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Interested in interdisciplinary research, scientific writing, science communication, teaching, and offering consultancy for the industry. My core research interests and expertise include renewable energy—focusing on hydropower and complementarity resources, hydropower impacts, river restoration and management, e-flows, floods, droughts, climate change, fluvial hydraulics, sediment transport in open-channel flows; embankment structures; hydraulic structures; Sustainable Drainage Systems (SuDS), long-term meteorological and hydrologic trends and variability analysis, ecohydraulics, ecohydrology, and artificial intelligence applications in the field hydraulics and hydrology.
Many Total Solar Irradiance (TSI) and Solar Constant (SC) values are given and used in the literature, sometimes leading to confusion. The TSI value is relevant and has great importance in engineering and scientific research in energy. In this study, a review has been done to study the TSI and SC concepts. A reevaluation of the TSI and SC is undertaken to consider state of the art. The effect of three different TSI values concerning different locations is studied for estimating global radiation with the Ångström-Prescott (A-P) formulation over Spain. New seasonal A-P coefficient sets are developed for Spain. The good performance for all the 29 stations and seasons and the slight differences observed for the different TSI values employed ensure global solar radiation (GSR) accuracy with the A-P model. The results highlight the local and seasonal A-P coefficient calibration relevance instead of using a single general pair of calibration coefficients. Based on the TSI study, we recommended calculations of solar radiation models to adopt the TSI value equal to 1361 W m−2. The revised TSI is lower than the previous values proposed. Therefore, it is highly recommended to review the value and concept of TSI.
Javier Almorox; Cyril Voyant; Nadjem Bailek; Alban Kuriqi; J.A. Arnaldo. Total solar irradiance's effect on the performance of empirical models for estimating global solar radiation: An empirical-based review. Energy 2021, 236, 121486 .
AMA StyleJavier Almorox, Cyril Voyant, Nadjem Bailek, Alban Kuriqi, J.A. Arnaldo. Total solar irradiance's effect on the performance of empirical models for estimating global solar radiation: An empirical-based review. Energy. 2021; 236 ():121486.
Chicago/Turabian StyleJavier Almorox; Cyril Voyant; Nadjem Bailek; Alban Kuriqi; J.A. Arnaldo. 2021. "Total solar irradiance's effect on the performance of empirical models for estimating global solar radiation: An empirical-based review." Energy 236, no. : 121486.
We bring a practical and comprehensive GIS-based framework to utilize freely available remotely sensed datasets to assess wildfire ignition probability and spreading capacities of vegetated landscapes. The study area consists of the country-level scale of the Romanian territory, characterized by a diversity of vegetated landscapes threatened by climate change. We utilize the Wildfire Ignition Probability/Wildfire Spreading Capacity Index (WIPI/WSCI). WIPI/WSCI models rely on a multi-criteria data mining procedure assessing the study area’s social, environmental, geophysical, and fuel properties based on open access remotely sensed data. We utilized the Receiver Operating Characteristic (ROC) analysis to weigh each indexing criterion’s impact factor and assess the model’s overall sensitivity. Introducing ROC analysis at an earlier stage of the workflow elevated the final Area Under the Curve (AUC) of WIPI from 0.705 to 0.778 and WSCI from 0.586 to 0.802. The modeling results enable discussion on the vulnerability of protected areas and the exposure of man-made structures to wildfire risk. Our study shows that within the wildland–urban interface of Bucharest’s metropolitan area, there is a remarkable building stock of healthcare, residential and educational functions, which are significantly exposed and vulnerable to wildfire spreading risk.
Artan Hysa; Velibor Spalevic; Branislav Dudic; Sanda Roșca; Alban Kuriqi; Ștefan Bilașco; Paul Sestras. Utilizing the Available Open-Source Remotely Sensed Data in Assessing the Wildfire Ignition and Spread Capacities of Vegetated Surfaces in Romania. Remote Sensing 2021, 13, 2737 .
AMA StyleArtan Hysa, Velibor Spalevic, Branislav Dudic, Sanda Roșca, Alban Kuriqi, Ștefan Bilașco, Paul Sestras. Utilizing the Available Open-Source Remotely Sensed Data in Assessing the Wildfire Ignition and Spread Capacities of Vegetated Surfaces in Romania. Remote Sensing. 2021; 13 (14):2737.
Chicago/Turabian StyleArtan Hysa; Velibor Spalevic; Branislav Dudic; Sanda Roșca; Alban Kuriqi; Ștefan Bilașco; Paul Sestras. 2021. "Utilizing the Available Open-Source Remotely Sensed Data in Assessing the Wildfire Ignition and Spread Capacities of Vegetated Surfaces in Romania." Remote Sensing 13, no. 14: 2737.
Accurate prediction of daily runoff’s dynamic nature is necessary for better watershed planning and management. This study analyzes the applicability of artificial neural network (ANN), wavelet-coupled artificial neural network (WANN), adaptive neuro-fuzzy inference system (ANFIS), and wavelet-coupled adaptive neuro-fuzzy inference system (WANFIS) models for daily runoff prediction of Koyna River basin, India. Gamma test (GT) was used to select the best input vector to avoid the time-consuming and tedious trial and error input selection methods. Original daily rainfall and runoff time series data were decomposed into different multifrequency sub-signals using three types (Haar, Daubechies, and Coiflet) of mother wavelets. The decomposed sub-signals were fed to ANN and ANFIS as inputs for developing hybrid WANN and WANFIS models, respectively. The quantitative and qualitative performance evaluation criteria were used for assessing the prediction accuracy of developed models. An uncertainty analysis was employed to study the reliability of the developed models. It was observed that hybrid data-driven models (WANN/WANFIS) outperformed simple data-driven models (ANN/ANFIS). Finally, it was found that the Coiflet wavelet-coupled ANFIS model can be successfully applied for daily runoff prediction of the highly dynamic and complex Koyna River basin. The sensitivity analysis was also carried out to detect the most crucial variable for daily runoff prediction. The sensitivity analysis indicated that the previous 1-day runoff (Qt–1) is the most crucial variable for daily runoff prediction.
