A systematic review on integrated surface–subsurface modeling using watershed WASH123D model

Hussain, Fiaz

doi:10.1007/s40808-021-01203-7

Cited by 4 publications

(4 citation statements)

References 52 publications

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“…They claimed that numerical groundwater modeling is a better alternative to costly aquifer pumping tests for describing aquifer response to external loads. Guevara et al [79,80] studied three mathematical models to represent variable-density groundwater flow simulations in a systematic investigation.…”

Section: Related Workmentioning

confidence: 99%

Mathematical and Machine Learning Models for Groundwater Level Changes: A Systematic Review and Bibliographic Analysis

et al. 2022

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With the effects of climate change such as increasing heat, higher rainfall, and more recurrent extreme weather events including storms and floods, a unique approach to studying the effects of climatic elements on groundwater level variations is required. These unique approaches will help people make better decisions. Researchers and stakeholders can attain these goals if they become familiar with current machine learning and mathematical model approaches to predicting groundwater level changes. However, descriptions of machine learning and mathematical model approaches for forecasting groundwater level changes are lacking. This study picked 117 papers from the Scopus scholarly database to address this knowledge gap. In a systematic review, the publications were examined using quantitative and qualitative approaches, and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was chosen as the reporting format. Machine learning and mathematical model techniques have made significant contributions to predicting groundwater level changes, according to the study. However, the domain is skewed because machine learning has been more popular in recent years, with random forest (RF) methods dominating, followed by the methods of support vector machine (SVM) and artificial neural network (ANN). Machine learning ensembles have also been found to help with aspects of computational complexity, such as performance and training times. Furthermore, compared to mathematical model techniques, machine learning approaches achieve higher accuracies, according to our research. As a result, it is advised that academics employ new machine learning techniques while also considering mathematical model approaches to predicting groundwater level changes.

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Section: Related Workmentioning

confidence: 99%

Mathematical and Machine Learning Models for Groundwater Level Changes: A Systematic Review and Bibliographic Analysis

et al. 2022

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“…These geological features provide useful hints related to groundwater flow and contribution. There are 14 observation wells inside the study area as shown in Figure 1 [25].…”

Section: Study Area Descriptionmentioning

confidence: 99%

“…An unconfined and three confined aquifers, namely, F1 (layer 1), F2, F3-1 and F3-2 (layer 4), were identified from shallow to 200 m depth as shown in Figure 2. [25].…”

Section: Study Area Descriptionmentioning

confidence: 99%

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Characterization of Regional Groundwater System Based on Aquifer Response to Recharge–Discharge Phenomenon and Hierarchical Clustering Analysis

Hussain

Lin

et al. 2021

Water

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The investigations of groundwater hydrograph reasonably reflect the aquifer response to recharge–discharge phenomenon and its characteristics. A better understanding of aquifer characteristics such as regional aquifer classification, recharge and discharge patterns, aquifer geology and flow patterns are the surface indicators that may be more effective and less costly for interpreting basic regional hydrogeological conditions and assessments. This study deals with the application of Hierarchical Clustering Analysis to understand the groundwater spatio-temporal patterns and to visualize/classify the nature of the aquifer in the regional area of Kaohsiung City, Taiwan. Groundwater level fluctuation patterns and slopes of rising and recession limbs are used to identify the pumping effects and classify aquifers. The results of clustering analysis show that the groundwater observation wells in the study area can be divided into five major characteristics along with the upstream to downstream of Kaoping River. The clusters are consistent with basic lithology distribution and age of sedimentary, which represents the characteristics of groundwater level fluctuation. The identified groundwater hydrographs patterns provide newer insights related to aquifer response to recharge–discharge phenomenon, types of aquifers and their behaviors. The knowledge of water level fluctuations in the observation wells provides a piece of prior information about the abstraction of groundwater. The proposed aquifer classification and pumping effect have great potential for applied use in groundwater management e.g., save drilling cost.

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A fuzzy-based flood warning system using 19-year remote sensing time series data in the Google Earth Engine cloud platform

Rostami

Akhoondzadeh²,

Amani³

2022

Advances in Space Research

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A systematic review on integrated surface–subsurface modeling using watershed WASH123D model

Cited by 4 publications

References 52 publications

Mathematical and Machine Learning Models for Groundwater Level Changes: A Systematic Review and Bibliographic Analysis

Mathematical and Machine Learning Models for Groundwater Level Changes: A Systematic Review and Bibliographic Analysis

Characterization of Regional Groundwater System Based on Aquifer Response to Recharge–Discharge Phenomenon and Hierarchical Clustering Analysis

A fuzzy-based flood warning system using 19-year remote sensing time series data in the Google Earth Engine cloud platform

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