Data Intelligence Model and Meta-Heuristic Algorithms-Based Pan Evaporation Modelling in Two Different Agro-Climatic Zones: A Case Study from Northern India

Kushwaha, Nand Lal; Rajput, Jitendra; Elbeltagi, Ahmed; Elnaggar, Ashraf Y.; Sena, D.R.; Vishwakarma, Dinesh Kumar; Mani, Indra; Hussein, Enas E.

doi:10.3390/atmos12121654

Cited by 70 publications

(34 citation statements)

References 43 publications

(73 reference statements)

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“…The SRM computer program involves the assessment of model accuracy through the graphical representation of the estimated hydrograph and observed runoff. In addition to the plots, the SRM uses two performance indices, the percent volume difference (Dv) and the coefficient of determination (R 2 ) [24,32,[51][52][53]. The coefficient of determination is estimated as follows:…”

Section: Model Accuracymentioning

confidence: 99%

Assessment of Climate Change Impact on Snowmelt Runoff in Himalayan Region

et al. 2022

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Under different climate change scenarios, the current study was planned to simulate runoff due to snowmelt in the Lidder River catchment in the Himalayan region. A basic degree-day model, the Snowmelt-Runoff Model (SRM), was utilized to assess the hydrological consequences of change in the climate. The performance of the SRM model during calibration and validation was assessed using volume difference (Dv) and coefficient of determination (R2). The Dv was found to be 11.7, −10.1, −11.8, 1.96, and 8.6 in 2009–2014, respectively, while the respective R2 was 0.96, 0.92, 0.95, 0.90, and 0.94. The Dv and R2 values indicate that the simulated snowmelt runoff closely agrees with the observed values. The simulated findings were assessed under three different climate change scenarios: (a) an increase in precipitation by +20%, (b) a temperature rise of +2 °C, and (c) a temperature rise of +2 °C with a 20% increase in snow cover. In scenario (b), the simulated results showed that runoff increased by 53% in summer (April–September). In contrast, the projected increased discharge for scenarios (a) and (c) was 37% and 67%, respectively. The SRM efficiently forecasts future water supplies due to snowmelt runoff in high elevation, data-scarce mountain environments.

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Section: Model Accuracymentioning

confidence: 99%

Assessment of Climate Change Impact on Snowmelt Runoff in Himalayan Region

et al. 2022

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“…The success of machine learning models is mainly governed by a good selection of the best predictors, i.e., the best input variables (Malik et al 2019 ; Shukla et al 2021 ; Kushwaha et al 2021 ; Elbeltagi et al 2022b , a; Kumar et al 2022 ). From a general point of view, based on the available input variables, we believe that testing several input combinations is the more suitable procedure for obtaining the best final model; in addition, testing several input combinations can help provide a multitude of alternatives with different structures.…”

Section: Resultsmentioning

confidence: 99%

“…Taylor diagrams, radar charts, and box plots were also investigated to visually compare model performance (Taylor 2001 ; Citakoglu 2021 ; Başakın et al 2022 ; Görkemli et al 2022 ). More details about models evaluation and comparison can be found in Kushwaha et al ( 2021 ), Elbeltagi et al ( 2022a ), and Vishwakarma et al ( 2022 ).…”

Section: Statistical Performance Assessmentmentioning

confidence: 99%

“…Over the past two decades, artificial intelligence (AI) and machine learning techniques have been successfully developed and widely used for estimating and predicting (Citakoglu and Coşkun 2022 ), in particular, modeling non-linear hydrologic systems and agriculture field (Shukla et al 2021 ), meteorological droughts and standardized precipitation index (SPI) (Malik et al 2021 ; Xu et al 2022 ), lake water level (Zhu et al 2020 ), rainfall forecasting (Luk et al 2001 ; Olsson et al 2004 ; Abbot and Marohasy 2012 ; Lee et al 2018 ; Mirabbasi et al 2019 ; Adnan et al 2020 ; Armin et al 2021 ; Khosravi et al 2022 ), streamflow forecasting (Yaseen et al 2016 ; Shukla et al 2021 ; Khodakhah et al 2022 ), hydrological drought (Shamshirband et al 2020 ; Aghelpour et al 2021 ; Muhammad et al 2021 ; Almikaeel et al 2022 ), pan evaporation forecasting (Shiri and Özgur 2011 ; Mohammad et al 2019 ; Malik et al 2020 ; Al-Mukhtar 2021 ; Kushwaha et al 2021 ), evapotranspiration (Granata 2019 ; Wu et al 2019 ; Tikhamarine et al 2019 , 2020 ; Chen et al 2020 ; Chia et al 2020 ; Ferreira and da Cunha 2020 ; Elbeltagi et al 2022b ), water level forecasting (Daliakopoulos et al 2005 ; Nayak et al 2006 ; Ali Ghorbani et al 2010 ; Kisi et al 2012 ; Buyukyildiz et al 2014 ; Seo et al 2015 , 2017 ), velocity predictions in compound channels with vegetated floodplains (Harris et al 2003 ), suspended sediment load prediction (Melesse et al 2011 ; Rajaee et al 2011 ; Azamathulla et al 2013 ; Kakaei Lafdani et al 2013 ; Gupta et al 2021 ), soil temperature (Yang and Wang 2008 ; Bilgili 2010 ; Singh et al 2018 ; Penghui et al 2020 ...…”

