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Article ; Online: The impact of weather conditions on the quality of groundwater in the area of a municipal waste landfill

Dąbrowska Dominika / Rykała Wojciech / Nourani Vahid

Environmental & Socio-economic Studies, Vol 11, Iss 3, Pp 14-

2023 Volume 21

Abstract: The quality of groundwater in the source area of pollution depends on many factors, including the weather and hydrogeological conditions within the given area. Anassessment of water quality can be carried out based on data obtained from sensors placed in ...

Abstract	The quality of groundwater in the source area of pollution depends on many factors, including the weather and hydrogeological conditions within the given area. Anassessment of water quality can be carried out based on data obtained from sensors placed in boreholes. This research examined the influence of air and water temperature, groundwater table position and precipitation on the value of electrical conductivity in groundwater in a selected piezometer belonging to the monitoring network of the Quaternary aquifer in the area of a waste landfill site in Tychy-Urbanowice in southern Poland. The influence of individual factors was checked by using twenty neural network architectures of a Multilayer Perceptron Model (MLP). Each of these indicated factors were selected as input variables. Ultimately, three neural networks were selected, which were characterized by the smallest validation and test errors and showed the highest learning quality. The significance of individual variables for the effectiveness of the model was checked using a global sensitivity analysis. Three selected MLP models contained seven to nine neurons in the hidden layer and used a linear or exponential function as the hidden and output activation. The maximum test quality was 0.8369, while the smallest test error was 0.0011. The results of the sensitivity analysis highlighted the important role of water temperature and water table position on the conductivity value. The obtained goodness of fit results of the models to the input data allowed us to conclude that the MLP was applicable to such forecasts and can be extended by the analysis of further factors.
Keywords	hydrogeology ; landfill ; artificial neural networks ; mlp ; tychy ; poland ; Environmental sciences ; GE1-350
Subject code	550
Language	English
Publishing date	2023-09-01T00:00:00Z
Publisher	Sciendo
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Collective dynamics analysis based on the multiplex network method to unravel the backbone of fluctuations in groundwater level data

Naghipour, Leyla / Aalami, Mohammad Taghi / Nourani, Vahid

Computers and Geosciences. 2023 Mar., v. 172 p.105310-

2023

Abstract: Collective behavior of groundwater level (GWL) in the Tabriz plain, northwest Iran, was modeled in the framework of complex networks. The GWL is considerably influenced by the lack of recharge due to drought in recent years and the increasing water ... ...

Abstract	Collective behavior of groundwater level (GWL) in the Tabriz plain, northwest Iran, was modeled in the framework of complex networks. The GWL is considerably influenced by the lack of recharge due to drought in recent years and the increasing water demand of the industrial city of Tabriz. In this order, convergent cross-mapping (CCM) method was utilized to infer the causal relations by adopting a robust statistical approach based on hypothesis testing. Reconstructing structure is a challenge in network-based approaches to capture the dynamical variability of the measurements. This study aims to construct functional connectivity for a better understanding of informational flows among the wells. Multiplex network (MUX) was used as a set of coupled networks through interconnected layers to structure the information obtained from collective dynamics and time-reversal. The proposed procedure was evaluated by performing a systematic analysis of random network (RN) as a well-known model to consider the topological characteristics. The influence of connectivity reconstruction on network topology was also investigated by the generated experiments for different numbers of the dynamical units and lengths of the simulations at each unit. The MUX was constructed from the GWL for monthly observations over 15 years (2001–2017). Afterwards, the time-series was divided into three categories to infer the network connections in different time periods for various sets of the monitoring wells. The analysis indicates that unphysical connections are reduced by increasing the number of the coupling units as a desirable feature for detecting the underlying connectivity. The MUX is employed with the proposed statistical approach to quantitatively unravel the backbone of fluctuations in the GWL whose indirect causal relations form the skeleton. The MUX dynamics efficiently determines the underlying processes and identifies spatio-temporal patterns of the GWL fluctuations in two layers of the constructed networks.
Keywords	drought ; group behavior ; models ; skeleton ; statistical analysis ; time series analysis ; topology ; water table ; Iran ; Causality ; Collective dynamics ; Groundwater level ; Multiplex network ; Unphysical interactions ; Tabriz plain
Language	English
Dates of publication	2023-03
Publishing place	Elsevier Ltd
Document type	Article ; Online
ISSN	0098-3004
DOI	10.1016/j.cageo.2023.105310
Database	NAL-Catalogue (AGRICOLA)

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Article: Multi-station runoff-sediment modeling using seasonal LSTM models

Nourani, Vahid / Behfar, Nazanin

Journal of hydrology. 2021 Oct., v. 601

2021

Abstract: In this study two types of seasonal long-short-term memory (LSTM) artificial neural networks, named sequenced-LSTM (SLSTM) and wavelet-LSTM (WLSTM), were presented to model runoff-sediment process of three gauging stations, located in Missouri and Upper ... ...

