Groundwater spring potential assessment using new ensemble data mining techniques

Yousefi, Saleh; Sãdhasivam, Nitheshnirmal; Pourghasemi, Hamid Reza; Nazarlou, Hamid Ghaffari; Golkar, Foroogh; Tavangar, Shahla; Santosh, M.

doi:10.1016/j.measurement.2020.107652

Cited by 34 publications

(8 citation statements)

References 102 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…RF requires the modeler to choose two parameters: the number of variables and the number of trees. Furthermore, RF provides two importance ranking indices, "mean decrease Gini" and "mean decrease accuracy" (also termed percent increase in mean square error (MSE)) [58]. The higher the MSE percentage, the greater the importance of the independent variable considered [59].…”

Section: Random Forest (Rf)mentioning

confidence: 99%

A Modeling Comparison of Groundwater Potential Mapping in a Mountain Bedrock Aquifer: QUEST, GARP, and RF Models

Moghaddam

Rahmati

Haghizadeh

et al. 2020

Water

View full text Add to dashboard Cite

In some arid regions, groundwater is the only source of water for human needs, so understanding groundwater potential is essential to ensure its sustainable use. In this study, three machine learning models (Genetic Algorithm for Rule-Set Production (GARP), Quick Unbiased Efficient Statistical Tree (QUEST), and Random Forest (RF)) were applied and verified for spatial prediction of groundwater in a mountain bedrock aquifer in Piranshahr Watershed, Iran. A spring location dataset consisting of 141 springs was prepared by field surveys, and from this three different sample datasets (S1–S3) were randomly generated (70% for training and 30% for validation). A total of 10 groundwater conditioning factors were prepared for modeling, namely slope percent, relative slope position (RSP), plan curvature, altitude, drainage density, slope aspect, topographic wetness index (TWI), terrain ruggedness index (TRI), land use, and lithology. The area under the receiver operating characteristic curve (AUC) and true skill statistic (TSS) were used to evaluate the accuracy of models. The results indicated that all models had excellent goodness-of-fit and predictive performance, but that RF (AUCmean = 0.995, TSSmean = 0.89) and GARP (AUCmean = 0.957, TSSmean = 0.82) outperformed QUEST (AUCmean = 0.949, TSSmean = 0.74). In robustness analysis, RF was slightly more sensitive than GARP and QUEST, making it necessary to consider several random partitioning options for preparing training and validation groups. The outcomes of this study can be useful in sustainable management of groundwater resources in the study region.

show abstract

Section: Random Forest (Rf)mentioning

confidence: 99%

A Modeling Comparison of Groundwater Potential Mapping in a Mountain Bedrock Aquifer: QUEST, GARP, and RF Models

Moghaddam

Rahmati

Haghizadeh

et al. 2020

Water

View full text Add to dashboard Cite

show abstract

“…SVM is an extensively exercised MLA in diverse fields of research that functions on the principle of statistical learning concept and structural risk minimization given by Vapnik [32], which is utilized for classification as well as regression intricacies [33-34]. SVM has a high efficacy in classifying both linearly separable and inseparable data classes [35].…”

Section: Methodsmentioning

confidence: 99%

Assessment of the outbreak risk, mapping and infestation behavior of COVID-19: Application of the autoregressive and moving average (ARMA) and polynomial models