Tarate Suryakant Bajirao; Pravendra Kumar; Manish Kumar; Ahmed Elbeltagi; Alban Kuriqi. Potential of hybrid wavelet-coupled data-driven-based algorithms for daily runoff prediction in complex river basins. Theoretical and Applied Climatology 2021, 145, 1207 -1231.
AMA StyleTarate Suryakant Bajirao, Pravendra Kumar, Manish Kumar, Ahmed Elbeltagi, Alban Kuriqi. Potential of hybrid wavelet-coupled data-driven-based algorithms for daily runoff prediction in complex river basins. Theoretical and Applied Climatology. 2021; 145 (3-4):1207-1231.
Chicago/Turabian StyleTarate Suryakant Bajirao; Pravendra Kumar; Manish Kumar; Ahmed Elbeltagi; Alban Kuriqi. 2021. "Potential of hybrid wavelet-coupled data-driven-based algorithms for daily runoff prediction in complex river basins." Theoretical and Applied Climatology 145, no. 3-4: 1207-1231.
According to various sources, Southern Morocco has stood out as an outstanding tourist destination in recent decades, with global appeal. Dakhla City, including Dakhla Bay, classified by the Convention on Wetlands in 2005 as a Wetland of International Importance, offers visitors various entertainment opportunities at many city sites. Therefore, human activity and social benefits should be considered in conjunction with the need to safeguard the ecosystems and maintain the Ecosystem Services (ES). This study aims to provide an overview of the tourism dynamics and hotspots related to cultural ecosystem services in Dakhla Bay. The landscape attributes are used along with an InVEST model to detect the distribution of preferences for the Cultural Ecosystem Services (CESs), map the hotspots, and identify the spatial correlations between features such as the landscape and visiting rate to understand which elements of nature attract people to the locations around the study area. Geotagged photos posted to the Flickr™ website between 2005 and 2017 were used to approximate the number of tourist visits. The results showed that tourism suffered several dips in 2005–2017 and that tourist visits are currently rising. Additionally, an estimated annual tourist visit rate shows that tourism in Dakhla Bay has been growing steadily by 2%.
Ikram Mouttaki; Youssef Khomalli; Mohamed Maanan; Ingrida Bagdanavičiūtė; Hassan Rhinane; Alban Kuriqi; Quoc Pham; Mehdi Maanan. A New Approach to Mapping Cultural Ecosystem Services. Environments 2021, 8, 56 .
AMA StyleIkram Mouttaki, Youssef Khomalli, Mohamed Maanan, Ingrida Bagdanavičiūtė, Hassan Rhinane, Alban Kuriqi, Quoc Pham, Mehdi Maanan. A New Approach to Mapping Cultural Ecosystem Services. Environments. 2021; 8 (6):56.
Chicago/Turabian StyleIkram Mouttaki; Youssef Khomalli; Mohamed Maanan; Ingrida Bagdanavičiūtė; Hassan Rhinane; Alban Kuriqi; Quoc Pham; Mehdi Maanan. 2021. "A New Approach to Mapping Cultural Ecosystem Services." Environments 8, no. 6: 56.
In the present study, estimating pan evaporation (Epan) was evaluated based on different input parameters: maximum and minimum temperatures, relative humidity, wind speed, and bright sunshine hours. The techniques used for estimating Epan were the artificial neural network (ANN), wavelet-based ANN (WANN), radial function-based support vector machine (SVM-RF), linear function-based SVM (SVM-LF), and multi-linear regression (MLR) models. The proposed models were trained and tested in three different scenarios (Scenario 1, Scenario 2, and Scenario 3) utilizing different percentages of data points. Scenario 1 includes 60%: 40%, Scenario 2 includes 70%: 30%, and Scenario 3 includes 80%: 20% accounting for the training and testing dataset, respectively. The various statistical tools such as Pearson’s correlation coefficient (PCC), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), and Willmott Index (WI) were used to evaluate the performance of the models. The graphical representation, such as a line diagram, scatter plot, and the Taylor diagram, were also used to evaluate the proposed model’s performance. The model results showed that the SVM-RF model’s performance is superior to other proposed models in all three scenarios. The most accurate values of PCC, RMSE, NSE, and WI were found to be 0.607, 1.349, 0.183, and 0.749, respectively, for the SVM-RF model during Scenario 1 (60%: 40% training: testing) among all scenarios. This showed that with an increase in the sample set for training, the testing data would show a less accurate modeled result. Thus, the evolved models produce comparatively better outcomes and foster decision-making for water managers and planners.
Manish Kumar; Anuradha Kumari; Deepak Kumar; Nadhir Al-Ansari; Rawshan Ali; Raushan Kumar; Ambrish Kumar; Ahmed Elbeltagi; Alban Kuriqi. The Superiority of Data-Driven Techniques for Estimation of Daily Pan Evaporation. Atmosphere 2021, 12, 701 .
AMA StyleManish Kumar, Anuradha Kumari, Deepak Kumar, Nadhir Al-Ansari, Rawshan Ali, Raushan Kumar, Ambrish Kumar, Ahmed Elbeltagi, Alban Kuriqi. The Superiority of Data-Driven Techniques for Estimation of Daily Pan Evaporation. Atmosphere. 2021; 12 (6):701.
Chicago/Turabian StyleManish Kumar; Anuradha Kumari; Deepak Kumar; Nadhir Al-Ansari; Rawshan Ali; Raushan Kumar; Ambrish Kumar; Ahmed Elbeltagi; Alban Kuriqi. 2021. "The Superiority of Data-Driven Techniques for Estimation of Daily Pan Evaporation." Atmosphere 12, no. 6: 701.
