Estimation of crop evapotranspiration using statistical and machine learning techniques with limited meteorological data: a case study in Udham Singh Nagar, India

Theoretical and Applied Climatology, Год журнала: 2024, Номер 155(6), С. 5279 - 5296

Опубликована: Апрель 3, 2024

Язык: Английский

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An attention mechanism augmented CNN-GRU method integrating optimized variational mode decomposition and frequency feature classification for complex signal forecasting

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 126464 - 126464

Опубликована: Янв. 1, 2025

Язык: Английский

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Evaluating land use and climate change impacts on Ravi river flows using GIS and hydrological modeling approach

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Сен. 27, 2024

Язык: Английский

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Evaluating statistical and machine learning techniques for sugarcane yield forecasting in the tarai region of North India

Computers and Electronics in Agriculture, Год журнала: 2024, Номер 229, С. 109667 - 109667

Опубликована: Дек. 9, 2024

Язык: Английский

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Optimizing water management and climate-resilient agriculture in rice-fallow regions of the Dwarakeswar river basin using ML models

Deleted Journal, Год журнала: 2025, Номер 7(4)

Опубликована: Апрель 11, 2025

Язык: Английский

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Improved streamflow prediction accuracy in Boreal climate watershed using a LSTM model: A comparative study

PLOS Water, Год журнала: 2025, Номер 4(4), С. e0000359 - e0000359

Опубликована: Апрель 21, 2025

Streamflow plays a vital role in water resource management and environmental impact assessment. This study is novel application of the Long Short-Term Memory (LSTM) model, type recurrent neural network, for real-time streamflow prediction Upper Humber River Watershed western Newfoundland. It also compares performance LSTM model with physically based SWAT model. The was optimized by tuning hyperparameters adjusting window size to balance capturing historical data ensuring stability. Using single input variables such as daily average temperature or precipitation, achieved high Nash-Sutcliffe Efficiency (NSE) 0.95. In comparison, results show that delivers more competitive performance, achieving an NSE 0.95 versus SWAT’s 0.77, percent bias (PBIAS) 0.62 compared 8.26. Unlike SWAT, does not overestimate flows excels predicting low flows. Additionally, successfully predicted using data. Despite challenges interpretability generalizability, demonstrated strong particularly during extreme events, making it valuable tool cold climates where accurate forecasts are crucial effective management. highlights potential model’s

Язык: Английский

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Comparative evaluation of hybrid and individual models for predicting soybean yellow mosaic virus incidence

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Май 6, 2025

Abstract Forecasting the severity of crop diseases is crucial for agricultural productivity and can be achieved through statistical machine learning techniques. Predictive models that consider weather conditions during critical growth stages crops have shown promising accuracy. However, selecting most suitable forecasting model remains a challenge. This research investigates impact various factors on Soybean Yellow Mosaic Virus (SYMV) incidence. Specifically, six multivariate Stepwise Multiple Linear Regression (SMLR), Artificial Neural Networks (ANN), Least Absolute Shrinkage Selection Operator (LASSO), Ridge (RR), Elastic Net (ELNET), SMLR_ANN both direct with Principal Component Analysis (PCA)-were developed using 20 years data (2001 to 2020) predict soybean disease in Pantnagar, Uttarakhand. The dataset was divided into two parts, 80% used calibration remaining 20% validation. Model accuracy evaluated several criteria, including R², RMSE, nRMSE, MAE, PE, EF. results indicated PCA-SMLR-ANN (nRMSE val = 0.76%) effective predictor severity, closely followed by PCA-ANN 3.67%) model. Hybrid such as outperformed individual like SMLR 47.72%) ANN 6.82%). performance ranking follows: ≈ SMLR-ANN > PCA-ELNET PCA-Ridge ELNET RR PCA-LASSO LASSO PCA-SMLR SMLR. These findings highlight superior efficiency hybrid predicting based indices study region.

Язык: Английский

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Multi-time scale analysis of the water level minima in Lake Titicaca over the past 103 years

Frontiers in Climate, Год журнала: 2025, Номер 7

Опубликована: Май 7, 2025

Lowest events in Lake Titicaca’s water level (LTWL) significantly impact local ecosystems and the drinking supply Peru Bolivia. However, hydroclimatic mechanisms driving extreme lake-level lowstands remain poorly understood. To investigate these low events, we analyzed detrended monthly LTWL anomalies, sea surface temperature (SST) datasets covering period 1921–2023. ERA5 reanalysis covers 1940–2023. A multiple linear regression model was developed to compute excluding multidecadal residual components. Interdecadal Pacific Oscillation (IPO) Decadal (PDO) indices were also for same period. Results indicate that 25% of all minima have a short duration <5 months, while remaining 75% long more than 9 respectively. All long-lived are associated with reduced moisture flow from Amazon basin toward Titicaca, but large-scale forcing varies phase change decadal component 11–15 years band PDO (PDO ). Under warm phases, driven by an enhanced South American low-level jet (SALLJ) caused SST anomalies over eastern Ocean. Warm tropical North Atlantic central cold which reinforce through reduction SALLJ. Conversely, under neutral phases westerly confined Peruvian Altiplano. Therefore, IPO do not drive because their relationship does consistent time. In conclusion, exhibit regional nature or IPO, as shows no modes

Язык: Английский

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Developing a novel hybrid model based on GRU deep neural network and Whale optimization algorithm for precise forecasting of river’s streamflow

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Июнь 3, 2025

Streamflow contemplates a fundamental criterion to evaluate the impact of human activities and climate changes on hydrological cycle. In this study, novel innovative deep neural network (DNN) structure by integrating double Gated Recurrent Units (GRU) model with multiplication layer meta-heuristic whale optimization algorithm (WOA) (i.e., hybrid 2GRU×-WOA model) is developed improve prediction accuracy performance mean monthly Chehel-Chai River's streamflow (CCRSFm) in Iran. The Pearson's correlation coefficient (PCC) Cosine Amplitude Sensitivity (CAS) as feature (input) selection process determine only precipitation (Pm) most effective input variable among list on-site potential time series parameters recorded study area. Thanks well-proportioned structural framework suggested model, it leads an appropriate total learnable parameter (TLP) compared standard individual GRU Bi-GRU benchmark models comparable meta-parameters. This under optimal meant meta-parameters tuned i.e., coupling state activation functions (SAF) tanh-softsign, dropout rate (P-rate) 0.5, numbers hidden neurons (NHN) 70, outperforms R2 0.79, NSE 0.76, MAE 0.21 (m3/s), MBE -0.11(m3/s), RMSE 0.36 (m3/s). Hybridizing 2GRU× WOA causes increase value 6.8% reduce 20.4%. Comparatively, result 0.59 0.66, 0.55 0.6, 0.91 0.53 0.047 - 0.06 1.29 0.83 respectively.

Язык: Английский

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Evaluating the Accuracy of Machine Learning, Deep Learning and Hybrid Algorithms for Flood Routing Calculations

Pure and Applied Geophysics, Год журнала: 2024, Номер unknown

Опубликована: Окт. 26, 2024

Язык: Английский

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