Abstract	In this study two types of seasonal long-short-term memory (LSTM) artificial neural networks, named sequenced-LSTM (SLSTM) and wavelet-LSTM (WLSTM), were presented to model runoff-sediment process of three gauging stations, located in Missouri and Upper Mississippi regions in both daily and monthly scales. For this purpose, twenty-year observed streamflow and suspended sediment load (SSL) data were employed in both daily and monthly scales. The proposed seasonal models have full profits of classic LSTM model in time series processing and handle sole LSTM model’s weaknesses in failing to capture seasonal information of the process which usually exist in hydro-climate time series. The proposed models enhanced the long-short autoregressive dependency of runoff-sediment data by taking into consideration of very long seasonal dependency of data. The obtained outputs indicate the outperformance of proposed seasonal LSTM models to the classic LSTM and feed forward neural network models in test step up to about 25% and 28% in daily and monthly scales, respectively.
Keywords	neural networks ; sediment contamination ; stream flow ; suspended sediment ; time series analysis ; Mississippi ; Missouri
Language	English
Dates of publication	2021-10
Publishing place	Elsevier B.V.
Document type	Article
ZDB-ID	1473173-3
ISSN	1879-2707 ; 0022-1694
ISSN (online)	1879-2707
ISSN	0022-1694
DOI	10.1016/j.jhydrol.2021.126672
Database	NAL-Catalogue (AGRICOLA)

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Article: The Accuracy of Precipitation Forecasts at Timescales of 1–15 Days in the Volta River Basin

Gebremichael, Mekonnen / Yue, Haowen / Nourani, Vahid

Remote Sensing. 2022 Feb. 15, v. 14, no. 4

2022

Abstract: Medium-range (1–15 day) precipitation forecasts are increasingly available from global weather models. This study presents evaluation of the Global Forecast System (GFS) for the Volta river basin in West Africa. The evaluation was performed using two ... ...

Abstract	Medium-range (1–15 day) precipitation forecasts are increasingly available from global weather models. This study presents evaluation of the Global Forecast System (GFS) for the Volta river basin in West Africa. The evaluation was performed using two satellite-gauge merged products: NASA’s Integrated Multi-satellitE Retrievals (IMERG) “Final Run” satellite-gauge merged rainfall observations, and the University of California Santa Barbara’s Climate Hazard’s group Infrared Precipitation with Stations (CHIRPS). The performance of GFS depends on the climate zone, with underestimation bias in the dry Sahel climate, overestimation bias in the wet Guinea Coastal climate, and relatively no bias in the moderately wet Savannah climate. Averaging rainfall over the watershed of the Akosombo dam (i.e., averaging across all three climate zones), the GFS forecast indicates low skill (Kling-Gupta Efficiency KGE = 0.42 to 0.48) for the daily, 1-day, lead GFS forecast, which deteriorates further as the lead time increases. A sharp decrease in KGE occurred between 6 to 10 days. Aggregating the forecasts over long timescales improves the accuracy of the GFS forecasts. On a 15-day accumulation timescale, GFS shows higher skills (KGE = 0.74 to 0.88).
Keywords	Sahel ; lead ; rain ; watersheds ; California ; Guinea
Language	English
Dates of publication	2022-0215
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
ZDB-ID	2513863-7
ISSN	2072-4292
ISSN	2072-4292
DOI	10.3390/rs14040937
Database	NAL-Catalogue (AGRICOLA)

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Article: Uncertainty assessment of LSTM based groundwater level predictions

Nourani, Vahid / Khodkar, Kasra / Gebremichael, Mekonnen

Hydrological sciences journal. 2022 Apr. 04, v. 67, no. 5

2022

Abstract: Due to the underlying uncertainty in groundwater level (GWL) modelling, point prediction of GWLs does not provide sufficient information. Moreover, the insufficiency of data on subjects such as illegal exploitation wells and wastewater pounds, which are ... ...