Rivers play an essential role to humans and ecosystems, but they also burst their banks during floods, often causing extensive damage to crop, property, and loss of lives. This paper characterizes the 2014 flood of the Indus River in Pakistan using the US Army Corps of Engineers Hydrologic Engineering Centre River Analysis System (HEC-RAS) model, integrated into a geographic information system (GIS) and satellite images from Landsat-8. The model is used to estimate the spatial extent of the flood and assess the damage that it caused by examining changes to the different land-use/land-cover (LULC) types of the river basin. Extreme flows for different return periods were estimated using a flood frequency analysis using a log-Pearson III distribution, which the Kolmogorov–Smirnov (KS) test identified as the best distribution to characterize the flow regime of the Indus River at Taunsa Barrage. The output of the flood frequency analysis was then incorporated into the HEC-RAS model to determine the spatial extent of the 2014 flood, with the accuracy of this modelling approach assessed using images from the Moderate Resolution Imaging Spectroradiometer (MODIS). The results show that a supervised classification of the Landsat images was able to identify the LULC types of the study region with a high degree of accuracy, and that the most affected LULC was crop/agricultural land, of which 50% was affected by the 2014 flood. Finally, the hydraulic simulation of extent of the 2014 flood was found to visually compare very well with the MODIS image, and the surface area of floods of different return periods was calculated. This paper provides further evidence of the benefit of using a hydrological model and satellite images for flood mapping and for flood damage assessment to inform the development of risk mitigation strategies.
Aqil Tariq; Hong Shu; Alban Kuriqi; Saima Siddiqui; Alexandre Gagnon; Linlin Lu; Nguyen Linh; Quoc Pham. Characterization of the 2014 Indus River Flood Using Hydraulic Simulations and Satellite Images. Remote Sensing 2021, 13, 2053 .
AMA StyleAqil Tariq, Hong Shu, Alban Kuriqi, Saima Siddiqui, Alexandre Gagnon, Linlin Lu, Nguyen Linh, Quoc Pham. Characterization of the 2014 Indus River Flood Using Hydraulic Simulations and Satellite Images. Remote Sensing. 2021; 13 (11):2053.
Chicago/Turabian StyleAqil Tariq; Hong Shu; Alban Kuriqi; Saima Siddiqui; Alexandre Gagnon; Linlin Lu; Nguyen Linh; Quoc Pham. 2021. "Characterization of the 2014 Indus River Flood Using Hydraulic Simulations and Satellite Images." Remote Sensing 13, no. 11: 2053.
In irrigation and drainage channels, vertical drops are generally used to transfer water from a higher elevation to a lower level. Downstream of these structures, measures are taken to prevent the destruction of the channel bed by the flow and reduce its destructive kinetic energy. In this study, the effect of use steps and grid dissipators on hydraulic characteristics regarding flow pattern, relative downstream depth, relative pool depth, and energy dissipation of a vertical drop was investigated by numerical simulation following the symmetry law. Two relative step heights and two grid dissipator cell sizes were used. The hydraulic model describes fully coupled three-dimensional flow with axial symmetry. For the simulation, critical depths ranging from 0.24 to 0.5 were considered. Values of low relative depth obtained from the numerical results are in satisfactory agreement with the laboratory data. The simultaneous use of step and grid dissipators increases the relative energy dissipation compared to a simple vertical drop and a vertical drop equipped with steps. By using the grid dissipators and the steps downstream of the vertical drop, the relative pool depth increases. Changing the pore size of the grid dissipators does not affect the relative depth of the pool. The simultaneous use of steps and grid dissipators reduces the downstream Froude number of the vertical drop from 3.83–5.20 to 1.46–2.00.
Rasoul Daneshfaraz; Ehsan Aminvash; Amir Ghaderi; Alban Kuriqi; John Abraham. Three-Dimensional Investigation of Hydraulic Properties of Vertical Drop in the Presence of Step and Grid Dissipators. Symmetry 2021, 13, 895 .
AMA StyleRasoul Daneshfaraz, Ehsan Aminvash, Amir Ghaderi, Alban Kuriqi, John Abraham. Three-Dimensional Investigation of Hydraulic Properties of Vertical Drop in the Presence of Step and Grid Dissipators. Symmetry. 2021; 13 (5):895.
Chicago/Turabian StyleRasoul Daneshfaraz; Ehsan Aminvash; Amir Ghaderi; Alban Kuriqi; John Abraham. 2021. "Three-Dimensional Investigation of Hydraulic Properties of Vertical Drop in the Presence of Step and Grid Dissipators." Symmetry 13, no. 5: 895.
The Egyptian irrigation system depends mainly on canals that take water from the River Nile; nevertheless, the arid climate that dominates most of the country influences the high rate of water losses, mainly through evaporation. Thus, the main objective of this study is to develop a practical approach that helps to accommodate solar photovoltaic (PV) panels over irrigation canals to reduce the water evaporation rate. Meanwhile, a solar PV panel can contribute effectively and economically to an on-grid system by generating a considerable amount of electricity. A hybrid system includes a solar PV panel and a diesel generator. Several factors such as the levelized cost of energy (LCOE), total net present cost, loss of power supply probability, and greenhouse gas emissions should be considered while developing a technoeconomically feasible grid-connected renewable integrated system. A mathematical formulation for the water loss was introduced and the evaporation loss was monthly estimated. Thus, this study also aims to enhance an innovative metaheuristic algorithm based on a cuckoo search optimizer to show the way forward for developing a technoeconomic study of an irrigation system integrated with an on-grid solar PV panel designed for a 20-year lifespan. The results are compared using the mature genetic algorithm and particle swarm optimization to delimit the optimal size and configuration of the on-grid system. The optimal technoeconomic feasibility is connected to the graphical information system to delimit the optimal length and direction of the solar PV accommodation covering the canals. Finally, based on the simulated results, the optimal sizing and configuration of the irrigation-system-integrated on-grid solar PV accommodation have less impact on the LCOE without violating any constraint and, at the same time, generating clean energy.
