GIS-based groundwater vulnerability modelling: A case study of the Witbank, Ermelo and Highveld Coalfields in South Africa

Sakala, Emmanuel; Fourie, F.; Gomo, M.; Coetzee, Henk

doi:10.1016/j.jafrearsci.2017.09.012

Cited by 13 publications

(9 citation statements)

References 22 publications

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“…The sulphate parameter which is a good indicator for AMD was used for model validation and for comparing the performance of the ANN and DRIST groundwater vulnerability models. ANN model with the fuzzy model (Sakala et al, 2018), the correlation results of the ANN are higher. Thus, the deep ANN, which was purposefully built from complex relationship the network learnt from the input-output pairs, improves the correlation.…”

Section: Model Validationmentioning

confidence: 91%

“…AMD is formed when sulphide-bearing material found in coal discard or ore bodies reacts with water in the presence of oxygen. The generated product is characterised by high concentration of toxic heavy metals and sulphide (Sakala et al, 2018). In the study area, a regional knowledge-driven fuzzy expert system was used by (Sakala et al, 2018), which correlated slightly with concentration of sulphate in boreholes.…”

Section: Introductionmentioning

confidence: 99%

“…The generated product is characterised by high concentration of toxic heavy metals and sulphide (Sakala et al, 2018). In the study area, a regional knowledge-driven fuzzy expert system was used by (Sakala et al, 2018), which correlated slightly with concentration of sulphate in boreholes. Due to a large amount of boreholes with sulphide concentration couple with the need to improve this correlation, a data-driven deep neural network approach was used.…”

Section: Introductionmentioning

confidence: 99%

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Groundwater vulnerability mapping of Witbank coalfield in South Africa using deep learning artificial neural networks

Sakala¹,

Fourie²,

Gomo³

et al. 2022

SA J of Geomatics

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This study highlights the usage of deep learning artificial neural networks in the assessment of groundwater vulnerability of a coalfield. The network uses the DRIST model with parameters (depth to water level, recharge, impact of the vadose zone, soils and topographic slope) as training inputs and borehole sulphate concentration as training output. This technique was applied to Witbank coalfield, where acid mine drainage emanating from coal mining operations is a huge concern for surrounding environment and groundwater resources. The generated groundwater vulnerability model was validated with another sulphate dataset not used during model training. The deep neural network model with dropout and decaying learning rate regularisers correlated very well with sulphate data from another source as compared to the index and overlay DRIST model. The approach, differentiated areas in terms of vulnerability to acid mine drainage, which can aid policy, and decision makers to make scientifically informed decisions on land use planning. The approach developed in this research can be applied to other coalfields in order to evaluate its robustness to different hydrogeological and geological conditions.

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Section: Model Validationmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Groundwater vulnerability mapping of Witbank coalfield in South Africa using deep learning artificial neural networks

Sakala¹,

Fourie²,

Gomo³

et al. 2022

SA J of Geomatics

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“…Few studies have been conducted at the nationwide and municipal levels in South Africa to assess and predict groundwater risk [12][13][14][15]. The studies mentioned above were implemented on a larger scale which is limited in providing detailed and precise information at the catchment level.…”

Section: Introductionmentioning

confidence: 99%

Groundwater Vulnerability Assessment Using Modified DRASTIC Index, the Case of Doornfontein Area (Johannesburg)

Assefa¹,

Dinka²

2023

Pol. J. Environ. Stud.

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Groundwater is an essential resource for the domestic, agricultural, and industrial sectors. Nevertheless, groundwater quality has declined due to the increased use of pesticides and industrial effluent being discharged into water systems. This study aims to assess groundwater vulnerability by using the modified DRASTIC model around Doornfontein, Central Business District (CBD) of Johannesburg. Land use types are considered as a model parameter to produce a modified DRASTIC model, where the result varies between 103 and 219 (low to very high, vulnerable zones). Approximately 5.6% of the study area is covered by very high vulnerable zones and distributed in southern region. High, moderate and low vulnerability zone covers the area of 55.3%, 28.8% and 10.3%, respectively. The study uses a nitrate concentration level to validate the accuracy of the model. The correlation analysis between nitrate level and DRASTIC model shows R 2 = 0.548 and significant correlation with the modified DRASTIC model R 2 = 0.56. The single parameter sensitivity analysis reveal that land use, aquifer media, soil media, and topography have a prominent role in assessing vulnerability. The study output shows the area is highly vulnerable to contamination, necessitating the relevant authorities' careful attention and protection of the area's groundwater resources.

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“…Various models have been developed to reduce the risk of aquifer agrochemical pollution. According to Momejian et al (2019), Nistor (2020), Barreto-Neto and Marchesi (2019), Secron et al (2017), Sakala et al (2018, and Calderon et al (2016), the high risk of contaminating local groundwaters near agricultural areas is normally associated with how climate change impacts the rainy season, and how rains, in turn, reinforce the leaching of soils contaminated with agrochemical products produced by agribusiness, in particular insecticides, fungicides, herbicides, and fertilizers. Aside from climate change, other regional characteristics facilitate the transport of agrochemicals, such as:…”

Section: Introductionmentioning

confidence: 99%

Groundwater vulnerability to agrochemical contamination

Soares

Silva

Filho

et al. 2020

Rev. Bras. Ciênc. Ambient. (Online)

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This research aimed at evaluating groundwater vulnerability to agrochemical contamination. To that end, we developed an index called Hydric Vulnerability and Agrochemical Contamination Index (HVACI), which integrates a geographic information system and fuzzy logic to measure catchment vulnerability to agrochemical contamination. Our case study investigates two sub-basins, the Baixo Jaguaribe and the Médio Jaguaribe, in the state of Ceará, Brazil. We built a logical relationship matrix involving economic and environmental information as a tool to enhance public managers' decision-making capabilities. Evaluation was based on four categories of vulnerability — high, medium-high, medium-low, and low —, and we found that the joint area of the Baixo Jaguaribe and Médio Jaguaribe sub-basins presented the following levels of risk contamination: 80.3% of the area had low vulnerability, 3.5% had medium-low vulnerability, 3.0% had medium-high vulnerability, and 13.2% had high vulnerability. Geographically, the municipalities with high vulnerability to contamination by pesticides were Aracati, Icapuí, Limoeiro do Norte, Tabuleiro do Norte, and Quixeré. Therefore, HVACI is an important tool for directing environmental management efforts toward areas identified as highly vulnerable to agrochemical contamination.

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GIS-based groundwater vulnerability modelling: A case study of the Witbank, Ermelo and Highveld Coalfields in South Africa

Cited by 13 publications

References 22 publications

Groundwater vulnerability mapping of Witbank coalfield in South Africa using deep learning artificial neural networks

Groundwater vulnerability mapping of Witbank coalfield in South Africa using deep learning artificial neural networks

Groundwater Vulnerability Assessment Using Modified DRASTIC Index, the Case of Doornfontein Area (Johannesburg)

Groundwater vulnerability to agrochemical contamination

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