that (i) the Nash-Sutcliff Efficiency (NSE) coefficient for ALPRIFT with measured land subsidence values is approx. 0.21; (ii) NSE is improved to 0.88 by learning the weights at Level 1 using fuzzy logic, and (iii) NSE is further improved to 0.94 by further learning at Level 2 using GEP.

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Section: Three Models Based On Fuzzy Logicmentioning

confidence: 99%

A study of subsidence hotspots by mapping vulnerability indices through innovatory ‘ALPRIFT’ using artificial intelligence at two levels

Nadiri

Khatibi²,

Khalifi

et al. 2020

Bull Eng Geol Environ

Self Cite

View full text Add to dashboard Cite

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“…In the last few years, most of the research studies used soft computing techniques for GWL [27]. These softcomputing techniques included ANN [30], support vector machines (SVM) [31], and adaptive neuro-fuzzy interface systems (ANFIS) [32].…”

Section: Literature Reviewmentioning

confidence: 99%

Groundwater Level Prediction Model Using Correlation and Difference Mechanisms Based on Boreholes Data for Sustainable Hydraulic Resource Management

et al. 2021

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Drilling data for groundwater extraction incur changes over time due to variations in hydrogeological and weather conditions. At any time, if there is a need to deploy a change in drilling operations, drilling companies keep monitoring the time-series drilling data to make sure it is not introducing any changes or new errors. Therefore, a solution is needed to predict groundwater levels (GWL) and detect a change in boreholes data to improve drilling efficiency. The proposed study presents an ensemble GWL prediction (E-GWLP) model using boosting and bagging models based on stacking techniques to predict GWL for enhancing hydraulic resource management and planning. The proposed research study consists of two modules; descriptive analysis of boreholes data and GWL prediction model using ensemble model based on stacking. First, descriptive analysis techniques, such as correlation analysis and difference mechanisms, are applied to investigate boreholes log data for extracting underlying characteristics, which is critical for enhancing hydraulic resource management. Second, an ensemble prediction model is developed based on multiple hydrological patterns using robust machine learning (ML) techniques to predict GWL for enhancing drilling efficiency and water resource management. The architecture of the proposed ensemble model involves three boosting algorithms as base models (level-0) and a bagging algorithm as a meta-model that combines the base models predictions (level-1). The base models consist of the following boosting algorithms; eXtreme Gradient Boosting (XGBoost), AdaBoost, Gradient Boosting (GB). The meta-model includes Random Forest (RF) as a bagging algorithm referred to as a level-1 model. Furthermore, different evaluation metrics are used, including mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE), mean absolute percentage error (MAPE), and R2 score. The performance of the proposed E-GWLP model is compared with existing ensemble and baseline models. The experimental results reveal that the proposed model performed accurately in respect of MAE, MSE, and RMSE of 0.340, 0.564, and 0.751, respectively. The MAPE and R2 score of our proposed approach is 12.658 and 0.976, respectively, which signifies the importance of our work. Moreover, experimental results suggest that E-GWLP model is suitable for sustainable water resource management and improves reservoir engineering.

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“…Ghazavi et al (2018) selecionou locais para poços de recarga artificiais em uma área urbana no Irã usando técnicas de lógica fuzzy. Nadiri et al (2019) aplicou a lógica fuzzy para modelar as variações do nível do lençol freático na província do Azerbaijão Oriental, Irã. Já Das e Pal (2019) utilizaram logica fuzzy e análise AHP para determinar potenciais zonas de recarga na Índia.…”

Section: Artigosunclassified

Modelagem da favorabilidade à recarga das águas subterrâneas pelo paradigma da lógica difusa

2021

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As informações geográficas são imprecisas, o que significa que os limites entre fenômenos diferentes são confusos ou há heterogeneidade dentro de uma classe, devido a diferenças entre feições geológicas, pedológicas, geomorfológicas, vegetais e assim por diante. Métodos baseados em inteligência artificial (IA) fornecem soluções sensíveis à natureza imprecisa (fuzzy) do mundo real baseados em regras definidas por especialistas. A natureza incerta dos processos que controlam a recarga das águas subterrâneas em bacias hidrográficas permite que esses métodos possam ser aplicados na gestão das águas subterrâneas, auxiliando no planejamento e na tomada de decisão quanto ao uso da água e proteção de áreas vulneráveis. O objetivo deste trabalho foi definir áreas favoráveis para recarga das águas subterrâneas a partir de variáveis relacionadas amostradas próximas a poços de monitoramento em uma bacia hidrográfica em área de afloramento do Sistema Aquífero Guarani (SAG). Para isso, utilizou-se da lógica fuzzy para definir um sistema de inferência capaz de extrapolar espacialmente os dados amostrados pontualmente para toda a bacia hidrográfica, resultando em um mapa de favorabilidade à recarga baseado em variáveis relacionadas à textura e manejo do solo, feições de terreno e vegetação. O mapa síntese auxilia tanto o planejamento quanto a tomada de decisão do uso da terra considerando os processos hidrológicos em suas interfaces de superfície e subsuperfície. A partir dos resultados alcançados, estende-se a discussão sobre a importância das escolhas éticas nos processos de tomada de decisão em hidrogeologia relacionadas ao uso de métodos baseados em IA

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Modelling groundwater level variations by learning from multiple models using fuzzy logic

Cited by 88 publications

References 38 publications

A study of subsidence hotspots by mapping vulnerability indices through innovatory ‘ALPRIFT’ using artificial intelligence at two levels

A study of subsidence hotspots by mapping vulnerability indices through innovatory ‘ALPRIFT’ using artificial intelligence at two levels

Groundwater Level Prediction Model Using Correlation and Difference Mechanisms Based on Boreholes Data for Sustainable Hydraulic Resource Management

Modelagem da favorabilidade à recarga das águas subterrâneas pelo paradigma da lógica difusa

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