Ayman Alhejji; Alban Kuriqi; Jakub Jurasz; Farag Abo-Elyousr. Energy Harvesting and Water Saving in Arid Regions via Solar PV Accommodation in Irrigation Canals. Energies 2021, 14, 2620 .
AMA StyleAyman Alhejji, Alban Kuriqi, Jakub Jurasz, Farag Abo-Elyousr. Energy Harvesting and Water Saving in Arid Regions via Solar PV Accommodation in Irrigation Canals. Energies. 2021; 14 (9):2620.
Chicago/Turabian StyleAyman Alhejji; Alban Kuriqi; Jakub Jurasz; Farag Abo-Elyousr. 2021. "Energy Harvesting and Water Saving in Arid Regions via Solar PV Accommodation in Irrigation Canals." Energies 14, no. 9: 2620.
Accurate monthly runoff estimation is crucial in water resources management, planning, and development, preventing and reducing water-related problems, such as flooding and droughts. This article evaluates the monthly hydrological rainfall-runoff model’s performance, the GR2M model, in Thailand’s southern basins. The GR2M model requires only two parameters: production store (X1) and groundwater exchange rate (X2). Moreover, no prior research has been reported on its application in this region. The 37 runoff stations, which are located in three sub-watersheds of Thailand’s southern region, namely; Thale Sap Songkhla, Peninsular-East Coast, and Peninsular-West Coast, were selected as study cases. The available monthly hydrological data of runoff, rainfall, air temperature from the Royal Irrigation Department (RID) and the Thai Meteorological Department (TMD) were collected and analyzed. The Thornthwaite method was utilized for the determination of evapotranspiration. The model’s performance was conducted using three statistical indices: Nash–Sutcliffe Efficiency (NSE), Correlation Coefficient (r), and Overall Index (OI). The model’s calibration results for 37 runoff stations gave the average NSE, r, and OI of 0.657, 0.825, and 0.757, respectively. Moreover, the NSE, r, and OI values for the model’s verification were 0.472, 0.750, and 0.639, respectively. Hence, the GR2M model was qualified and reliable to apply for determining monthly runoff variation in this region. The spatial distribution of production store (X1) and groundwater exchange rate (X2) values was conducted using the IDW method. It was susceptible to the X1, and X2 values of approximately more than 0.90, gave the higher model’s performance.
Pakorn Ditthakit; Sirimon Pinthong; Nureehan Salaeh; Fadilah Binnui; Laksanara Khwanchum; Alban Kuriqi; Khaled Khedher; Quoc Pham. Performance Evaluation of a Two-Parameters Monthly Rainfall-Runoff Model in the Southern Basin of Thailand. Water 2021, 13, 1226 .
AMA StylePakorn Ditthakit, Sirimon Pinthong, Nureehan Salaeh, Fadilah Binnui, Laksanara Khwanchum, Alban Kuriqi, Khaled Khedher, Quoc Pham. Performance Evaluation of a Two-Parameters Monthly Rainfall-Runoff Model in the Southern Basin of Thailand. Water. 2021; 13 (9):1226.
Chicago/Turabian StylePakorn Ditthakit; Sirimon Pinthong; Nureehan Salaeh; Fadilah Binnui; Laksanara Khwanchum; Alban Kuriqi; Khaled Khedher; Quoc Pham. 2021. "Performance Evaluation of a Two-Parameters Monthly Rainfall-Runoff Model in the Southern Basin of Thailand." Water 13, no. 9: 1226.
The general perception of small run-of-river hydropower plants as renewable energy sources with little or no environmental impacts has led to a global proliferation of this hydropower technology. However, such hydropower schemes may alter the natural flow regime and impair the fluvial ecosystem at different trophic levels. This paper presents a global-scale analysis of the major ecological impacts of three main small run-of-river hydropower types: dam-toe, diversion weir, and pondage schemes. This review's main objective is to provide an extensive overview of how changing the natural flow regime due to hydropower operation may affect various aspects of the fluvial ecosystem. Ultimately, it will inform decision-makers in water resources and ecosystem conservation for better planning and management. This review analyses data on ecological impacts from 33 countries in five regions, considering the last forty years' most relevant publications, a total of 146 peer-reviewed publications. The analysis was focused on impacts in biota, water quality, hydrologic alteration, and geomorphology. The results show, notably, the diversion weir and the pondage hydropower schemes are less eco-friendly; the opposite was concluded for the dam-toe hydropower scheme. Although there was conflicting information from different countries and sources, the most common impacts are: water depletion downstream of the diversion, water quality deterioration, loss of longitudinal connectivity, habitat degradation, and simplification of the biota community composition. A set of potential non-structural and structural mitigation measures was recommended to mitigate several ecological impacts such as connectivity loss, fish injuries, and aquatic habitat degradation. Among mitigation measures, environmental flows are fundamental for fluvial ecosystem conservation. The main research gaps and some of the pressing future research needs were highlighted, as well. Finally, interdisciplinary research progress involving different stakeholders is crucial to harmonize conflicting interests and enable the sustainable development of small run-of-river hydropower plants.
Alban Kuriqi; António N. Pinheiro; Alvaro Sordo-Ward; María D. Bejarano; Luis Garrote. Ecological impacts of run-of-river hydropower plants—Current status and future prospects on the brink of energy transition. Renewable and Sustainable Energy Reviews 2021, 142, 110833 .
AMA StyleAlban Kuriqi, António N. Pinheiro, Alvaro Sordo-Ward, María D. Bejarano, Luis Garrote. Ecological impacts of run-of-river hydropower plants—Current status and future prospects on the brink of energy transition. Renewable and Sustainable Energy Reviews. 2021; 142 ():110833.
