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Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (31 January 2021) | Viewed by 29516

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Special Issue Editor

Department of Civil Engineering, Faculty of Natural Sciences and Engineering Ilia State University, 0162 Tbilisi, Georgia
Interests: developing novel algorithms and methods towards the innovative solution of hydrologic forecasting and modeling; suspended sediment modeling; forecasting, estimation, and spatial and temporal analysis of hydro-climatic variables such as precipitation, streamflow, suspended sediment, evaporation, evapotranspiration, groundwater, lake level and water quality parameters; hydro-informatics
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Special Issue Information

Dear Colleagues,

Water resources management (WR) mostly necessitates the prediction or estimation of the nonlinear phenomena (e.g., the parameters related to hydrological cycle). With climate change and population growth in the most parts of the world, the solution of such problems necessitates advanced computational tools. The main aim of this Special Issue is to explore various implementations of machine learning methods (MLM) improved with metaheuristic algorithms (MAs) to advance prediction and/or modeling hydrological/water resources phenomena which have vital importance in management of water resources. The topics of this Special Issue include, but are not limited to:

  • Hydrologic forecasting (modeling streamflow, sediment, groundwater, lake level, evaporation, evapotranspiration etc.) with advanced MLM
  • Implementation of MLM with new metaheuristic algorithms in WR
  • Reservoir operation using MAs
  • Ensemble modeling procedure with MLM in WR
  • Application of conjunction MLM such as wavelet or EEMD based MLM

Prof. Ozgur Kisi
Guest Editor

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Keywords

  • Sustainability in water resources management
  • Machine learning in WR
  • Hybrid modeling with MLM
  • Hydrologic modeling with advanced MLM
  • MAs implementation in WR

Published Papers (11 papers)

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Editorial

Jump to: Research, Review

2 pages, 145 KiB  
Editorial
Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management
by Ozgur Kisi
Sustainability 2021, 13(15), 8596; https://doi.org/10.3390/su13158596 - 02 Aug 2021
Cited by 4 | Viewed by 1344
Abstract
Management of available water resources needs good planning and to do this, prognostication of hydrological parameters (parameters of the hydrological cycle such as rainfall, runoff, solar radiation, groundwater, evaporation/evapotranspiration) [...] Full article

