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Artificial Intelligence and Machine Learning Applications in Water Resources Management

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A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water Resources Management, Policy and Governance".

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 50988

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Special Issue Editor

Prof. Dr. Mustafa M. Aral

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School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
Interests: computational methods; stochastic methods; large-scale environmental systems; climate change and sea level rise; water and wastewater treatment; water and health systems policy, adaptation, and mitigation; ecosystem restoration and resilience analysis; sensors and critical infrastructure protection and management; trans-border water assessments and management; population dynamics
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Dear Colleagues,

Artificial Intelligence and Machine learning techniques are expected to be at the center of many fields of engineering and sciences in the next digital technology period. With the advances made in computer hardware and software technologies and the introduction of automation applications, the use of these smart methodologies is gaining more and more importance in the analysis, evaluation, and management of very complex problems. Computers are also becoming smaller, faster, and cheaper, with the desired outcome of being more accessible to many engineers and researchers in diverse fields. One of these fields is water resources, which has the widest application range and covers an area from watershed applications to groundwater and surface water resources management, to engineered systems applications such as water distribution systems and water treatment technologies, etc. At the center of all these fields are advances made in numerical, stochastic, artificial intelligence, and machine learning algorithms that are used in these systems.

We also recognize that the field of water resources is common to all the applications listed above. As such, the solution strategies and the problems encountered are also common. With this observation in mind, the aim of the proposed “Artificial Intelligence and Machine Learning Applications in Water Resources Management” Special Issue in the Water journal is to create a forum of exchange among diverse fields of water resources applications that develop and use these methods. Since the methodologies that are developed and used in one field may find applications in other fields of water resources, the purpose of this Special Issue is to provide an open repository of these algorithms at one source.

Prof. Dr. Mustafa M. Aral
Guest Editor

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Keywords

water resources
artificial intelligence
machine learning
computational methods
stochastic methods
watershed applications
surface water
groundwater
water treatment
engineered systems applications

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Published Papers (7 papers)

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Research

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16 pages, 3567 KiB

Open AccessArticle

Successive-Station Streamflow Prediction and Precipitation Uncertainty Analysis in the Zarrineh River Basin Using a Machine Learning Technique

by Mahdi Nakhaei, Fereydoun Ghazban, Pouria Nakhaei, Mohammad Gheibi, Stanisław Wacławek and Mehdi Ahmadi

Water 2023, 15(5), 999; https://doi.org/10.3390/w15050999 - 6 Mar 2023

Cited by 6 | Viewed by 2701

Abstract

Precise forecasting of streamflow is crucial for the proper supervision of water resources. The purpose of the present investigation is to predict successive-station streamflow using the Gated Recurrent Unit (GRU) model and to quantify the impact of input information (i.e., precipitation) uncertainty on the GRU model’s prediction using the Generalized Likelihood Uncertainty Estimation (GLUE) computation. The Zarrineh River basin in Lake Urmia, Iran, was nominated as the case study due to the importance of the location and its significant contribution to the lake inflow. Four stations in the basin were considered to predict successive-station streamflow from upstream to downstream. The GRU model yielded highly accurate streamflow prediction in all stations. The future precipitation data generated under the Representative Concentration Pathway (RCP) scenarios were used to estimate the effect of precipitation input uncertainty on streamflow prediction. The p-factor (inside the uncertainty interval) and r-factor (width of the uncertainty interval) indices were used to evaluate the streamflow prediction uncertainty. GLUE predicted reliable uncertainty ranges for all the stations from 0.47 to 0.57 for the r-factor and 61.6% to 89.3% for the p-factor. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications in Water Resources Management)

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20 pages, 9201 KiB

Open AccessArticle

Feature Extraction and Prediction of Water Quality Based on Candlestick Theory and Deep Learning Methods