Chicago/Turabian StyleAlban Kuriqi; António N. Pinheiro; Alvaro Sordo-Ward; María D. Bejarano; Luis Garrote. 2021. "Ecological impacts of run-of-river hydropower plants—Current status and future prospects on the brink of energy transition." Renewable and Sustainable Energy Reviews 142, no. : 110833.
Drought is a fundamental physical feature of the climate pattern worldwide. Over the past few decades, a natural disaster has accelerated its occurrence, which has significantly impacted agricultural systems, economies, environments, water resources, and supplies. Therefore, it is essential to develop new techniques that enable comprehensive determination and observations of droughts over large areas with satisfactory spatial and temporal resolution. This study modeled a new drought index called the Combined Terrestrial Evapotranspiration Index (CTEI), developed in the Ganga river basin. For this, five Machine Learning (ML) techniques, derived from artificial intelligence theories, were applied: the Support Vector Machine (SVM) algorithm, decision trees, Matern 5/2 Gaussian process regression, boosted trees, and bagged trees. These techniques were driven by twelve different models generated from input combinations of satellite data and hydrometeorological parameters. The results indicated that the eighth model performed best and was superior among all the models, with the SVM algorithm resulting in an R2 value of 0.82 and the lowest errors in terms of the Root Mean Squared Error (RMSE) (0.33) and Mean Absolute Error (MAE) (0.20), followed by the Matern 5/2 Gaussian model with an R2 value of 0.75 and RMSE and MAE of 0.39 and 0.21 mm/day, respectively. Moreover, among all the five methods, the SVM and Matern 5/2 Gaussian methods were the best-performing ML algorithms in our study of CTEI predictions for the Ganga basin.
Ahmed Elbeltagi; Nikul Kumari; Jaydeo Dharpure; Ali Mokhtar; Karam Alsafadi; Manish Kumar; Behrouz Mehdinejadiani; Hadi Ramezani Etedali; Youssef Brouziyne; Abu Towfiqul Islam; Alban Kuriqi. Prediction of Combined Terrestrial Evapotranspiration Index (CTEI) over Large River Basin Based on Machine Learning Approaches. Water 2021, 13, 547 .
AMA StyleAhmed Elbeltagi, Nikul Kumari, Jaydeo Dharpure, Ali Mokhtar, Karam Alsafadi, Manish Kumar, Behrouz Mehdinejadiani, Hadi Ramezani Etedali, Youssef Brouziyne, Abu Towfiqul Islam, Alban Kuriqi. Prediction of Combined Terrestrial Evapotranspiration Index (CTEI) over Large River Basin Based on Machine Learning Approaches. Water. 2021; 13 (4):547.
Chicago/Turabian StyleAhmed Elbeltagi; Nikul Kumari; Jaydeo Dharpure; Ali Mokhtar; Karam Alsafadi; Manish Kumar; Behrouz Mehdinejadiani; Hadi Ramezani Etedali; Youssef Brouziyne; Abu Towfiqul Islam; Alban Kuriqi. 2021. "Prediction of Combined Terrestrial Evapotranspiration Index (CTEI) over Large River Basin Based on Machine Learning Approaches." Water 13, no. 4: 547.
Accurate monitoring and forecasting of drought are crucial. They play a vital role in the optimal functioning of irrigation systems, risk management, drought readiness, and alleviation. In this work, Artificial Intelligence (AI) models, comprising Multi-layer Perceptron Neural Network (MLPNN) and Co-Active Neuro-Fuzzy Inference System (CANFIS), and regression, model including Multiple Linear Regression (MLR), were investigated for multi-scalar Standardized Precipitation Index (SPI) prediction in the Garhwal region of Uttarakhand State, India. The SPI was computed on six different scales, i.e., 1-, 3-, 6-, 9-, 12-, and 24-month, by deploying monthly rainfall information of available years. The significant lags as inputs for the MLPNN, CANFIS, and MLR models were obtained by utilizing Partial Autocorrelation Function (PACF) with a significant level equal to 5% for SPI-1, SPI-3, SPI-6, SPI-9, SPI-12, and SPI-24. The predicted multi-scalar SPI values utilizing the MLPNN, CANFIS, and MLR models were compared with calculated SPI of multi-time scales through different performance evaluation indicators and visual interpretation. The appraisals of results indicated that CANFIS performance was more reliable for drought prediction at Dehradun (3-, 6-, 9-, and 12-month scales), Chamoli and Tehri Garhwal (1-, 3-, 6-, 9-, and 12-month scales), Haridwar and Pauri Garhwal (1-, 3-, 6-, and 9-month scales), Rudraprayag (1-, 3-, and 6-month scales), and Uttarkashi (3-month scale) stations. The MLPNN model was best at Dehradun (1- and 24- month scales), Tehri Garhwal and Chamoli (24-month scale), Haridwar (12- and 24-month scales), Pauri Garhwal (12-month scale), Rudraprayag (9-, 12-, and 24-month), and Uttarkashi (1- and 6-month scales) stations, while the MLR model was found to be optimal at Pauri Garhwal (24-month scale) and Uttarkashi (9-, 12-, and 24-month scales) stations. Furthermore, the modeling approach can foster a straightforward and trustworthy expert intelligent mechanism for projecting multi-scalar SPI and decision making for remedial arrangements to tackle meteorological drought at the stations under study.
Anurag Malik; Anil Kumar; Priya Rai; Alban Kuriqi. Prediction of Multi-Scalar Standardized Precipitation Index by Using Artificial Intelligence and Regression Models. Climate 2021, 9, 28 .