Research

Jump to: Editorial, Review

23 pages, 5151 KiB  
Article
Prognostication of Shortwave Radiation Using an Improved No-Tuned Fast Machine Learning
by Isa Ebtehaj, Keyvan Soltani, Afshin Amiri, Marzban Faramarzi, Chandra A. Madramootoo and Hossein Bonakdari
Sustainability 2021, 13(14), 8009; https://doi.org/10.3390/su13148009 - 17 Jul 2021
Cited by 13 | Viewed by 1825
Abstract
Shortwave radiation density flux (SRDF) modeling can be key in estimating actual evapotranspiration in plants. SRDF is the result of the specific and scattered reflection of shortwave radiation by the underlying surface. SRDF can have profound effects on some plant biophysical processes such [...] Read more.
Shortwave radiation density flux (SRDF) modeling can be key in estimating actual evapotranspiration in plants. SRDF is the result of the specific and scattered reflection of shortwave radiation by the underlying surface. SRDF can have profound effects on some plant biophysical processes such as photosynthesis and land surface energy budgets. Since it is the main energy source for most atmospheric phenomena, SRDF is also widely used in numerical weather forecasting. In the current study, an improved version of the extreme learning machine was developed for SRDF forecasting using the historical value of this variable. To do that, the SRDF through 1981–2019 was extracted by developing JavaScript-based coding in the Google Earth Engine. The most important lags were found using the auto-correlation function and defined fifteen input combinations to model SRDF using the improved extreme learning machine (IELM). The performance of the developed model is evaluated based on the correlation coefficient (R), root mean square error (RMSE), mean absolute percentage error (MAPE), and Nash–Sutcliffe efficiency (NSE). The shortwave radiation was developed for two time ahead forecasting (R = 0.986, RMSE = 21.11, MAPE = 8.68%, NSE = 0.97). Additionally, the estimation uncertainty of the developed improved extreme learning machine is quantified and compared with classical ELM and found to be the least with a value of ±3.64 compared to ±6.9 for the classical extreme learning machine. IELM not only overcomes the limitation of the classical extreme learning machine in random adjusting of bias of hidden neurons and input weights but also provides a simple matrix-based method for practical tasks so that there is no need to have any knowledge of the improved extreme learning machine to use it. Full article
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26 pages, 8498 KiB  
Article
Evaluating Irrigation Performance and Water Productivity Using EEFlux ET and NDVI
by Usha Poudel, Haroon Stephen and Sajjad Ahmad
Sustainability 2021, 13(14), 7967; https://doi.org/10.3390/su13147967 - 16 Jul 2021
Cited by 14 | Viewed by 3303
Abstract
Southern California’s Imperial Valley (IV) faces serious water management concerns due to its semi-arid environment, water-intensive crops and limited water supply. Accurate and reliable irrigation system performance and water productivity information is required in order to assess and improve the current water management [...] Read more.
Southern California’s Imperial Valley (IV) faces serious water management concerns due to its semi-arid environment, water-intensive crops and limited water supply. Accurate and reliable irrigation system performance and water productivity information is required in order to assess and improve the current water management strategies. This study evaluates the spatially distributed irrigation equity, adequacy and crop water productivity (CWP) for two water-intensive crops, alfalfa and sugar beet, using remotely sensed data and a geographical information system for the 2018/2019 crop growing season. The actual crop evapotranspiration (ETa) was mapped in Google Earth Engine Evapotranspiration Flux, using the linear interpolation method in R version 4.0.2. The approx() function in the base R was used to produce daily ETa maps, and then totaled to compute the ETa for the whole season. The equity and adequacy were determined according to the ETa’s coefficient of variation (CV) and relative evapotranspiration (RET), respectively. The crop classification was performed using a machine learning approach (a random forest algorithm). The CWP was computed as a ratio of the crop yield to the crop water use, employing yield disaggregation to map the crop yield, using county-level production statistics data and normalized difference vegetation index (NDVI) images. The relative errors (RE) of the ETa compared to the reported literature values were 7–27% for alfalfa and 0–3% for sugar beet. The average ETa variation was low; however, the spatial variation within the fields showed that 35% had a variability greater than 10%. The RET was high, indicating adequate irrigation; 31.5% of the alfalfa and 12% of the sugar beet fields clustered in the Valley’s central corner were consuming more water than their potential visibly. The CWP showed wide variation, with CVs of 32.92% for alfalfa and 25.4% for sugar beet, signifying a substantial scope for CWP enhancement. The correlation between the CWP, ETa and yield showed that reducing the ETa to approximately 1500 mm for alfalfa and 1200 mm for sugar beet would help boost the CWP without decreasing the yield, which is nearly equivalent to 44.52M cu. m (36,000 acre-ft) of water. The study’s results could help water managers to identify poorly performing fields where water conservation and management could be focused. Full article
