Groundwater level response identification by hybrid wavelet–machine learning conjunction models using meteorological data

Samani, Saeideh; Vadiati, Meysam; Nejatijahromi, Zohre; Etebari, Behrooz; Kişi, Özgür

doi:10.1007/s11356-022-23686-2

Cited by 29 publications

(10 citation statements)

References 110 publications

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“…The experimental results show that this indeed improves the prediction result, and the weighted accuracy involving all eight groups is almost one. Similar results were reported in [36,37], indicating that the integration of wavelet-based machine learning models significantly enhances the performance of standalone models. This shows the significance of the hybrid methods.…”

Section: Discussionsupporting

confidence: 83%

Predicting the Overflowing of Urban Personholes Based on Machine Learning Techniques

Chang,

Tseng,

Hsu

2023

Water

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Urban stormwater drainage systems, which include many personholes to collect and discharge precipitation within a city, are extensively constructed to prevent streets and buildings from flooding. This research intends to build a machine learning model to predict whether a personhole will overflow soon, which is crucial to alleviate the damage caused by floods. To address the challenges posed by many diverse personholes, we proposed segmenting the personholes into several groups and have designed two methods employing different personhole features. The first, the geography-based method, uses the geographical locations of the personholes for the grouping. The second, the hydrology-based method, uses the characteristics that are directly related to the overflowing situation, such as the depth of the personhole, and the average and the maximum water level of the personholes. We also investigated several machine learning techniques, such as the multilayer perceptron (MLP) model and a fine-tuning architecture. The study area was located in the new Taipei city and the experimental results have shown the impressive predictive ability of the proposed approaches. Particularly, by applying the hydrology-based grouping method, and using a hybrid model combining the machine learning model prediction results with heuristic rules, we can obtain the best prediction result, and the accuracy is over 99%. We have also noticed the influence of the activation function used in the neural network and the number of frozen layers in the fine-tuning architecture. Particularly, using the tanh function with one frozen layer is good in some cases. However, since it is not general enough, we suggest the readers perform empirical studies before choosing the best setting in their own environment.

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Section: Discussionsupporting

confidence: 83%

Predicting the Overflowing of Urban Personholes Based on Machine Learning Techniques

Chang,

Tseng,

Hsu

2023

Water

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“…Last, in the field of environmental sciences, machine learning (ML) methods have been widely used with a high success rate. The authors in [39,40] present innovative research on the application of hybrid wavelet-ML models in the domain of environmental science and hydrology. [41] presents a comprehensive review of the challenges in modeling, optimization, diagnostics, and control of Internal Combustion Engines (ICE); and explores the potential of modern ML techniques, proposing a ML-based grey-box approach as a robust solution to address these challenges for ICEs.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Towards Cleaner Ports: Predictive Modeling of Sulfur Dioxide Shipping Emissions in Maritime Facilities Using Machine Learning

et al. 2023

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Maritime ports play a pivotal role in fostering the growth of domestic and international trade and economies. As ports continue to expand in size and capacity, the impact of their operations on air quality and climate change becomes increasingly significant. While nearby regions may experience economic benefits, there are significant concerns regarding the emission of atmospheric pollutants, which have adverse effects on both human health and climate change. Predictive modeling of port emissions can serve as a valuable tool in identifying areas of concern, evaluating the effectiveness of emission reduction strategies, and promoting sustainable development within ports. The primary objective of this research is to utilize machine learning frameworks to estimate the emissions of SO2 from ships during various port activities, including hoteling, maneuvering, and cruising. By employing these models, we aim to gain insights into the emission patterns and explore strategies to mitigate their impact. Through our analysis, we have identified the most effective models for estimating SO2 emissions. The AutoML TPOT framework emerges as the top-performing model, followed by Non-Linear Regression with interaction effects. On the other hand, Linear Regression exhibited the lowest performance among the models evaluated. By employing these advanced machine learning techniques, we aim to contribute to the body of knowledge surrounding port emissions and foster sustainable practices within the maritime industry.

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“…Recently, an increasing trend in the use of machine learning methods [22,23], artificial neural networks [24,25], fuzzy logic [26,27], genetic algorithms [28,29], and hybrid machine learning methods [30,31] has been observed. A good and updated review about pumping tests can be found in [32], with a complete bibliography to refer to for further information.…”

Section: Introductionmentioning

confidence: 99%

Hydrodynamic Characterization of Carbonate Aquifers Using Atypical Pumping Tests without the Interruption of the Drinking Water Supply

Rusi,

Di Curzio,

Di Giovanni

2024

Water

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The Gran Sasso carbonate aquifer is the largest and most productive in the Apennines. Its hydrogeological structure has been studied since the middle of the last century for the springs’ characterization for drinking purposes and for a motorway tunnel. Meanwhile, its hydrodynamic parametrization is less developed and has been limited to monitoring the discharge and chemical and isotopic parameters. Secondary porosity characterizes the aquifer, and an underlying impermeable marly complex represents the basal aquiclude. It might appear inappropriate to characterize the hydraulic properties via pumping tests, as their reliability has been proven in homogeneous and isotropic media. However, the high extent of the aquifer, the wells’ location, the scarcity of information available and the lack of alternatives has forced the estimation of hydrodynamic parameters as in porous aquifers and the experimental testing of the aquifer, especially in maximum pumping conditions, for a possible exploitation increase. Since aquifer testing was performed during the normal well field’s activities, it was not possible to perform typical tests. Therefore, the step-drawdown test was conducted by turning on an increasing number of wells over time and keeping the observation points fixed. As results, a mean hydraulic conductivity of 5 × 10−3 m/s and a mean transmissivity of 0.3 m2/s were established without interrupting the water supply; meanwhile, the influence radius and flow directions were also estimated.

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Groundwater level response identification by hybrid wavelet–machine learning conjunction models using meteorological data

Cited by 29 publications

References 110 publications

Predicting the Overflowing of Urban Personholes Based on Machine Learning Techniques

Predicting the Overflowing of Urban Personholes Based on Machine Learning Techniques

Towards Cleaner Ports: Predictive Modeling of Sulfur Dioxide Shipping Emissions in Maritime Facilities Using Machine Learning

Hydrodynamic Characterization of Carbonate Aquifers Using Atypical Pumping Tests without the Interruption of the Drinking Water Supply

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