AMA StyleAnurag Malik, Anil Kumar, Priya Rai, Alban Kuriqi. Prediction of Multi-Scalar Standardized Precipitation Index by Using Artificial Intelligence and Regression Models. Climate. 2021; 9 (2):28.
Chicago/Turabian StyleAnurag Malik; Anil Kumar; Priya Rai; Alban Kuriqi. 2021. "Prediction of Multi-Scalar Standardized Precipitation Index by Using Artificial Intelligence and Regression Models." Climate 9, no. 2: 28.
Estimating sediment flow rate from a drainage area plays an essential role in better watershed planning and management. In this study, the validity of simple and wavelet-coupled Artificial Intelligence (AI) models was analyzed for daily Suspended Sediment (SSC) estimation of highly dynamic Koyna River basin of India. Simple AI models such as the Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were developed by supplying the original time series data as an input without pre-processing through a Wavelet (W) transform. The hybrid wavelet-coupled W-ANN and W-ANFIS models were developed by supplying the decomposed time series sub-signals using Discrete Wavelet Transform (DWT). In total, three mother wavelets, namely Haar, Daubechies, and Coiflets were employed to decompose original time series data into different multi-frequency sub-signals at an appropriate decomposition level. Quantitative and qualitative performance evaluation criteria were used to select the best model for daily SSC estimation. The reliability of the developed models was also assessed using uncertainty analysis. Finally, it was revealed that the data pre-processing using wavelet transform improves the model’s predictive efficiency and reliability significantly. In this study, it was observed that the performance of the Coiflet wavelet-coupled ANFIS model is superior to other models and can be applied for daily SSC estimation of the highly dynamic rivers. As per sensitivity analysis, previous one-day SSC (St-1) is the most crucial input variable for daily SSC estimation of the Koyna River basin.
Tarate Bajirao; Pravendra Kumar; Manish Kumar; Ahmed Elbeltagi; Alban Kuriqi. Superiority of Hybrid Soft Computing Models in Daily Suspended Sediment Estimation in Highly Dynamic Rivers. Sustainability 2021, 13, 542 .
AMA StyleTarate Bajirao, Pravendra Kumar, Manish Kumar, Ahmed Elbeltagi, Alban Kuriqi. Superiority of Hybrid Soft Computing Models in Daily Suspended Sediment Estimation in Highly Dynamic Rivers. Sustainability. 2021; 13 (2):542.
Chicago/Turabian StyleTarate Bajirao; Pravendra Kumar; Manish Kumar; Ahmed Elbeltagi; Alban Kuriqi. 2021. "Superiority of Hybrid Soft Computing Models in Daily Suspended Sediment Estimation in Highly Dynamic Rivers." Sustainability 13, no. 2: 542.
Accurate information about groundwater level prediction is crucial for effective planning and management of groundwater resources. In the present study, the Artificial Neural Network (ANN), optimized with a Genetic Algorithm (GA-ANN), was employed for seasonal groundwater table depth (GWTD) prediction in the area between the Ganga and Hindon rivers located in Uttar Pradesh State, India. A total of 18 models for both seasons (nine for the pre-monsoon and nine for the post-monsoon) have been formulated by using groundwater recharge (GWR), groundwater discharge (GWD), and previous groundwater level data from a 21-year period (1994–2014). The hybrid GA-ANN models’ predictive ability was evaluated against the traditional GA models based on statistical indicators and visual inspection. The results appraisal indicates that the hybrid GA-ANN models outperformed the GA models for predicting the seasonal GWTD in the study region. Overall, the hybrid GA-ANN-8 model with an 8-9-1 structure (i.e., 8: inputs, 9: neurons in the hidden layer, and 1: output) was nominated optimal for predicting the GWTD during pre- and post-monsoon seasons. Additionally, it was noted that the maximum number of input variables in the hybrid GA-ANN approach improved the prediction accuracy. In conclusion, the proposed hybrid GA-ANN model’s findings could be readily transferable or implemented in other parts of the world, specifically those with similar geology and hydrogeology conditions for sustainable planning and groundwater resources management.
Kusum Pandey; Shiv Kumar; Anurag Malik; Alban Kuriqi. Artificial Neural Network Optimized with a Genetic Algorithm for Seasonal Groundwater Table Depth Prediction in Uttar Pradesh, India. Sustainability 2020, 12, 8932 .
AMA StyleKusum Pandey, Shiv Kumar, Anurag Malik, Alban Kuriqi. Artificial Neural Network Optimized with a Genetic Algorithm for Seasonal Groundwater Table Depth Prediction in Uttar Pradesh, India. Sustainability. 2020; 12 (21):8932.
Chicago/Turabian StyleKusum Pandey; Shiv Kumar; Anurag Malik; Alban Kuriqi. 2020. "Artificial Neural Network Optimized with a Genetic Algorithm for Seasonal Groundwater Table Depth Prediction in Uttar Pradesh, India." Sustainability 12, no. 21: 8932.
Environmental flow assessments (e-flows) are relatively new practices, especially in developing countries such as Nepal. This study presents a comprehensive analysis of the influence of hydrologically based e-flow methods in the natural flow regime. The study used different hydrological-based methods, namely, the Global Environmental Flow Calculator, the Tennant method, the flow duration curve method, the dynamic method, the mean annual flow method, and the annual distribution method to allocate e-flows in the Kaligandaki River. The most common practice for setting e-flows consists of allocating a specific percentage of mean annual flow or portion of flow derived from specific percentiles of the flow duration curve. However, e-flow releases should mimic the river’s intra-annual variability to meet the specific ecological function at different river trophic levels and in different periods over a year covering biotas life stages. The suitability of the methods was analyzed using the Indicators of Hydrological Alterations and e-flows components. The annual distribution method and the 30%Q-D (30% of daily discharge) methods showed a low alteration at the five global indexes for each group of Indicators of Hydrological Alterations and e-flows components, which allowed us to conclude that these methods are superior to the other methods. Hence, the study results concluded that 30%Q-D and annual distribution methods are more suitable for the e-flows implementation to meet the riverine ecosystem’s annual dynamic demand to maintain the river’s health. This case study can be used as a guideline to allocate e-flows in the Kaligandaki River, particularly for small hydropower plants.