Abstract	Due to the underlying uncertainty in groundwater level (GWL) modelling, point prediction of GWLs does not provide sufficient information. Moreover, the insufficiency of data on subjects such as illegal exploitation wells and wastewater pounds, which are untraceable, underlines the importance of evolved uncertainty in the groundwaters of the Ardabil plain. Thus, estimating prediction intervals (PIs) for groundwater modelling can be an important step. In this paper, PIs were estimated for GWLs of selected piezometers of the Ardebil plain in Iran using the artificial neural network (ANN)-based lower upper bound estimation (LUBE) method. The classic feedforward neural network (FFNN) and deep-learning-based long short-term memory (LSTM) were used. GWL data of piezometers and hydrological data (1992–2018) were applied for modelling. The results indicate that LSTM outperforms FFNN in both PI and point prediction tasks. LSTM-based LUBE was found to be superior to FFNN-based LUBE, providing an average 25% lower coverage width criterion (CWC). PIs estimated for piezometers with high transmissivity resulted in 50% lower CWC than PIs estimated for piezometers in areas with lower transmissivity.
Keywords	groundwater ; hydrologic data ; neural networks ; piezometers ; prediction ; uncertainty ; wastewater ; water table ; Iran
Language	English
Dates of publication	2022-0404
Size	p. 773-790.
Publishing place	Taylor & Francis
Document type	Article
ISSN	2150-3435
DOI	10.1080/02626667.2022.2046755
Database	NAL-Catalogue (AGRICOLA)

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Article: Application of PPIE method to assess the uncertainty and accuracy of multi-climate model-based temperature and precipitation downscaling

Nourani, Vahid / Paknezhad, Nardin Jabbarian / Huang‬‬‬‬, Jinhui Jeanne

Theoretical and applied climatology. 2022 Feb., v. 147, no. 3-4

2022

Abstract: This study attempted to investigate the accuracy and uncertainty involved in downscaled climatic parameters obtained via the artificial neural network (ANN). Two climate models from the Coupled Model Intercomparison Project 5 (CMIP5) and CMIP6 were used ... ...

Abstract	This study attempted to investigate the accuracy and uncertainty involved in downscaled climatic parameters obtained via the artificial neural network (ANN). Two climate models from the Coupled Model Intercomparison Project 5 (CMIP5) and CMIP6 were used to downscale temperature and precipitation parameters over Tabriz and Rasht cities with distinct climatic regimes (respectively dry and wet). In order to assess the accuracy and uncertainty of downscaling climate models, point prediction and prediction intervals (PIs) of models were calculated simultaneously via the proposed point and PI estimation (PPIE) method. Monthly data of 1951 to 2014 and 1977 to 2014 respectively from Tabriz and Rasht stations were considered for precipitation and temperature downscaling. The dominant predictors of climate models were determined by K-means clustering method and mutual information (MI) measure. Comparison of the obtained results indicated that the performance of CMIP6 is superior compared to the CMIP5 for downscaling both precipitation and temperature parameters in both stations. According to the obtained accuracy and uncertainty combination assessment measure, CMIP6 performed better up to 95% and 76% compared to those for CMIP5, respectively, for Tabriz and Rasht stations. Criteria of assessing accuracy and uncertainty combination for downscaled temperature compared to those for the precipitation downscaling in Tabriz and Rasht stations were respectively 78% and 97% more accurate and reliable for downscaling temperature compared to those for the precipitation. Thereafter, Shared Socioeconomic Pathway5 85 (SSP5 85) and Representative Concentration Pathway 85 (RCP85) scenarios were respectively applied for CMIP6 and CMIP5 models to project the precipitation and temperature for near future (2050) and far future (2100). Comparison of the values of temperature under SSP585 showed that temperature may get up to 3.6 [Formula: see text] and 4.87 [Formula: see text] increase in Tabriz and 4.5 [Formula: see text] and 4.2 [Formula: see text] increase in Rasht station respectively in years 2050 and 2100 compared to 2005. Under SSP5 85 scenario, precipitation may get up to 30-mm and 38-mm drop in Tabriz and may experience up to 30-mm and 50-mm increase in Rasht station, respectively, in years 2050 and 2100 compared to 2005.
Keywords	climate ; climatology ; neural networks ; prediction ; temperature ; uncertainty
Language	English
Dates of publication	2022-02
Size	p. 1327-1343.
Publishing place	Springer Vienna
Document type	Article
ZDB-ID	1463177-5
ISSN	1434-4483 ; 0177-798X
ISSN (online)	1434-4483
ISSN	0177-798X
DOI	10.1007/s00704-021-03884-7
Database	NAL-Catalogue (AGRICOLA)

