A comparative survey between cascade correlation neural network (CCNN) and feedforward neural network (FFNN) machine learning models for forecasting suspended sediment concentration.

Suspended sediment concentration prediction is critical for the design of reservoirs, dams, rivers ecosystems, various operations of aquatic resource structure, environmental safety, and water management. In this study, two different machine models, namely the cascade correlation neural network (CCNN) and feedforward neural network (FFNN) were applied to predict daily-suspended sediment concentration (SSC) at Simga and Jondhara stations in Sheonath basin, India. Daily-suspended sediment concentration and discharge data from 2010 to 2015 were collected and used to develop the model to predict suspended sediment concentration. The developed models were evaluated using statistical indices like Nash and Sutcliffe efficiency coefficient (NES), root mean square error (RMSE), Willmott's index of agreement (WI), and Legates-McCabe's index (LM), supplemented by a scatter plot, density plots, histograms and Taylor diagram for graphical representation. The developed model was evaluated and compared with CCNN and FFNN. Nine input combinations were explored using different lag-times for discharge (Qt-n) and suspended sediment concentration (St-n) as input variables, with the current suspended sediment concentration as the desired output, to develop CCNN and FFNN models. The CCNN4 model with 4 lagged inputs (St-1, St-2, St-3, St-4) outperformed the other developed models with the lowest RMSE = 95.02 mg/l and the highest NES = 0.0.662, WI = 0.890 and LM = 0.668 for the Jondhara Station while the same CCNN4 model secure as the best with the lowest RMSE = 53.71 mg/l and the highest NES = 0.785, WI = 0.936 and LM = 0.788 for the Simga Station. The result shows the CCNN model was better than the FFNN model for predicting daily-suspended sediment at both stations in the Sheonath basin, India. Overall, CCNN showed better forecasting potential for suspended sediment concentration compared to FFNN at both stations, demonstrating their applicability for hydrological forecasting with complex relationships.

Forecasting actual evapotranspiration without climate data based on stacked integration of DNN and meta-heuristic models across China from 1958 to 2021.

As a non-linear phenomenon that varies along with agro-climatic conditions alongside many other factors, Evapotranspiration (ET) process represents a complexity when be assessed especially if there is a data scarcity in the weather data. However, even under such a data scarcity, the accurate estimates of ET values remain necessary for precise irrigation. So, the present study aims to: i) evaluate the performance of six hybrid machine learning (ML) models in estimating the monthly actual ET values under different agro-climatic conditions in China for seven provinces (Shandong, Jiangsu, Zhejiang, Fujian, Jiangxi, Hubei, and Henan), and ii) select the best-developed model based on statistical metrics and reduce errors between predicted and actual ET (AET) values. AET datasets were divided into 78% for model training (from 1958 to 2007) and the remaining was used for testing (from 2008 to 2021). Deep Neural Networks (DNN) was used as a standalone model at first then the stacking method was applied to integrate DNN with data-driven models such as Additive regression (AR), Random Forest (RF), Random Subspace (RSS), M5 Burned Tree (M5P) and Reduced Error Purning Tree (REPTree). Partial Auto-Correlation Function (PACF) was used for selection of the best lags inputs to the developed models. Results have revealed that DNN-based hybrid models held better performance than non-hybrid DNN models, such that the DNN-RF algorithm outperformed others during both training and testing stages, followed by DNN-RSS. This model has acquired the best values of every statistical measure [MAE (10.8, 12.9), RMSE (15.6, 17.4), RAE (31.9%, 41.4%), and RRSE (39.3%, 47.2%)] for training and testing, respectively. In contrast, the DNN model held the worst performance [MAE (14.9, 13.7), RMSE (20.1, 18.2), RAE (43.9%, 43.7%), and RRSE (50.6%, 49.3%)], for training and testing, respectively. Results from the study presented have revealed the capability of DNN-based hybrid models for long-term predictions of the AET values. Moreover, the DNN-RF model has been suggested as the most suitable model to improve future investigation for AET predictions, which could benefit the enhancement of the irrigation process and increase crop yield.

Assessment of hybrid machine learning algorithms using TRMM rainfall data for daily inflow forecasting in Três Marias Reservoir, eastern Brazil.

This study investigates the application of the Gaussian Radial Basis Function Neural Network (GRNN), Gaussian Process Regression (GPR), and Multilayer Perceptron Optimized by Particle Swarm Optimization (MLP-PSO) models in analyzing the relationship between rainfall and runoff and in predicting runoff discharge. These models utilize autoregressive input vectors based on daily-observed TRMM rainfall and TMR inflow data. The performance evaluation of each model is conducted using statistical measures to compare their effectiveness in capturing the complex relationships between input and output variables. The results consistently demonstrate that the MLP-PSO model outperforms the GRNN and GPR models, achieving the lowest root mean square error (RMSE) across multiple input combinations. Furthermore, the study explores the application of the Empirical Mode Decomposition-Hilbert-Huang Transform (EMD-HHT) in conjunction with the GPR and MLP-PSO models. This combination yields promising results in streamflow prediction, with the MLP-PSO-EMD model exhibiting superior accuracy compared to the GPR-EMD model. The incorporation of different components into the MLP-PSO-EMD model significantly improves its accuracy. Among the presented scenarios, Model M4, which incorporates the simplest components, emerges as the most favorable choice due to its lowest RMSE values. Comparisons with other models reported in the literature further underscore the effectiveness of the MLP-PSO-EMD model in streamflow prediction. This study offers valuable insights into the selection and performance of different models for rainfall-runoff analysis and prediction.

Assessment of data intelligence algorithms in modeling daily reference evapotranspiration under input data limitation scenarios in semi-arid climatic condition.