Naresh Suwal; Alban Kuriqi; Xianfeng Huang; João Delgado; Dariusz Młyński; Andrzej Walega. Environmental Flows Assessment in Nepal: The Case of Kaligandaki River. Sustainability 2020, 12, 8766 .
AMA StyleNaresh Suwal, Alban Kuriqi, Xianfeng Huang, João Delgado, Dariusz Młyński, Andrzej Walega. Environmental Flows Assessment in Nepal: The Case of Kaligandaki River. Sustainability. 2020; 12 (21):8766.
Chicago/Turabian StyleNaresh Suwal; Alban Kuriqi; Xianfeng Huang; João Delgado; Dariusz Młyński; Andrzej Walega. 2020. "Environmental Flows Assessment in Nepal: The Case of Kaligandaki River." Sustainability 12, no. 21: 8766.
Streamflow forecasting is vital for designing and managing water resources systems. This study evaluates the prediction accuracy of two heuristic methods, artificial neural network-genetic algorithm (ANN-GA) and adaptive neurofuzzy inference system-genetic algorithm (ANFIS-GA) in streamflow prediction using monthly streamflow data of Neelum and Kunhar Rivers of Pakistan. The prediction capability of two methods are tested using the different time lags input combinations using statistical indicators and compared with M5 Regression Tree (M5RT) model. In results, it is found that ANN-GA and ANFIS-GA provided better prediction accuracy than M5RT model. Addition of month number showed a positive effect of periodicity on the prediction accuracy of models.
Rana Muhammad Adnan; Zhongmin Liang; Alban Kuriqi; Ozgur Kisi; Anurag Malik; Binquan Li. Streamflow forecasting using heuristic machine learning methods. 2020 2nd International Conference on Computer and Information Sciences (ICCIS) 2020, 1 -6.
AMA StyleRana Muhammad Adnan, Zhongmin Liang, Alban Kuriqi, Ozgur Kisi, Anurag Malik, Binquan Li. Streamflow forecasting using heuristic machine learning methods. 2020 2nd International Conference on Computer and Information Sciences (ICCIS). 2020; ():1-6.
Chicago/Turabian StyleRana Muhammad Adnan; Zhongmin Liang; Alban Kuriqi; Ozgur Kisi; Anurag Malik; Binquan Li. 2020. "Streamflow forecasting using heuristic machine learning methods." 2020 2nd International Conference on Computer and Information Sciences (ICCIS) , no. : 1-6.
Floods are one of nature's most destructive disasters because of the immense damage to land, buildings, and human fatalities. It is difficult to forecast the areas that are vulnerable to flash flooding due to the dynamic and complex nature of the flash floods. Therefore, earlier identification of flash flood susceptible sites can be performed using advanced machine learning models for managing flood disasters. In this study, we applied and assessed two new hybrid ensemble models, namely Dagging and Random Subspace (RS) coupled with Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Machine (SVM) which are the other three state-of-the-art machine learning models for modelling flood susceptibility maps at the Teesta River basin, the northern region of Bangladesh. The application of these models includes twelve flood influencing factors with 413 current and former flooding points, which were transferred in a GIS environment. The information gain ratio, the multicollinearity diagnostics tests were employed to determine the association between the occurrences and flood influential factors. For the validation and the comparison of these models, for the ability to predict the statistical appraisal measures such as Freidman, Wilcoxon signed-rank, and t-paired tests and Receiver Operating Characteristic Curve (ROC) were employed. The value of the Area Under the Curve (AUC) of ROC was above 0.80 for all models. For flood susceptibility modelling, the Dagging model performs superior, followed by RF, the ANN, the SVM, and the RS, then the several benchmark models. The approach and solution-oriented outcomes outlined in this paper will assist state and local authorities as well as policy makers in reducing flood-related threats and will also assist in the implementation of effective mitigation strategies to mitigate future damage.
Abu Reza Md Towfiqul Islam; Swapan Talukdar; Susanta Mahato; Sonali Kundu; Kutub Uddin Eibek; Quoc Bao Pham; Alban Kuriqi; Nguyen Thi Thuy Linh. Flood susceptibility modelling using advanced ensemble machine learning models. Geoscience Frontiers 2020, 12, 101075 .
AMA StyleAbu Reza Md Towfiqul Islam, Swapan Talukdar, Susanta Mahato, Sonali Kundu, Kutub Uddin Eibek, Quoc Bao Pham, Alban Kuriqi, Nguyen Thi Thuy Linh. Flood susceptibility modelling using advanced ensemble machine learning models. Geoscience Frontiers. 2020; 12 (3):101075.
Chicago/Turabian StyleAbu Reza Md Towfiqul Islam; Swapan Talukdar; Susanta Mahato; Sonali Kundu; Kutub Uddin Eibek; Quoc Bao Pham; Alban Kuriqi; Nguyen Thi Thuy Linh. 2020. "Flood susceptibility modelling using advanced ensemble machine learning models." Geoscience Frontiers 12, no. 3: 101075.
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.
Manish Kumar; Anuradha Kumari; Daniel Kushwaha; Pravendra Kumar; Anurag Malik; Rawshan Ali; Alban Kuriqi. Estimation of Daily Stage–Discharge Relationship by Using Data-Driven Techniques of a Perennial River, India. Sustainability 2020, 12, 7877 .
AMA StyleManish Kumar, Anuradha Kumari, Daniel Kushwaha, Pravendra Kumar, Anurag Malik, Rawshan Ali, Alban Kuriqi. Estimation of Daily Stage–Discharge Relationship by Using Data-Driven Techniques of a Perennial River, India. Sustainability. 2020; 12 (19):7877.