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26 pages, 7050 KiB  
Article
Estimation of Fuzzy Parameters in the Linear Muskingum Model with the Aid of Particle Swarm Optimization
by Mike Spiliotis, Alvaro Sordo-Ward and Luis Garrote
Sustainability 2021, 13(13), 7152; https://doi.org/10.3390/su13137152 - 25 Jun 2021
Cited by 6 | Viewed by 1598
Abstract
The Muskingum method is one of the widely used methods for lumped flood routing in natural rivers. Calibration of its parameters remains an active challenge for the researchers. The task has been mostly addressed by using crisp numbers, but fuzzy seems a reasonable [...] Read more.
The Muskingum method is one of the widely used methods for lumped flood routing in natural rivers. Calibration of its parameters remains an active challenge for the researchers. The task has been mostly addressed by using crisp numbers, but fuzzy seems a reasonable alternative to account for parameter uncertainty. In this work, a fuzzy Muskingum model is proposed where the assessment of the outflow as a fuzzy quantity is based on the crisp linear Muskingum method but with fuzzy parameters as inputs. This calculation can be achieved based on the extension principle of the fuzzy sets and logic. The critical point is the calibration of the proposed fuzzy extension of the Muskingum method. Due to complexity of the model, the particle swarm optimization (PSO) method is used to enable the use of a simulation process for each possible solution that composes the swarm. A weighted sum of several performance criteria is used as the fitness function of the PSO. The function accounts for the inclusive constraints (the property that the data must be included within the produced fuzzy band) and for the magnitude of the fuzzy band, since large uncertainty may render the model non-functional. Four case studies from the references are used to benchmark the proposed method, including smooth, double, and non-smooth data and a complex, real case study that shows the advantages of the approach. The use of fuzzy parameters is closer to the uncertain nature of the problem. The new methodology increases the reliability of the prediction. Furthermore, the produced fuzzy band can include, to a significant degree, the observed data and the output of the existent crisp methodologies even if they include more complex assumptions. Full article
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19 pages, 4603 KiB  
Article
Novel Ensemble Forecasting of Streamflow Using Locally Weighted Learning Algorithm
by Rana Muhammad Adnan, Abolfazl Jaafari, Aadhityaa Mohanavelu, Ozgur Kisi and Ahmed Elbeltagi
Sustainability 2021, 13(11), 5877; https://doi.org/10.3390/su13115877 - 24 May 2021
Cited by 30 | Viewed by 1980
Abstract
The development of advanced computational models for improving the accuracy of streamflow forecasting could save time and cost for sustainable water resource management. In this study, a locally weighted learning (LWL) algorithm is combined with the Additive Regression (AR), Bagging (BG), Dagging (DG), [...] Read more.
The development of advanced computational models for improving the accuracy of streamflow forecasting could save time and cost for sustainable water resource management. In this study, a locally weighted learning (LWL) algorithm is combined with the Additive Regression (AR), Bagging (BG), Dagging (DG), Random Subspace (RS), and Rotation Forest (RF) ensemble techniques for the streamflow forecasting in the Jhelum Catchment, Pakistan. To build the models, we grouped the initial parameters into four different scenarios (M1–M4) of input data with a five-fold cross-validation (I–V) approach. To evaluate the accuracy of the developed ensemble models, previous lagged values of streamflow were used as inputs whereas the cross-validation technique and periodicity input were used to examine prediction accuracy on the basis of root correlation coefficient (R), root mean squared error (RMSE), mean absolute error (MAE), relative absolute error (RAE), and root relative squared error (RRSE). The results showed that the incorporation of periodicity (i.e., MN) as an additional input variable considerably improved both the training performance and predictive performance of the models. A comparison between the results obtained from the input combinations III and IV revealed a significant performance improvement. The cross-validation revealed that the dataset M3 provided more accurate results compared to the other datasets. While all the ensemble models successfully outperformed the standalone LWL model, the ensemble LWL-AR model was identified as the best model. Our study demonstrated that the ensemble modeling approach is a robust and promising alternative to the single forecasting of streamflow that should be further investigated with different datasets from other regions around the world. Full article
