Groundwater Potential Mapping Using Maximum Entropy

Salvacion, Arnold R.

doi:10.1007/978-981-16-6573-8_13

Cited by 5 publications

(1 citation statement)

References 55 publications

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“…usgs.gov/media/images/map-annual-average-precipitation-us-1981-2010, accessed on 1 December 2021), indicating that climate, specifically precipitation, may be an important predictor of GIE occurrence in the region. This relationship with precipitation was also noted in groundwater ecosystem mapping efforts in the Philippines [73].…”

Section: Important Variables In Ensembled Modelssupporting

confidence: 66%

Predicting the Presence of Groundwater-Influenced Ecosystems in the Northeastern United States with Ensembled Models

Snyder,

Loftin,

Reeve

2023

Water

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Globally, groundwater-influenced ecosystems (GIEs) are increasingly vulnerable to groundwater extraction and land use practices. Groundwater supports these ecosystems by providing inflow, which can maintain water levels, water temperature, and the chemistry necessary to sustain the biodiversity that they support. Many aquatic systems receive groundwater as a portion of baseflow, and in some systems, the connection with groundwater is significant and important to the system’s integrity and persistence. There is a lack of information about where these systems are found and their relationships with environmental conditions in the surrounding landscape. Additionally, groundwater management for human use often does not address maintaining the ecological functions of GIEs. We used correlative distribution modeling methods (GLM, GAM, MaxEnt, Random Forest) to predict landscape-scale habitat suitability for GIEs in two ecologically distinct ecoregions (EPA Level II ecoregions: Atlantic Highlands and Mixed Wood Plains) in the northeastern United States. We evaluated and combined the predictions to create ensemble models for each ecoregion. The accuracy of the ensemble models was 75% in the Atlantic Highlands and 86% in the Mixed Wood Plains. In the Mixed Wood Plains, hydric soil, surface materials, and soil permeability were the best predictors of GIE presence, whereas hydric soil, topographic wetness index, and elevation were the best predictors of GIE presence in the Atlantic Highlands. Approximately 1% of the total land area in each ecoregion was predicted to be suitable for GIEs, highlighting that there likely is a small proportion of the landscape occupied by these systems.

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Section: Important Variables In Ensembled Modelssupporting

confidence: 66%

Predicting the Presence of Groundwater-Influenced Ecosystems in the Northeastern United States with Ensembled Models

Snyder,

Loftin,

Reeve

2023

Water

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Delineating village-level drought risk in Marinduque Island, Philippines

Salvacion

2022

Nat Hazards

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Reciprocal analysis of groundwater potentiality and vulnerability modeling in the Bahabad Plain, Iran

Yazdi

Motamedvaziri

Hosseini

et al. 2023

Environ Sci Pollut Res

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Heuristic and statistical groundwater quality assessment models are e cient tools in the zoning of groundwater vulnerability to contamination. An important reciprocal methodology, yet neglected in Iran, was conducted to assess the performance of three groundwater vulnerability models, namely GODS, SI and DRASTIC, and a data mining model for groundwater potential, maximum entropy (MaxEnt). For both the training and validation stages for the MaxEnt model, the Mahalanobis distance technique was adopted. The vulnerability rates obtained from the DRASTIC model with a coe cient of determination (R2) value of 0.76 had a statistically signi cant correlation with nitrate concentrations in the 21 wells, compared to SI and GODS. The DRASTIC model can better re ect the vulnerability of groundwater resources to contamination. The impact of the vadose zone with an average effective weight of 33 is more important to other parameters, followed by depth to groundwater (D) (32.01), net recharge (R) (28.95) and the aquifer media (A) (18.1). These weights may not be changed. MaxEnt showed signi cant performance in both the training and validation stages with the respective area under the receiver operating characteristic curve (AUROC) values of 0.907 and 0.901. A reciprocal analysis between the vulnerability map in the superior model and the groundwater potential map derived from MaxEnt revealed that areas with high groundwater potential are still in safe state, but require more attention as the top priority for amendment practices. In addition, approximately 8.7% of the entire study area has a high vulnerability to contamination, which requires immediate pragmatic actions.

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Groundwater Potential Mapping Using Maximum Entropy

Cited by 5 publications

References 55 publications

Predicting the Presence of Groundwater-Influenced Ecosystems in the Northeastern United States with Ensembled Models

Predicting the Presence of Groundwater-Influenced Ecosystems in the Northeastern United States with Ensembled Models

Delineating village-level drought risk in Marinduque Island, Philippines

Reciprocal analysis of groundwater potentiality and vulnerability modeling in the Bahabad Plain, Iran

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