Chicago/Turabian StyleManish Kumar; Anuradha Kumari; Daniel Kushwaha; Pravendra Kumar; Anurag Malik; Rawshan Ali; Alban Kuriqi. 2020. "Estimation of Daily Stage–Discharge Relationship by Using Data-Driven Techniques of a Perennial River, India." Sustainability 12, no. 19: 7877.
A better understanding of intra/inter-annual streamflow variability and trends enables more effective water resources planning and management for current and future needs. This paper investigates the variability and trends of streamflow data from five stations (i.e. Ashti, Chindnar, Pathgudem, Polavaram, and Tekra) in Godavari river basin, India. The streamflow data were obtained from the Indian Central Water Commission and cover more than 30 years of mean daily records (i.e. 1972–2011). The streamflow data were statistically assessed using Gamma, Generalised Extreme Value and Normal distributions to understand the probability distribution features of data at inter-annual time-scale. Quantifiable changes in observed streamflow data were identified by Sen’s slope method. Two other nonparametric, Mann–Kendall and Innovative Trend Analysis methods were also applied to validate findings from Sen’s slope trend analysis. The mean flow discharge for each month (i.e. January to December), seasonal variation (i.e. Spring, Summer, Autumn, and Winter) as well as an annual mean, annual maximum and minimum flows were analysed for each station. The results show that three stations (i.e. Ashti, Tekra, and Polavaram) demonstrate an increasing trend, notably during Winter and Spring. In contrast, two other stations (i.e. Pathgudem, Chindnar) revealed a decreasing trend almost at all seasons. A significant decreasing trend was observed at all station over Summer and Autumn seasons. Notably, all stations showed a decreasing trend in maximum flows; remarkably, Tekra station revealed the highest decreasing magnitude. Significant decrease in minimum flows was observed in two stations only, Chindnar and Pathgudem. Findings resulted from this study might be useful for water managers and decision-makers to propose more sustainable water management recommendations and practices.
Alban Kuriqi; Rawshan Ali; Quoc Bao Pham; Julio Montenegro Gambini; Vivek Gupta; Anurag Malik; Nguyen Thi Thuy Linh; Yogesh Joshi; Duong Tran Anh; Van Thai Nam; Xiaohua Dong. Seasonality shift and streamflow flow variability trends in central India. Acta Geophysica 2020, 68, 1461 -1475.
AMA StyleAlban Kuriqi, Rawshan Ali, Quoc Bao Pham, Julio Montenegro Gambini, Vivek Gupta, Anurag Malik, Nguyen Thi Thuy Linh, Yogesh Joshi, Duong Tran Anh, Van Thai Nam, Xiaohua Dong. Seasonality shift and streamflow flow variability trends in central India. Acta Geophysica. 2020; 68 (5):1461-1475.
Chicago/Turabian StyleAlban Kuriqi; Rawshan Ali; Quoc Bao Pham; Julio Montenegro Gambini; Vivek Gupta; Anurag Malik; Nguyen Thi Thuy Linh; Yogesh Joshi; Duong Tran Anh; Van Thai Nam; Xiaohua Dong. 2020. "Seasonality shift and streamflow flow variability trends in central India." Acta Geophysica 68, no. 5: 1461-1475.
The water-energy-ecosystem nexus represents a complex interlinkage that depends on the flow regime type. Inadequate environmental flows setting may adversely affect the riverine ecosystem and/or hydropower revenue. This issue was addressed quantitatively in this study by considering a run-of-river hydropower plant located in a river of the Tagus basin characterised by a pluvial winter flow regime. Three models: a hydropower, a hydrologic, and an ecohydraulic model were integrated to estimate the influence of nine hydrologically-based environmental flow methods on hydropower production, flow regime alteration and fish habitat conditions. The target fish species was a native cyprinid fish, the Iberian barbel (Luciobarbus bocagei), considering three life-stages. Results show that high environmental flow releases did not necessarily provide the highest habitat availability and suitability at all seasons and fish life-stages. The adult life-stage resulted in being more vulnerable to water diversion, particularly during Spring season. Shallow-water hydromorphological units suffered the highest habitat loss. Some of the environmental flow methods demonstrated inconsistent results over seasons and fish life-stages by either allowing for higher environmental flow releases whereas highly restricting hydropower production or vice versa. However, results also suggest that combined and dynamic environmental flow methods promoted reasonable hydropower production while inducing less than 50% habitat loss and flow regime alteration, regarding the three life-stages over all seasons. This study contributes to the identification of the relationship between conflicting objectives, such as hydropower production and riverine ecosystem conservation. Also, it provides strategic recommendations on energy-ecosystem regulation for sustainable hydropower operation. This study also highlights the importance of considering seasonal flow discharge variability when analysing the water-energy-ecosystem nexus.
Alban Kuriqi; António N. Pinheiro; Alvaro Sordo-Ward; Luis Garrote. Water-energy-ecosystem nexus: Balancing competing interests at a run-of-river hydropower plant coupling a hydrologic–ecohydraulic approach. Energy Conversion and Management 2020, 223, 113267 .
AMA StyleAlban Kuriqi, António N. Pinheiro, Alvaro Sordo-Ward, Luis Garrote. Water-energy-ecosystem nexus: Balancing competing interests at a run-of-river hydropower plant coupling a hydrologic–ecohydraulic approach. Energy Conversion and Management. 2020; 223 ():113267.
Chicago/Turabian StyleAlban Kuriqi; António N. Pinheiro; Alvaro Sordo-Ward; Luis Garrote. 2020. "Water-energy-ecosystem nexus: Balancing competing interests at a run-of-river hydropower plant coupling a hydrologic–ecohydraulic approach." Energy Conversion and Management 223, no. : 113267.