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15 pages, 2103 KiB  
Article
Machine Learning Improvement of Streamflow Simulation by Utilizing Remote Sensing Data and Potential Application in Guiding Reservoir Operation
by Shaokun He, Lei Gu, Jing Tian, Lele Deng, Jiabo Yin, Zhen Liao, Ziyue Zeng, Youjiang Shen and Yu Hui
Sustainability 2021, 13(7), 3645; https://doi.org/10.3390/su13073645 - 25 Mar 2021
Cited by 7 | Viewed by 2191
Abstract
Hydro-meteorological datasets are key components for understanding physical hydrological processes, but the scarcity of observational data hinders their potential application in poorly gauged regions. Satellite-retrieved and atmospheric reanalysis products exhibit considerable advantages in filling the spatial gaps in in-situ gauging networks and are [...] Read more.
Hydro-meteorological datasets are key components for understanding physical hydrological processes, but the scarcity of observational data hinders their potential application in poorly gauged regions. Satellite-retrieved and atmospheric reanalysis products exhibit considerable advantages in filling the spatial gaps in in-situ gauging networks and are thus forced to drive the physically lumped hydrological models for long-term streamflow simulation in data-sparse regions. As machine learning (ML)-based techniques can capture the relationship between different elements, they may have potential in further exploring meteorological predictors and hydrological responses. To examine the application prospects of a physically constrained ML algorithm using earth observation data, we used a short-series hydrological observation of the Hanjiang River basin in China as a case study. In this study, the prevalent modèle du Génie Rural à 9 paramètres Journalier (GR4J-9) hydrological model was used to initially simulate streamflow, and then, the simulated series and remote sensing data were used to train the long short-term memory (LSTM) method. The results demonstrated that the advanced GR4J9–LSTM model chain effectively improves the performance of the streamflow simulation by using more remote sensing data related to the hydrological response variables. Additionally, we derived a reservoir operation model by feeding the LSTM-based simulation outputs, which further revealed the potential application of our proposed technique. Full article
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22 pages, 3718 KiB  
Article
Bayesian Model Averaging: A Unique Model Enhancing Forecasting Accuracy for Daily Streamflow Based on Different Antecedent Time Series
by Sungwon Kim, Meysam Alizamir, Nam Won Kim and Ozgur Kisi
Sustainability 2020, 12(22), 9720; https://doi.org/10.3390/su12229720 - 21 Nov 2020
Cited by 14 | Viewed by 1951
Abstract
Streamflow forecasting is a vital task for hydrology and water resources engineering, and the different artificial intelligence (AI) approaches have been employed for this purposes until now. Additionally, the forecasting accuracy and uncertainty estimation are the meaningful assignments that need to be recognized. [...] Read more.
Streamflow forecasting is a vital task for hydrology and water resources engineering, and the different artificial intelligence (AI) approaches have been employed for this purposes until now. Additionally, the forecasting accuracy and uncertainty estimation are the meaningful assignments that need to be recognized. The addressed research investigates the potential of novel ensemble approach, Bayesian model averaging (BMA), in streamflow forecasting using daily time series data from two stations (i.e., Hongcheon and Jucheon), South Korea. Six categories (i.e., M1–M6) of input combination using different antecedent times were employed for streamflow forecasting. The outcomes of BMA model were compared with those of multivariate adaptive regression spline (MARS), M5 model tree (M5Tree), and Kernel extreme learning machines (KELM) models considering four assessment indexes, root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), correlation coefficient (R), and mean absolute error (MAE). The results revealed the superior accuracy of BMA model over three machine learning models in daily streamflow forecasting. Considering RMSE values among the best models during testing phase, the best BMA model (i.e., BMA2) enhanced the forecasting accuracy of MARS1, M5Tree4, and KELM3 models by 5.2%, 5.8%, and 3.4% in Hongcheon station. Additionally, the best BMA model (i.e., BMA1) improved the forecasting accuracy of MARS1, M5Tree1, and KELM1 models by 6.7%, 9.5%, and 3.7% in Jucheon station. In addition, the best BMA models in both stations allowed the uncertainty estimation, and produced higher uncertainty of peak flows compared to that of low flows. As one of the most robust and effective tools, therefore, the BMA model can be successfully employed for streamflow forecasting with different antecedent times. Full article
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21 pages, 4706 KiB  
Article