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Article: Coupling wavelet transform with multivariate adaptive regression spline for simulating suspended sediment load: Independent testing approach

Shiri, Naser / Shiri, Jalal / Nourani, Vahid / Karimi, Sepideh

ISH journal of hydraulic engineering. 2022 Nov. 01, v. 28, no. S1

2022

Abstract: Accurate prediction of suspended sediment load (SSL) of a river is very important as it directly affects the performance of the corresponding hydraulic structures. SSL can give valuable information on the catchment erodibility and deposition of the ... ...

Abstract	Accurate prediction of suspended sediment load (SSL) of a river is very important as it directly affects the performance of the corresponding hydraulic structures. SSL can give valuable information on the catchment erodibility and deposition of the sediment being produced through scouring phenomenon. Despite the hydraulic approaches for studying the scouring/sedimentation processes in streams, hydrologic approaches may provide valuable information about SSL deposition magnitudes as well as its temporal distribution in relation with the streamflow power. A hydrologic-based approach through coupling the wavelet-based processed signals and multi adaptive regression spline (WMARS) methodology is suggested in the present paper for the first time to predict SSL values of rivers using the simultaneous streamflow and SSL records. The developed models were assessed through the most powerful k-fold testing data scanning procedure. The obtained results showed the superiority of the proposed hybrid WMARS models over the single MARS and traditional sediment rating curve techniques. Due to the presence of hysteresis effects during the study periods, adaptation of k-fold testing assessment approach is very necessary to get better insight about the models performances.
Keywords	erodibility ; hysteresis ; prediction ; rivers ; sediment contamination ; sediments ; stream flow ; suspended sediment ; watersheds ; wavelet
Language	English
Dates of publication	2022-1101
Size	p. 356-365.
Publishing place	Taylor & Francis
Document type	Article
ISSN	2164-3040
DOI	10.1080/09715010.2020.1801528
Database	NAL-Catalogue (AGRICOLA)

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Article: Multi-step-ahead solar irradiance modeling employing multi-frequency deep learning models and climatic data

Nourani, Vahid / Sharghi, Elnaz / Behfar, Nazanin / Zhang, Yongqiang

Applied energy. 2022 June 01, v. 315

2022

Abstract: In this paper two enhanced long-short-term memory (LSTM) models of sequenced-LSTM (SLSTM) and wavelet-LSTM (WLSTM), provided for multi-step-ahead simulation of solar irradiance of six stations, located in Iran and USA. In this respect, twenty-year ... ...

Abstract	In this paper two enhanced long-short-term memory (LSTM) models of sequenced-LSTM (SLSTM) and wavelet-LSTM (WLSTM), provided for multi-step-ahead simulation of solar irradiance of six stations, located in Iran and USA. In this respect, twenty-year recorded solar irradiance and climate data were employed. The proposed multi-frequency models serve all the capabilities of classic LSTM network and also handle its weakness in detecting and modeling multi-frequency information that often included in natural datasets. The suggested methodology improved the long-short auto-regressive term of climate-solar irradiance data by including very long frequencies of time series. The results revealed that the suggested multi-frequency LSTM methods could exceed the feed forward neural network and classic LSTM network in test phase up to 23% and 13%, respectively.
Keywords	data collection ; energy ; light intensity ; meteorological data ; neural networks ; solar radiation ; time series analysis ; Iran
Language	English
Dates of publication	2022-0601
Publishing place	Elsevier Ltd
Document type	Article
ZDB-ID	2000772-3
ISSN	0306-2619
ISSN	0306-2619
DOI	10.1016/j.apenergy.2022.119069
Database	NAL-Catalogue (AGRICOLA)

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Article: Spatiotemporal Analysis of Droughts Over Different Climate Regions Using Hybrid Clustering Method [Erratum: January 2022, v.36(2); p.489]

Roushangar, Kiyoumars / Ghasempour, Roghayeh / Nourani, Vahid

Water resources management. 2022 Jan., v. 36, no. 2

2022

Abstract: Assessment of spatiotemporal variations of drought is an efficient method for implementing drought mitigation strategies and reducing its negative impacts. This study aimed to assess the spatiotemporal pattern of short- to long-term droughts for an area ... ...