Section: Introductionmentioning

confidence: 99%

“…Taylor diagrams, radar charts, and box plots were also investigated to visually compare model performance(Taylor 2001). More details about models evaluation and comparison can be found in(Kushwaha et al 2021;Elbeltagi et al 2022;…”

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Pre- and post-dam river water temperature alteration prediction using advanced machine learning models

Vishwakarma

Ali

Bhat

et al. 2022

Environ Sci Pollut Res

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Dams significantly impact river hydrology by changing the timing, size, and frequency of low and high flows, resulting in a hydrologic regime that differs significantly from the natural flow regime before the impoundment. For precise planning and judicious use of available water resources for agricultural operations and aquatic habitats, it is critical to assess the dam water’s temperature accurately. The building of dams, particularly several dams in rivers, can significantly impact downstream water. In this study, we predict the daily water temperature of the Yangtze River at Cuntan. Thus, this work reveals the potential of machine learning models, namely, M5 Pruned (M5P), Random Forest (RF), Random Subspace (RSS), and Reduced Error Pruning Tree (REPTree). The best and effective input variables combinations were determined based on the correlation coefficient. The outputs of the various machine learning algorithm models were compared with recorded daily water temperature data using goodness-of-fit criteria and graphical analysis to arrive at a final comparison. Based on a number of criteria, numerical comparison between the models revealed that M5P model performed superior ( R 2 = 0.9920, 0.9708; PCC = 0.9960, 0.9853; MAE = 0.2387, 0.4285; RMSE = 0.3449, 0.4285; RAE = 6.2573, 11.5439; RRSE = 8.0288, 13.8282) in pre-impact and post-impact spam, respectively. These findings suggest that a huge wave of dam construction in the previous century altered the hydrologic regimes of large and minor rivers. This study will be helpful for the ecologists and river experts in planning new reservoirs to maintain the flows and minimize the water temperature concerning spillway operation. Finally, our findings revealed that these algorithms could reliably estimate water temperature using a day lag time input in water level. They are cost-effective techniques for forecasting purposes.

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Reference evapotranspiration prediction using machine learning models: An empirical study from minimal climate data

Shaloo,

Kumar,

Bisht

et al. 2023

Agronomy Journal

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The scarcity of climatic data is the biggest challenge for developing countries and the development of models for reference evapotranspiration (ET0) estimation with limited datasets is crucial. Therefore, the current investigation assessed the efficacy of four machine learning (ML) models viz., Linear Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Neural Networks (NN) to predict ET0 based on minimal climate data in comparison with the standard FAO‐56 Penman‐Monteith (PM) method. The data on daily climate parameters were collected for the period 2000−2021, including maximum and minimum temperature (Tmax, Tmin), mean relative humidity (RH), wind speed (WS) and sunshine hours (SSH). The performance of the developed models considering different input combinations was evaluated by using several statistical performance measures. The results showed that the SVM model performed better than the other ML models during training (R2 = 0.985; mean absolute error [MAE] = 0.170 mm/d; mean square error [MSE] = 0.052 mm/d; root mean square error [RMSE] = 0.229 mm/d; mean absolute percentage error [MAPE] = 5.72%,) and testing stages (R2 = 0.985; MAE = 0.168 mm/d; MSE = 0.050 mm/d; RMSE = 0.224 mm/d; MAPE = 5.91%) under full dataset scenario. The best performance of the models to estimate was with Tmax, RH. Ws, SSH and Tmin. The results of the current study are substantial as it offers an approach to estimate ET0 in semi‐arid data scarce region.This article is protected by copyright. All rights reserved

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Data Intelligence Model and Meta-Heuristic Algorithms-Based Pan Evaporation Modelling in Two Different Agro-Climatic Zones: A Case Study from Northern India

Cited by 70 publications

References 43 publications

Assessment of Climate Change Impact on Snowmelt Runoff in Himalayan Region

Assessment of Climate Change Impact on Snowmelt Runoff in Himalayan Region

Pre- and post-dam river water temperature alteration prediction using advanced machine learning models

Reference evapotranspiration prediction using machine learning models: An empirical study from minimal climate data

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