ArticleEnvironmental science and pollution research international2021
Proposed formulation of surface water quality and modelling using gene expression, machine learning, and regression techniques.
Article in Environmental science and pollution research international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Air quality prediction and control systems using machine learning and adaptive neuro-fuzzy inference system.Heliyon · 2024Article
- Numerical and artificial intelligence based investigation on the development of design guidelines for pultruded GFRP RHS profiles subjected to web crippling.Scientific reports · 2024Article
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- Advances in machine learning and IoT for water quality monitoring: A comprehensive review.Heliyon · 2024Review
- The application of Rumex abyssinicus based activated carbon for Brilliant Blue Reactive dye adsorption from aqueous solution.BMC chemistry · 2023Article
- Improving Water Quality Index Prediction Using Regression Learning Models.International journal of environmental research and public health · 2022Article
- Investigating the Bond Strength of FRP Rebars in Concrete under High Temperature Using Gene-Expression Programming Model.Polymers · 2022Article
- Prediction Models for Evaluating Resilient Modulus of Stabilized Aggregate Bases in Wet and Dry Alternating Environments: ANN and GEP Approaches.Materials (Basel, Switzerland) · 2022Article
- Compressive Strength Estimation of Fly Ash/Slag Based Green Concrete by Deploying Artificial Intelligence Models.Materials (Basel, Switzerland) · 2022Article
- GEP Tree-Based Prediction Model for Interfacial Bond Strength of Externally Bonded FRP Laminates on Grooves with Concrete Prism.Polymers · 2022Article
- Predictive Modeling of Compression Strength of Waste PET/SCM Blended Cementitious Grout Using Gene Expression Programming.Materials (Basel, Switzerland) · 2022Article
- Predicting the Lateral Load Carrying Capacity of Reinforced Concrete Rectangular Columns: Gene Expression Programming.Materials (Basel, Switzerland) · 2022Article
- Predictive Modeling of Mechanical Properties of Silica Fume-Based Green Concrete Using Artificial Intelligence Approaches: MLPNN, ANFIS, and GEP.Materials (Basel, Switzerland) · 2021Article
- Multigene Expression Programming Based Forecasting the Hardened Properties of Sustainable Bagasse Ash Concrete.Materials (Basel, Switzerland) · 2021Article
- Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm.Materials (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rising water pollution from anthropogenic factors motivates further research in developing water quality predicting models. The available models have certain limitations due to limited timespan data and the incapability to provide empirical expressions. This study is devoted to model and derive empirical equations for surface water quality of upper Indus river basin using a 30-year dataset with machine learning techniques and then to determine the most reliable model capable to accurately predict river water quality. Total dissolve solids (TDS) and electrical conductivity (EC) were used as dependent variables, whereas eight parameters were used as independent variables with 70 and 30% data for model training and testing, respectively. Various evaluation criteria, i.e., Nash-Sutcliffe efficiency (NSE), root mean square error (RMSE), coefficient of determination (R
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.