Abstract	Assessment of spatiotemporal variations of drought is an efficient method for implementing drought mitigation strategies and reducing its negative impacts. This study aimed to assess the spatiotemporal pattern of short- to long-term droughts for an area with different climates. Therefore, 31 stations located in Iran were selected and the Standardized Precipitation Index (SPI) series with timescales of 3, 6, and 12 months were computed during the 1951-2016 period. A hybrid methodology including Maximal Overlap Discrete Wavelet Transform (MODWT) and K-means methods was used for obtaining the SPIs time-frequency properties and multiscale zoning of the area. The energy amounts of the decomposed subseries via the MODWT were applied as inputs for K-means. Also, the statistics in drought features (i.e. drought duration, severity, and peak) were assessed and the results showed that shorter term droughts (i.e. SPI-3 and -6) were more frequent and severe in the northern parts where the lowest values were obtained for drought duration. It was observed that the regions with more droughts frequency had the highest energy values. For shorter term droughts a direct relationship was obtained between the energy values and the mean SPIs, drought severity, and drought peak, whereas an inverse relationship was obtained for longer term drought. It was found that increasing the degree of SPI led to more similarity between the stations of each cluster. Also, the homogeneity of stations for the SPI-12 was slightly higher than the SPI-3 and -6.
Keywords	administrative management ; atmospheric precipitation ; drought ; energy ; statistics ; water ; wavelet ; Iran
Language	English
Dates of publication	2022-01
Size	p. 473-488.
Publishing place	Springer Netherlands
Document type	Article
ZDB-ID	59924-4
ISSN	1573-1650 ; 0920-4741
ISSN (online)	1573-1650
ISSN	0920-4741
DOI	10.1007/s11269-021-02974-5
Database	NAL-Catalogue (AGRICOLA)

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Article: Improving the performance of random forest for estimating monthly reservoir inflow via complete ensemble empirical mode decomposition and wavelet analysis

Ahmadi, Farshad / Mehdizadeh, Saeid / Nourani, Vahid

Stochastic environmental research and risk assessment. 2022 Sept., v. 36, no. 9

2022

Abstract: Estimation of reservoir inflow is of particular importance in optimal planning and management of water resources, proper allocation of water to consumption sectors, hydrological studies, etc. This study aimed to estimate monthly inflow (Q) to the Maroon ... ...

Abstract	Estimation of reservoir inflow is of particular importance in optimal planning and management of water resources, proper allocation of water to consumption sectors, hydrological studies, etc. This study aimed to estimate monthly inflow (Q) to the Maroon Dam reservoir located in Iran utilizing climatic data such as minimum, maximum, and mean air temperatures (Tₘᵢₙ, Tₘₐₓ, T), reservoir evaporation (E), and rainfall (R). The impact of any of the mentioned variables was analyzed by the entropy-based pre-processing technique. The results of the pre-processing showed that the rainfall is the most important parameter affecting the reservoir inflow. Therefore, three types of input patterns were taken into consideration consisting the antecedent Q-based, antecedent R-based, and combined antecedent Q and R-based input combinations. To estimate the monthly reservoir inflow, a random forest (RF) was firstly employed as the standalone model. Then, two different types of hybrid models were proposed via coupling the RF on complete ensemble empirical mode decomposition (CEEMD) and wavelet analysis (W) in order to implement the coupled CEEMD-RF and W-RF models. It is worthwhile to mentioning that six mother wavelets were used in developing the hybrid W-RF models. Four error metrics including root mean square error (RMSE), mean absolute error (MAE), Kling-Gupta efficiency (KGE), and Willmott index (WI) were used to assess the accuracy of implemented models. The attained results indicated the superiority of proposed hybrid models over the classic RF for estimating the monthly reservoir inflow. The most precise model during the test phase was W-RF(3) utilizing the Sym(2) as the mother wavelet under a lagged Q-based pattern with error measures of RMSE = 15.011 m³/s, MAE = 10.439 m³/s, KGE = 0.832, WI = 0.773.
Keywords	air ; evaporation ; hybrids ; hydrology ; models ; rain ; research ; risk assessment ; wavelet ; Iran
Language	English
Dates of publication	2022-09
Size	p. 2753-2768.
Publishing place	Springer Berlin Heidelberg
Document type	Article
ZDB-ID	1481263-0
ISSN	1436-3259 ; 1435-151X ; 1436-3240 ; 0931-1955
ISSN (online)	1436-3259 ; 1435-151X
ISSN	1436-3240 ; 0931-1955
DOI	10.1007/s00477-021-02159-x
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