Estimation of Daily Stage–Discharge Relationship by Using Data-Driven Techniques of a Perennial River, India
by Manish Kumar, Anuradha Kumari, Daniel Prakash Kushwaha, Pravendra Kumar, Anurag Malik, Rawshan Ali and Alban Kuriqi
Sustainability 2020, 12(19), 7877; https://doi.org/10.3390/su12197877 - 23 Sep 2020
Cited by 29 | Viewed by 3059
Abstract
Modeling the stage-discharge relationship in river flow is crucial in controlling floods, planning sustainable development, managing water resources and economic development, and sustaining the ecosystem. In the present study, two data-driven techniques, namely wavelet-based artificial neural networks (WANN) and a support vector machine [...] Read more.
Modeling the stage-discharge relationship in river flow is crucial in controlling floods, planning sustainable development, managing water resources and economic development, and sustaining the ecosystem. In the present study, two data-driven techniques, namely wavelet-based artificial neural networks (WANN) and a support vector machine with linear and radial basis kernel functions (SVM-LF and SVM-RF), were employed for daily discharge (Q) estimation. The hydrological data of daily stage (H) and discharge (Q) from June to October for 10 years (2004–2013) at the Govindpur station, situated in the Burhabalang river basin, Orissa, were considered for analysis. For model construction, an optimum number of inputs (lags) was extracted using the partial autocorrelation function (PACF) at a 5% level of significance. The outcomes of the WANN, SVM-LF, and SVM-RF models were appraised over the observed value of Q based on performance indicators, viz., root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), Pearson’s correlation coefficient (PCC), and Willmott index (WI), and through visual inspection (time variation, scatter plot, and Taylor diagram). Results of the evaluation showed that the SVM-RF model (RMSE = 104.426 m3/s, NSE = 0.925, PCC = 0.964, WI = 0.979) outperformed the WANN and SVM-LF models with the combination of three inputs, i.e., current stage, one-day antecedent stage, and discharge, during the testing period. In addition, the SVM-RF model was found to be more reliable and robust than the other models and having important implications for water resources management at the study site. Full article
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42 pages, 11454 KiB  
Article
Modeling and Uncertainty Analysis of Groundwater Level Using Six Evolutionary Optimization Algorithms Hybridized with ANFIS, SVM, and ANN
by Akram Seifi, Mohammad Ehteram, Vijay P. Singh and Amir Mosavi
Sustainability 2020, 12(10), 4023; https://doi.org/10.3390/su12104023 - 14 May 2020
Cited by 93 | Viewed by 4097
Abstract
In the present study, six meta-heuristic schemes are hybridized with artificial neural network (ANN), adaptive neuro-fuzzy interface system (ANFIS), and support vector machine (SVM), to predict monthly groundwater level (GWL), evaluate uncertainty analysis of predictions and spatial variation analysis. The six schemes, including [...] Read more.
In the present study, six meta-heuristic schemes are hybridized with artificial neural network (ANN), adaptive neuro-fuzzy interface system (ANFIS), and support vector machine (SVM), to predict monthly groundwater level (GWL), evaluate uncertainty analysis of predictions and spatial variation analysis. The six schemes, including grasshopper optimization algorithm (GOA), cat swarm optimization (CSO), weed algorithm (WA), genetic algorithm (GA), krill algorithm (KA), and particle swarm optimization (PSO), were used to hybridize for improving the performance of ANN, SVM, and ANFIS models. Groundwater level (GWL) data of Ardebil plain (Iran) for a period of 144 months were selected to evaluate the hybrid models. The pre-processing technique of principal component analysis (PCA) was applied to reduce input combinations from monthly time series up to 12-month prediction intervals. The results showed that the ANFIS-GOA was superior to the other hybrid models for predicting GWL in the first piezometer (RMSE:1.21, MAE:0.878, NSE:0.93, PBIAS:0.15, R2:0.93), second piezometer (RMSE:1.22, MAE:0.881, NSE:0.92, PBIAS:0.17, R2:0.94), and third piezometer (RMSE:1.23, MAE:0.911, NSE:0.91, PBIAS:0.19, R2:0.94) in the testing stage. The performance of hybrid models with optimization algorithms was far better than that of classical ANN, ANFIS, and SVM models without hybridization. The percent of improvements in the ANFIS-GOA versus standalone ANFIS in piezometer 10 were 14.4%, 3%, 17.8%, and 181% for RMSE, MAE, NSE, and PBIAS in training stage and 40.7%, 55%, 25%, and 132% in testing stage, respectively. The improvements for piezometer 6 in train step were 15%, 4%, 13%, and 208% and in test step were 33%, 44.6%, 16.3%, and 173%, respectively, that clearly confirm the superiority of developed hybridization schemes in GWL modelling. Uncertainty analysis showed that ANFIS-GOA and SVM had, respectively, the best and worst performances among other models. In general, GOA enhanced the accuracy of the ANFIS, ANN, and SVM models. Full article
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Review

