Perceptions about water pollution among university students: A case study from Iraq

Al-Maliki, Laheab A.; Farhan, Sabeeh Lafta; Jasim, Ihsan Abbas; Al-Mamoori, Sohaib Kareem; Al‐Ansari, Nadhir

doi:10.1080/23311916.2021.1895473

Cited by 25 publications

(4 citation statements)

References 31 publications

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“…The high temperatures caused the ice sheet to decrease. Studies and research proved that the Arctic ice cover decreased by 10% during the period (1975–1995) (Al-Maliki et al 2021 )…”

Section: Methodsmentioning

confidence: 99%

Perception of climate change effects on water resources: Iraqi undergraduates as a case study

et al. 2022

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Developing countries suffer from the effects of climate change on water resources more than other countries. This paper aims to specify the undergraduates’ knowledge about climate change effects on water resources. This study was conducted through a questionnaire distributed using Google form in May 2021. Descriptive analysis was used to display the level of awareness, and then the correlation between the respondents’ gender, stage, and scientific specialization were analyzed. The results showed that the general knowledge for all questionaries ranged between 40 and 50 %. Moreover, a weak positive correlation with the highest Spearman’s rho of 0.15 was shown for the students’ gender with their understanding of climate change main cause by 0.06 for the academic stage versus knowledge of climate change causes. Moreover, the results indicated a weak role for university education in exchange for a prominent role for television and social media in raising awareness. The research recommends integrating the Environmental Education (EE) programs into the Iraqi educational systems as it proposes a systematic educational method through which climate change and other environmental problems may be addressed holistically at all educational levels.

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“…The high temperatures caused the ice sheet to decrease. Studies and research proved that the Arctic ice cover decreased by 10% during the period (1975–1995) (Al-Maliki et al 2021 )…”

Section: Methodsmentioning

confidence: 99%

Perception of climate change effects on water resources: Iraqi undergraduates as a case study

et al. 2022

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“…Iraq is particularly susceptible to the efects of global warming as a country in an arid to semiarid environment. In general, this country is sufering from a shortage of water, which is anticipated to worsen in the future due to various issues, including climate change, the growth of the oil sector industry, urbanisation, and the high pace of the population increase [5]. Iraq's primary freshwater sources are the Euphrates and Tigris Rivers.…”

Section: Introductionmentioning

confidence: 99%

Application of Metaheuristic Algorithms and ANN Model for Univariate Water Level Forecasting

Mohammed

Zubaidi

Al‐Ansari

et al. 2023

Advances in Civil Engineering

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With the rapid development of machine learning (ML) models, the artificial neural network (ANN) is being increasingly applied for forecasting hydrological processes. However, researchers have not treated hybrid ML models in much detail. To address these issues, this study herein suggests a novel methodology to forecast the monthly water level (WL) based on multiple lags of the Tigris River in Al-Kut, Iraq, over ten years. The methodology includes preprocessing data methods, and the ANN model optimises with a marine predator algorithm (MPA). In the optimisation procedure, to decrease uncertainty and expand the predicting range, the slime mould algorithm (SMA-ANN), constriction coefficient-based particle swarm optimisation and chaotic gravitational search algorithms (CPSOCGSA-ANN), and particle swarm optimisation (PSO-ANN) are applied to compare and validate the MPA-ANN model performance. Analysis of results revealed that the data pretreatment methods improved the original data quality and selected the ideal predictors’ scenario by singular spectrum analysis and mutual information methods, respectively. For example, the correlation coefficient of the first lag improved from 0.648 to 0.938. Depending on various evaluation metrics, MPA-ANN tends to forecast WL better than SMA-ANN, PSO-ANN, and CPSOCGSA-ANN algorithms with coefficients of determination of 0.94, 0.81, 0.85, and 0.90, respectively. Evidence shows that the proposed methodology yields excellent results, with a scatter index equal to 0.002. The research outcomes represent an additional step towards evolving various hybrid ML techniques, which are valuable to practitioners wishing to forecast WL data and the management of water resources in light of environmental shifts.

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“…Iraq is an Arab Country in the Middle East that is one of the most sensitive to climate change due to its location in an arid to semiarid region. This area is generally experiencing a water shortage, which is expected to worsen due to various factors such as climate change, the oil industry, urbanization, and rapid population growth [7]. The Euphrates and Tigris rivers are Iraq's principal freshwater supplies.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Technique to Improve the River Water Level Forecasting Using Artificial Neural Network-Based Marine Predators Algorithm

Mohammed

Zubaidi

Al‐Ansari

et al. 2022

Advances in Civil Engineering

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Water level (WL) forecasting has become a difficult undertaking due to spatiotemporal fluctuations in climatic factors and complex physical processes. This paper proposes a novel hybrid machine learning model based on an artificial neural network (ANN) and the Marine Predators algorithm (MPA) for modeling monthly water levels of the Tigris River in Al-Kut, Iraq. Data preprocessing techniques are employed to enhance data quality and determine the optimal input model. Historical data for water level and climatic factors data are utilized from 2011 to 2020 to build and assess the model. MPA-ANN algorithm’s performance is compared with recent constriction coefficient-based particle swarm optimization and chaotic gravitational search algorithm (CPSOCGSA-ANN) and slime mold algorithm (SMA-ANN) to reduce uncertainty and raise the prediction range. The finding demonstrated that singular spectrum analysis is a highly effective method to denoise time series. MPA-ANN outperformed CPSOCGSA-ANN and SMA-ANN algorithms based on different statistical criteria. The suggested novel methodology offers good results with scatter index (SI) = 0.0009 and coefficient of determination (R2 = 0.98).

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Perceptions about water pollution among university students: A case study from Iraq

Cited by 25 publications

References 31 publications

Perception of climate change effects on water resources: Iraqi undergraduates as a case study

Perception of climate change effects on water resources: Iraqi undergraduates as a case study

Application of Metaheuristic Algorithms and ANN Model for Univariate Water Level Forecasting

Hybrid Technique to Improve the River Water Level Forecasting Using Artificial Neural Network-Based Marine Predators Algorithm

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