Assessment of environmental water requirement for rivers of the Miankaleh wetland drainage basin

Gholami, Vahid; Khalili, Ali; Sahour, Hossein; Khaleghi, Mohammad Reza; Tehrani, E. Nikzad

doi:10.1007/s13201-020-01319-8

Cited by 22 publications

(8 citation statements)

References 31 publications

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“…Some previous studies applied the environmental flow models of the river such as wetted perimeter method to assess the environmental needs of the wetland (e.g. Gholami et al 2020). However, these methods are not reliable method to assess environmental water requirements of the wetland due to incapability to generate the habitat suitability maps of the wetland ecosystem itself, which is a significant weakness for these types of methods.…”

Section: Discussionmentioning

confidence: 99%

An Ecological Expert System Optimization for Assessing Environmental Water Requirements of Hypersaline Lakes

Sedighkia

Abdoli²

2022

Wetlands

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The present study proposes an applicable methodology to optimize environmental water requirement of hypersaline lakes with a focus on Urmia lake as the case study in which remote sensing analysis, machine learning model and fuzzy expert system are linked. A machine learning model was developed to simulate effective abiotic parameters in which bands of operational land imager (Landsat 8) were inputs and depth and total dissolved solids were the outputs of the model. Moreover, an ecological expert system using Mamadani fuzzy inference system was developed to generate the habitat suitability map for the selected target species. Then, a multivariate linear model was developed to assess unit habitat suitability in which water level and total inflow of the lake were the variables of the model. An optimization model was developed to assess environmental water requirement in which habitat suitability between natural and regulated flows and water supply loss was minimized. The multivariate linear model was applied to assess habitat suitability in the optimization model. Based on the results in the case study, the proposed combined model is able to balance the ecological requirements and water demand by allocating 60% and 40% of total inflow to environmental water requirement and water demand respectively. Average habitat loss proposed by the optimal environmental water requirement was less than 20% which implies the robustness of the model. Generating habitat suitability maps of the lake by a reliable method which is used in the environmental flow optimization might be the significance of the proposed method.

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Section: Discussionmentioning

confidence: 99%

An Ecological Expert System Optimization for Assessing Environmental Water Requirements of Hypersaline Lakes

Sedighkia

Abdoli²

2022

Wetlands

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“…The region's main land use and land cover are rice farms, citrus gardens, urban areas, rangelands, and forests. The area is known for its rich ecosystems and wildlifeprotected areas (Gholami et al 2020). The coastal plain of Mazandaran consists mainly of alluvial sediments deposited during the Quaternary period.…”

Section: Study Areamentioning

confidence: 99%

Evaluation of machine learning algorithms for groundwater quality modeling

Sahour¹,

Khanbeyki

Gholami

et al. 2023

Environ Sci Pollut Res

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Groundwater quality is measured through water sampling, and lab analysis. The eld-based measurements are costly and time-consuming when applied over a large domain. In this study, we developed a machine learning-based framework to map groundwater quality in an uncon ned aquifer in the north of Iran. Groundwater samples were provided from 248 monitoring wells across the region. The groundwater quality index (GWQI) in each well was measured and classi ed into four classes of Very poor, Poor, Good, and Excellent according to their cut-off values. Factors affecting groundwater quality, including distance to industrial centers, distance to residential areas, population density, aquifer transmissivity, precipitation, evaporation, geology, and elevation, were identi ed and prepared in the GIS environment. Six machine learning classi ers, including extreme gradient boosting (XGB), random forest (RF), support vector machine (SVM), arti cial neural networks (ANN), k-nearest neighbor (KNN), and Gaussian classi er model (GCM), were used to establish relationships between GWQI and its controlling factors. The algorithms were evaluated using the receiver operating characteristic curve (ROC) and statistical e ciencies (overall accuracy, precision, recall, and f-1 score). Accuracy assessment showed that ML algorithms provided high accuracy in predicting groundwater quality. However, RF was selected as the optimum model given its higher accuracy (overall accuracy, precision, and recall = 0.92; ROC = 0.95). The trained RF model was used to map GWQI classes across the entire region. Results showed that the Poor GWQI class is dominant in the study area and Good GWQI can be found in southwest. An area of Very Poor GWQI was observed in the north. Findings indicated that the distance to industrial locations is the main factor affecting groundwater quality in the area. The study provides a cost-effective methodology in groundwater quality modeling that can be duplicated in other regions with similar hydrological and geo-logical settings.

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“…Miankaleh International Wetland (Coastal Biosphere Reserve) has been recognized as a "Ramsar site" since 1975 because of its cultural inheritance, historical, biological and esthetic characteristics (Gholami et al, 2020;Alipour et al, 2015). Miankaleh Wetland is a protected region, located on the southeast part of the Caspian Sea (Iran), a rich ecosystem and ecological haven in Asia (Ranjbar Jafarabadi et al, 2020).…”

Section: Introductionmentioning

confidence: 99%