Application of artificial neural network in water quality index prediction: a case study in Little Akaki River, Addis Ababa, Ethiopia

Yilma, Mulugeta; Kiflie, Zebene; Windsperger, Andreas; Gessese, Nebiyeleul

doi:10.1007/s40808-018-0437-x

Cited by 37 publications

(14 citation statements)

References 49 publications

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“…Miocene–Pleistocene volcanic successions dominate the geology in Akaki River Basin, including acidic and intermediate lava flows, basaltic lava flows, and pyroclastic flows forming an interlayered sequence with quaternary faults 39 . The region has highly variable and complex aquifers.…”

Section: Methodsmentioning

confidence: 99%

“…In the study area, the major point sources of nitrates are industrial discharges into the river and leaching from waste disposal sites 39 , 62 . Sources with high concentrations such as industrial discharge and leaching from solid waste disposal sites 63 , along Little Akaki River, were selected (Fig.…”

Section: Methodsmentioning

confidence: 99%

“…Nevertheless, regardless of the anthropogenic pollution and over-abstraction in some parts of the basin, the water supply of the city depends entirely on the Akaki River Basin 37 . City expansion and population growth are escalating the demand; the contribution of groundwater to Addis Ababa’s water supply increased over three folds in the last 15 years 31 , 38 , 39 . Groundwater abstraction wells are scattered through the entire basin, including in the city, without protected areas 37 .…”

Section: Study Area Descriptionsmentioning

confidence: 99%

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Assessing the effect of urbanization on regional-scale surface water-groundwater interaction and nitrate transport

Yifru

Chung

Kim

et al. 2022

Sci Rep

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Identifying regional-scale surface water-groundwater interactions (SGI) is vital for predicting anthropogenic effects on surface water bodies and underlying aquifers. However, large-scale water and nutrient flux studies rely on surface water or groundwater-focused models. This study aims to model the effect of urbanization, which is usually accompanied by high groundwater abstraction and surface water pollution, particularly in the developing world, on a regional-scale SGI and nitrate loading. In the study area, the urban expansion increased by over 3% in the last decade. The integrated SWAT-MODFLOW model, Soil and Water Assessment Tool (SWAT) and Modular Finite-Difference Groundwater Flow (MODFLOW) coupling code, was used to assess SGI. By coupling SWAT-MODFLOW with Reactive Transport in 3-Dimensions, the nutrient loading to the river from point and non-point sources was also modeled. Basin average annual results show that groundwater discharge declined with increasing groundwater abstraction and increased with Land use/Land cover (LULC) changes. Groundwater recharge decreased significantly in the Belge season (February to May), and the river seepage and groundwater discharge decreased correspondingly. High spatiotemporal changes in SGI and nitrate loading were found under the combined LULC and groundwater abstraction scenarios. The water yield decreased by 15%. In a large part of the region, the nitrate loading increased by 17–250%. Seasonally controlled groundwater abstraction and water quality monitoring are essential in this region.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Study Area Descriptionsmentioning

confidence: 99%

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Assessing the effect of urbanization on regional-scale surface water-groundwater interaction and nitrate transport

Yifru

Chung

Kim

et al. 2022

Sci Rep

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“…The validation showed that AR-SVM was a powerful method to identify river water quality with 0.86-0.95 accuracy when applied to three to six characteristics. Yilma et al [8] have used an artificial neural network to simulate the Akaki River's WQI. The twelve water quality indicators from 27 dry and wet season sample locations were utilized to calculate the index.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Except for one upstream location, all forecast results have shown low water quality. Here, the number of hidden layers (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20), hidden layer neurons (5,10,15,20,25), transfer, training, and learning functions were used to train and verify the neural network model through 12 inputs and one output. Their study has revealed that an artificial neural network with eight hidden layers and 15 hidden neurons accurately predicted the WQI with an accuracy of 0.93.…”

Section: Literature Reviewmentioning

confidence: 99%

Efficient Prediction of Water Quality Index (WQI) Using Machine Learning Algorithms

Hassan¹,

Hassan²,

Akter³

et al. 2021

HCIS

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The quality of water has a direct influence on both human health and the environment. Water is utilized for a variety of purposes, including drinking, agriculture, and industrial use. The water quality index (WQI) is a critical indication for proper water management. The purpose of this work was to use machine learning techniques such as RF, NN, MLR, SVM, and BTM to categorize a dataset of water quality in various places across India. Water quality is dictated by features such as dissolved oxygen (DO), total coliform (TC), biological oxygen demand (BOD), Nitrate, pH, and electric conductivity (EC). These features are handled in five steps: data pre-processing using min-max normalization and missing data management using RF, feature correlation, applied machine learning classification, and model's feature importance. The highest accuracy Kappa, Accuracy Lower, and Accuracy Upper findings in this research are 99.83, 99.17, 99.07, and 99.99, respectively. The finding showed that Nitrate, PH, conductivity, DO, TC, and BOD are the key qualities that contribute to the orderly classification of water quality,

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Sand Mining Consequences on Habitat Ecology, Water Quality and Species Diversity: Implementing of HSI, MLR, WQI and ANN Methods

Bhattacharya

Chatterjee

2021

River Sand Mining Modelling and Sustainable Practice

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Application of artificial neural network in water quality index prediction: a case study in Little Akaki River, Addis Ababa, Ethiopia

Cited by 37 publications

References 49 publications

Assessing the effect of urbanization on regional-scale surface water-groundwater interaction and nitrate transport

Assessing the effect of urbanization on regional-scale surface water-groundwater interaction and nitrate transport

Efficient Prediction of Water Quality Index (WQI) Using Machine Learning Algorithms

Sand Mining Consequences on Habitat Ecology, Water Quality and Species Diversity: Implementing of HSI, MLR, WQI and ANN Methods

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