Jump to: Editorial, Research

18 pages, 1458 KiB  
Review
Prediction Interval Estimation Methods for Artificial Neural Network (ANN)-Based Modeling of the Hydro-Climatic Processes, a Review
by Vahid Nourani, Nardin Jabbarian Paknezhad and Hitoshi Tanaka
Sustainability 2021, 13(4), 1633; https://doi.org/10.3390/su13041633 - 03 Feb 2021
Cited by 13 | Viewed by 2608
Abstract
Despite the wide applications of artificial neural networks (ANNs) in modeling hydro-climatic processes, quantification of the ANNs’ performance is a significant matter. Sustainable management of water resources requires information about the amount of uncertainty involved in the modeling results, which is a guide [...] Read more.
Despite the wide applications of artificial neural networks (ANNs) in modeling hydro-climatic processes, quantification of the ANNs’ performance is a significant matter. Sustainable management of water resources requires information about the amount of uncertainty involved in the modeling results, which is a guide for proper decision making. Therefore, in recent years, uncertainty analysis of ANN modeling has attracted noticeable attention. Prediction intervals (PIs) are one of the prevalent tools for uncertainty quantification. This review paper has focused on the different techniques of PI development in the field of hydrology and climatology modeling. The implementation of each method was discussed, and their pros and cons were investigated. In addition, some suggestions are provided for future studies. This review paper was prepared via PRISMA (preferred reporting items for systematic reviews and meta-analyses) methodology. Full article
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26 pages, 3413 KiB  
Review
Review of Nitrogen Compounds Prediction in Water Bodies Using Artificial Neural Networks and Other Models
by Pavitra Kumar, Sai Hin Lai, Jee Khai Wong, Nuruol Syuhadaa Mohd, Md Rowshon Kamal, Haitham Abdulmohsin Afan, Ali Najah Ahmed, Mohsen Sherif, Ahmed Sefelnasr and Ahmed El-Shafie
Sustainability 2020, 12(11), 4359; https://doi.org/10.3390/su12114359 - 26 May 2020
Cited by 26 | Viewed by 3895
Abstract
The prediction of nitrogen not only assists in monitoring the nitrogen concentration in streams but also helps in optimizing the usage of fertilizers in agricultural fields. A precise prediction model guarantees the delivering of better-quality water for human use, as the operations of [...] Read more.
The prediction of nitrogen not only assists in monitoring the nitrogen concentration in streams but also helps in optimizing the usage of fertilizers in agricultural fields. A precise prediction model guarantees the delivering of better-quality water for human use, as the operations of various water treatment plants depend on the concentration of nitrogen in streams. Considering the stochastic nature and the various hydrological variables upon which nitrogen concentration depends, a predictive model should be efficient enough to account for all the complexities of nature in the prediction of nitrogen concentration. For two decades, artificial neural networks (ANNs) and other models (such as autoregressive integrated moving average (ARIMA) model, hybrid model, etc.), used for predicting different complex hydrological parameters, have proved efficient and accurate up to a certain extent. In this review paper, such prediction models, created for predicting nitrogen concentration, are critically analyzed, comparing their accuracy and input variables. Moreover, future research works aiming to predict nitrogen using advanced techniques and more reliable and appropriate input variables are also discussed. Full article
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