by Rui Xu, Wenjie Wu, Yanpeng Cai, Hang Wan, Jian Li, Qin Zhu and Shiming Shen

Water 2023, 15(5), 845; https://doi.org/10.3390/w15050845 - 22 Feb 2023

Cited by 3 | Viewed by 2578

Abstract

In environmental hydrodynamics, a research topic that has gained popularity is the transmission and diffusion of water pollutants. Various types of change processes in hydrological and water quality are directly related to meteorological changes. If these changing characteristics are classified effectively, this will be conducive to the application of deep learning theory in water pollution simulation. When periodically monitoring water quality, data were represented with a candlestick chart, and different classification features were displayed. The water quality data from the research area from 2012 to 2019 generated 24 classification results in line with the physics laws. Therefore, a deep learning water pollution prediction method was proposed to classify the changing process of pollution to improve the prediction accuracy of water quality, based on candlestick theory, visual geometry group, and gate recurrent unit (CT-VGG-GRU). In this method, after the periodic changes of water quality were represented by candlestick graphically, the features were extracted by the VGG network based on its advantages in graphic feature extraction. Then, this feature and other scenario parameters were fused as the input of the time series network model, and the pollutant concentration sequence at the predicted station constituted the output of the model. Finally, a hybrid model combining graphical and time series features was formed, and this model used continuous time series data from multiple stations on the Lijiang River watershed to train and validate the model. Experimental results indicated that, compared with other comparison models, such as the back propagation neural network (BPNN), support vector regression (SVR), GRU, and VGG-GRU, the proposed model had the highest prediction accuracy, especially for the prediction of extreme values. Additionally, the change trend of water pollution was closer to the real situation, which indicated that the process change information of water pollution could be fully extracted by the CT-VGG-GRU model based on candlestick theory. For the water quality indicators DO, COD_Mn, and NH₃-N, the mean absolute errors (MAE) were 0.284, 0.113, and 0.014, the root mean square errors (RMSE) were 0.315, 0.122, and 0.016, and the symmetric mean absolute percentage errors (SMAPE) were 0.022, 0.108, and 0.127, respectively. The established CT-VGG-GRU model achieved superior computational performance. Using the proposed model, the classification information of the river pollution process could be obtained effectively and the time series information could also be retained, which made the application of the deep learning model to the transmission and diffusion process of river water pollution more explanatory. The proposed model can provide a new method for water quality prediction. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications in Water Resources Management)

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24 pages, 9904 KiB

Open AccessArticle

Understanding the Effect of Hydro-Climatological Parameters on Dam Seepage Using Shapley Additive Explanation (SHAP): A Case Study of Earth-Fill Tarbela Dam, Pakistan

by Muhammad Ishfaque, Saad Salman, Khan Zaib Jadoon, Abid Ali Khan Danish, Kifayat Ullah Bangash and Dai Qianwei

Water 2022, 14(17), 2598; https://doi.org/10.3390/w14172598 - 23 Aug 2022

Cited by 16 | Viewed by 4022

Abstract

For better stability, safety and water resource management in a dam, it is important to evaluate the amount of seepage from the dam body. This research is focused on machine learning approach to predict the amount of seepage from Pakistan’s Earth and rock fill Tarbela Dam during 2003 to 2015. The data of temperature, rainfall, water inflow, sediment inflow, reservoir level collected during 2003 to 2015 served as input while the seepage from dam during this period was the output. Artificial Neural Network (ANN), Random Forest (RF), Support Vector Machine (SVM), and CatBoost (CB), have been used to model the input-output relationship. The algorithms used to predict the dam seepage reported a high R² scores between actual and predicted values of average seepage, suggesting their reliability in predicting the seepage in the Tarbela Dam. Moreover, the CatBoost algorithm outperformed, by achieving an R² score of 0.978 in training, 0.805 in validation, and 0.773 in testing phase. Similarly, RMSE was 0.025 in training, 0.076 in validation, and 0.111 in testing phase. Furthermore, to understand the sensitivity of each parameter on the output (average seepage), Shapley Additive Explanations (SHAP), a model explanation algorithm, was used to understand the affect of each parameter on the output. A comparison of SHAP used for all the machine learning models is also presented. According to SHAP summary plots, reservoir level was reported as the most significant parameter, affecting the average seepage in Tarbela Dam. Moreover, a direct relationship was observed between reservoir level and average seepage. It was concluded that the machine learning models are reliable in predicting and understanding the dam seepage in the Tarbela Dam. These Machine Learning models address the limitations of humans in data collecting and analysis which is highly prone to errors, hence arriving at misleading information that can lead to dam failure. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications in Water Resources Management)

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16 pages, 1624 KiB

Open AccessArticle

Estimating the Standardized Precipitation Evapotranspiration Index Using Data-Driven Techniques: A Regional Study of Bangladesh

by Ahmed Elbeltagi, Faisal AlThobiani, Mohammad Kamruzzaman, Shamsuddin Shaid, Dilip Kumar Roy, Limon Deb, Md Mazadul Islam, Palash Kumar Kundu and Md. Mizanur Rahman

Water 2022, 14(11), 1764; https://doi.org/10.3390/w14111764 - 30 May 2022

Cited by 11 | Viewed by 8280

Abstract

Drought prediction is the most effective way to mitigate drought impacts. The current study examined the ability of three renowned machine learning models, namely additive regression (AR), random subspace (RSS), and M5P tree, and their hybridized versions (AR-RSS, AR-M5P, RSS-M5P, and AR-RSS-M5P) in predicting the standardized precipitation evapotranspiration index (SPEI) in multiple time scales. The SPEIs were calculated using monthly rainfall and temperature data over 39 years (1980–2018). The best subset regression model and sensitivity analysis were used to determine the most appropriate input variables from a series of input combinations involving up to eight SPEI lags. The models were built at Rajshahi station and validated at four other sites (Mymensingh, Rangpur, Bogra, and Khulna) in drought-prone northern Bangladesh. The findings indicated that the proposed models can accurately forecast droughts at the Rajshahi station. The M5P model predicted the SPEIs better than the other models, with the lowest mean absolute error (27.89–62.92%), relative absolute error (0.39–0.67), mean absolute error (0.208–0.49), root mean square error (0.39–0.67) and highest correlation coefficient (0.75–0.98). Moreover, the M5P model could accurately forecast droughts with different time scales at validation locations. The prediction accuracy was better for droughts with longer periods. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications in Water Resources Management)

