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Artificial intelligence for surface water quality monitoring and assessment: a systematic literature analysis

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Abstract

The goal of this paper was to conduct a systematic literature analysis on the application of different types of artificial intelligence models in surface water quality monitoring. The analysis focused on the methods used, the location of the experiments, the input parameters, and the output metrics applied to categorise the results presented. Furthermore, the main contribution of this paper is to synthesise the existing body of knowledge in the state of the art (the last decade) and identified common threads and gaps that would open up new challenging, exciting and significant research directions. From the study, it was observed that Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANN) are the most utilised artificial intelligence models for water quality monitoring and assessment in the last decade. Most of the studies using Neural Networks in surface water quality monitoring and assessment are originated in Iran and south-east Asia. ANFIS, Wavelet-ANN (W-ANN) and Wavelet-ANFIS (W-ANFIS) were most accurate for the prediction of surface water quality. There was no clear relationship between data size and R2 value (at the testing stage). Biochemical oxygen demand (BOD) was the most investigated parameter in surface water quality monitoring and assessment. An appraisal of recent literature was also presented and knowledge gaps and future perspective in the research area were proposed.

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The authors declare that there is no external data or materials involved in this study.

Abbreviations

AI:

Artificial Intelligence

ANFIS:

Adaptive Neuro-Fuzzy Inference System

ANN:

Artificial Neural Network

ARIMA:

Linear Auto-Regressive Integrated Moving Average

BOD:

Biochemical Oxygen Demand

BPNN:

Back Propagation Neural Network

CFS:

Cascaded Fuzzy Inference System

COD:

Chemical Oxygen Demand

DO:

Dissolved Oxygen

EC:

Electrical Conductivity

ELM:

Extreme Learning Machine

ENN:

Elman Neural Network

FIS:

Fuzzy Inference System

MLP:

Multi-Layer Perceptron Neural Network

NARX:

Nonlinear Autoregressive (with exogenous input) Network

NN-CS:

Neural Network trained by Cuckoo Search Algorithm

NN-GA:

Neural Network trained by Genetic Algorithm

NN-PSO:

Neural Network trained by Particle Swarm Algorithm

PI:

Permanganate Index

R2 :

Coefficient of Determination

RBF:

Radial Basis Function Neural Network

RMSE:

Root Mean Square Error

SAR:

Sodium Absorption Ratio

SVM:

Support Vector Machine

TA:

Total alkalinity

TH:

Total Hardness

TN:

Total Nitrogen

TP:

Total Phosphorus

TS:

Total Solids

TDS:

Total Dissolved Solids

TSS:

Total Suspended Solids

W-ANFIS:

Wavelet Adaptive Neuro-Fuzzy Inference System

W-ANN:

Wavelet Artificial Neural Network

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Acknowledgement

The authors would like to acknowledge all researchers whose papers were cited in the current review.

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The authors declare that there is no funding involved in this research.

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Authors and Affiliations

  1. Department of Chemical Engineering, University of Ilorin, P. M. B. 1515, Ilorin, Nigeria

    Joshua O. Ighalo & Adewale George Adeniyi

  2. Department of Chemical Engineering, Nnamdi Azikiwe University, P. M. B. 5025, Awka, Nigeria

    Joshua O. Ighalo

  3. Polytechnic of Coimbra, ESTGOH, Rua General Santos Costa, 3400-124, Oliveira Do Hospital, Portugal

    Gonçalo Marques

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  1. Joshua O. Ighalo
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  2. Adewale George Adeniyi
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  3. Gonçalo Marques
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Correspondence to Gonçalo Marques.

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Ighalo, J.O., Adeniyi, A.G. & Marques, G. Artificial intelligence for surface water quality monitoring and assessment: a systematic literature analysis. Model. Earth Syst. Environ. 7, 669–681 (2021). https://doi.org/10.1007/s40808-020-01041-z

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