Pourghasemi

Pouyan

Farajzadeh

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Infectious disease outbreaks pose a significant threat to human health worldwide. The outbreak of pandemic coronavirus disease 2019 (COVID 2019) has caused a global health emergency. Identification of regions with high risk for COVID 19 outbreak is a major priority of the governmental organizations and epidemiologists worldwide. The aims of the present study were to analyze the risk factors of coronavirus outbreak and identify areas with a high risk of human infection with virus in Fars Province, Iran. A geographic information system (GIS) based machine learning algorithm (MLA), support vector machine (SVM), was used for the assessment of the outbreak risk of COVID 19 in Fars Province, Iran. The daily observations of infected cases was tested in the third-degree polynomial and the autoregressive and moving average (ARMA) models to examine the patterns of virus infestation in the province and in Iran. The results of disease outbreak in Iran were compared with the data for Iran and the world. Sixteen effective factors including minimum temperature of coldest month (MTCM), maximum temperature of warmest month (MTWM), precipitation in wettest month (PWM), precipitation of driest month (PDM), distance from roads, distance from mosques, distance from hospitals, distance from fuel stations, human footprint, density of cities, distance from bus stations, distance from banks, distance from bakeries, distance from attraction sites, distance from automated teller machines (ATMs), and density of villages were selected for spatial modelling. The predictive ability of an SVM model was assessed using the receiver operator characteristic area under the curve (ROC AUC) validation technique. The validation outcome reveals that SVM achieved an AUC value of 0.786 (March 20), 0.799 (March 29), and 86.6 (April 10) a good prediction of change detection. The growth rate (GR) average for active cases in Fars for a period of 41 days was 1.26, whilst it was 1.13 in country and the world. The results of the third-degree polynomial and ARMA models revealed an increasing trend for GR with an evidence of turning, demonstrating extensive quarantines has been effective. The general trends of virus infestation in Iran and Fars Province were similar, although an explosive growth of the infected cases is expected in the country. The results of this study might assist better programming COVID 19 disease prevention and control and gaining sorts of predictive capability would have wide ranging benefits.

show abstract

“…Landsat 8 (2018) images having 30 m resolution were used to prepare the land cover layer [46]. Groundwater recharge is significantly influenced by land use pattern of an area [4,5]. The land use in the study area was dominated by agricultural and forest land; however, land use under urban and barren is rapidly increasing [30,34,43].…”

Section: Land Cover Patternmentioning

confidence: 99%

“…The sustainability of modern ecosystems and survivability of human are largely dependent on water availability especially to ensure food security of rising populace [1,2]. The existing resources of surface water are insufficient owing to high population density, uneven distribution of water resources, space and time mandate, economic development, and climate change [3][4][5]. Consequently, the demand for groundwater, which accounts for 30% of world freshwater, has increased.…”

Section: Introductionmentioning

confidence: 99%

Mapping Groundwater Potential for Irrigation, by Geographical Information System and Remote Sensing Techniques: A Case Study of District Lower Dir, Pakistan

et al. 2021

View full text Add to dashboard Cite

The changing climate and global warming have rendered existing surface water insufficient, which is projected to adversely influence the irrigated farming systems globally. Consequently, groundwater demand has increased significantly owing to increasing population and demand for plant-based foods especially in South Asia and Pakistan. This study aimed to determine the potential areas for groundwater use for agriculture sector development in the study area Lower Dir District. ArcGIS 10.4 was utilized for geospatial analysis, which is referred to as Multi Influencing Factor (MIF) methodology. Seven parameters including land cover, geology, soil, rainfall, underground faults (liniment) density, drainage density, and slope, were utilized for delineation purpose. Considering relative significance and influence of each parameter in the groundwater recharge rating and weightage was given and potential groundwater areas were classified into very high, high, good, and poor. The result of classification disclosed that the areas of 113.10, 659.38, 674.68, and 124.17 km2 had very high, high, good, and poor potential for groundwater agricultural uses, respectively. Field surveys for water table indicated groundwater potentiality, which was high for Kotkay and Lalqila union councils having shallow water table. However, groundwater potentiality was poor in Zimdara, Khal, and Talash, characterized with a very deep water table. Moreover, the study effectively revealed that remote sensing and GIS could be developed as potent tools for mapping potential sites for groundwater utilization. Furthermore, MIF technique could be a suitable approach for delineation of groundwater potential zone, which can be applied for further research in different areas.

show abstract

Groundwater spring potential assessment using new ensemble data mining techniques

Cited by 34 publications

References 102 publications

A Modeling Comparison of Groundwater Potential Mapping in a Mountain Bedrock Aquifer: QUEST, GARP, and RF Models

A Modeling Comparison of Groundwater Potential Mapping in a Mountain Bedrock Aquifer: QUEST, GARP, and RF Models

Assessment of the outbreak risk, mapping and infestation behavior of COVID-19: Application of the autoregressive and moving average (ARMA) and polynomial models

Mapping Groundwater Potential for Irrigation, by Geographical Information System and Remote Sensing Techniques: A Case Study of District Lower Dir, Pakistan

Contact Info

Product

Resources

About