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19 pages, 6731 KiB

Open AccessArticle

Optimal Design of Water Treatment Contact Tanks

by Mustafa M. Aral

Water 2022, 14(6), 973; https://doi.org/10.3390/w14060973 - 19 Mar 2022

Cited by 1 | Viewed by 11274

Abstract

In water treatment facilities, the last step of the treatment process includes disinfectant application to improve the water quality appropriate for a specific end-use purpose. At this step, contact tanks are used to mix water with the disinfectant. Mixing in contact tanks mainly relies on mechanical mixing processes to mix water with the disinfectant to activate the removal process. Thus, mixing efficiency of the contact tank design is critical for the reduction in the amount of disinfectant used to treat a fixed volume of water, to reduce the energy requirements to derive the treated volume of water through the system and to improve other design considerations of the contact tanks. There are numerous design alternatives reported in the literature that do achieve some of these purposes to a certain extent. Among the recent and more successful designs, one can cite the slot-baffle, the perforated-baffle, and the porous-baffle designs. Although these designs provide important improvements to the mixing process, the studies in which these concepts are reported did not provide an optimal design for the baffle geometry used in the design that would include other important considerations beyond the baffle geometry. In this paper, a new optimal design concept is introduced where important design considerations that are not considered in earlier studies are included in the analysis. The results show that new baffle geometries are possible for the optimal design of contact tanks when these innovative design criteria are included in the analysis. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications in Water Resources Management)

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19 pages, 5612 KiB

Open AccessArticle

Developing Predictive Equations for Water Capturing Performance and Sediment Release Efficiency for Coanda Intakes Using Artificial Intelligence Methods

by Oğuz Hazar, Gokmen Tayfur, Sebnem Elçi and Vijay P. Singh

Water 2022, 14(6), 972; https://doi.org/10.3390/w14060972 - 19 Mar 2022

Cited by 3 | Viewed by 2600

Abstract

Estimation of withdrawal water and filtered sediment amounts are important to obtain maximum efficiency from an intake structure. The purpose of this study is to develop empirical equations to predict Water Capturing Performance (WCP) and Sediment Release Efficiency (SRE) for Coanda type intakes. These equations were developed using 216 sets of experimental data. Intakes were tested under six different slopes, six screens, and three water discharges. In SRE experiments, sediment concentration was kept constant. Dimensionless parameters were first developed and then subjected to multicollinearity analysis. Then, nonlinear equations were proposed whose exponents and coefficients were obtained using the Genetic Algorithm method. The equations were calibrated and validated with 70 and 30% of the data, respectively. The validation results revealed that the empirical equations produced low MAE and RMSE and high R² values for both the WCP and the SRE. Results showed outperformance of the empirical equations against those of MNLR. Sensitivity analysis carried out by the ANNs revealed that the geometric parameters of the intake were comparably more sensitive than the flow characteristics. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications in Water Resources Management)

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Review

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28 pages, 4122 KiB

Open AccessReview

Application of Machine Learning in Water Resources Management: A Systematic Literature Review

by Fatemeh Ghobadi and Doosun Kang

Water 2023, 15(4), 620; https://doi.org/10.3390/w15040620 - 5 Feb 2023

Cited by 40 | Viewed by 17308

Abstract

In accordance with the rapid proliferation of machine learning (ML) and data management, ML applications have evolved to encompass all engineering disciplines. Owing to the importance of the world’s water supply throughout the rest of this century, much research has been concentrated on the application of ML strategies to integrated water resources management (WRM). Thus, a thorough and well-organized review of that research is required. To accommodate the underlying knowledge and interests of both artificial intelligence (AI) and the unresolved issues of ML in WRM, this overview divides the core fundamentals, major applications, and ongoing issues into two sections. First, the basic applications of ML are categorized into three main groups, prediction, clustering, and reinforcement learning. Moreover, the literature is organized in each field according to new perspectives, and research patterns are indicated so attention can be directed toward where the field is headed. In the second part, the less investigated field of WRM is addressed to provide grounds for future studies. The widespread applications of ML tools are projected to accelerate the formation of sustainable WRM plans over the next decade. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications in Water Resources Management)

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