Crop evapotranspiration is essential for planning and designing an efficient irrigation system. The present investigation assessed the capability of four machine learning algorithms, namely, XGBoost linear regression (XGBoost Linear), XGBoost Ensemble Tree, Polynomial Regression (Polynomial Regr), and Isotonic Regression (Isotonic Regr) in modeling daily reference evapotranspiration (ETo) at IARI, New Delhi. The models were developed considering full and limited dataset scenarios. The efficacy of the constructed models was assessed against the Penman-Monteith (PM56) model estimated daily ETo. Results revealed the under full and limited dataset conditions, XGBoost Ensemble Tree gave the best results for daily ETo modeling during the model training period, while in the testing period under scenarios S1(Tmax) and S2 (Tmax, and Tmin), the Isotonic Regr models yielded superior results over other models. In addition, the XGBoost Ensemble Tree models outperformed others for the rest of the input data scenarios. The XGBoost Ensemble Tree algorithms reported the best values of correlation coefficient (r), mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE). Thus, we recommend applying the XGBoost Ensemble Tree algorithm for precisely modeling daily ETo in semi-arid climatic conditions.

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

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 (R2 = 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.

Hypervitaminosis D and Acute Interstitial Nephritis: Tale of Injections.

A 33-year-old man came with nausea, vomiting and abdominal pain due to hypercalcaemia and renal dysfunction following two doses of intramuscular vitamin D injections. Levels of vitamin D were repeatedly above 300 ng/ml over a period of 10 months. Whole-body PET CT scan revealed a thin-walled collection in the right gluteal region. The patient refused a surgical intervention for the same. After 7 months of follow-up, the abscess ruptured spontaneously and was then surgically debrided. At this point, a history of pentazocine addiction was uncovered. One month later, vitamin D levels began to fall along with improvement in serum calcium and creatinine. This case unravels a diagnostic odyssey which ended with a simple surgical debridement. We aim to highlight that vitamin D supplementation in 'megadoses' in the presence of active infection can have an exaggerated response and may take months to resolve.

Evaluation of Roles of MicroRNA-21 and MicroRNA-18a in Esophageal Squamous Cell Carcinoma and Comparison of Their Changes in Expression Post-Chemoradiotherapy.

BACKGROUND: A number of circulating microRNAs (miRNAs) have been reported to be highly expressed in several cancers; whether their expression is associated with clinicopathological factors and prognosis in patients of esophageal squamous cell carcinoma (ESCC) is still under investigation. Although studies have demonstrated their overexpression in tissues of ESCC, there are limited data for circulating miRNAs. Aim of this study was to evaluate the expressions of miRNA-21 and miRNA-18a in patients of ESCC and the effect of chemoradiotherapy (CRT) on expression of these miRNAs. METHODS: This was a case-control study conducted from September 2014 to December 2015 at Sri Aurobindo Medical College and Postgraduate Institute, Indore, India. We compared the expression of miRNA-21 and miRNA-18a in 30 ESCC patients and 30 healthy controls using TaqMan probe-based quantitative real-time polymerase chain reaction (qRT-PCR) and changes in the expression in 16 patients of ESCC, who completed CRT. RESULTS: Both miRNA-21 and miRNA-18a had significantly higher levels of expression in ESCC patients than healthy controls (95% confidence interval (CI): 5.73 - 34.79; P < 0.002 and 95% CI: 3,361.36 - 6,744.23; P < 0.001), respectively. Receiver operating characteristic (ROC) curve analysis showed that combination of serum miRNA-18a and miRNA-21 overexpression could efficiently distinguish patients of ESCC from healthy controls. The miRNA-21 expression positively correlated with tumor invasion (P < 0.004), lymphatic metastasis (P < 0.011), distant metastasis (P < 0.038), and tumor stage (P < 0.001); however, there was no such association observed with miRNA-18a. In the treatment phase (post-CRT), a significant reduction (P < 0.001) was observed in both miRNAs (73.4% in miRNA-18a and 81.02% in miRNA-21). CONCLUSIONS: Both miRNA-21 and miRNA-18a were highly overexpressed in patients of ESCC and their expressions changed significantly with CRT. These miRNAs may be useful tools for the diagnosis and assessment of treatment response in ESCC patients. Further studies will be needed to validate these findings using large number of patients.

Correlation of Thyroid Hormone Profile with Biochemical Markers of Renal Function in Patients with Undialyzed Chronic Kidney Disease.

OBJECTIVE: The present study was conducted to evaluate the correlation of renal functions with thyroid hormone levels in patients with undialyzed chronic kidney disease (CKD). Literature shows significant alteration in thyroid hormone function tests in CKD patients who are receiving long-standing dialysis treatment. However, not much is described in those receiving conservative management without dialysis. Although CKD is associated with an increased prevalence of primary hypothyroidism, various studies on thyroid hormone status in uremic patients have reported conflicting results. METHODOLOGY: Thyroid hormone levels and biochemical markers of renal function were estimated in 30 undialyzed CKD patients and similar number of age- and sex-matched healthy controls, followed by statistical analysis and correlation. RESULTS: Free triiodothyronine (FT3) and free thyroxine (FT4) were found to be significantly reduced (P < 0.001 for each) in undialyzed CKD patients whereas thyroid-stimulating hormone (TSH) levels showed statistically insignificant alteration in both groups. We also observed that urea and creatinine were negatively correlated whereas creatinine clearance was positively correlated with both FT3 and FT4 having high statistical (two tailed) significance with P < 0.001. Nonsignificant correlation was seen between blood urea and TSH (r = 0.236, P = 0.069), creatinine clearance, and TSH (r = 0.206, P = 0.114 Pearson's correlation coefficient). There is just significant positive correlation between the serum creatinine values and TSH (r = 0.248, P = 0.049). CONCLUSIONS: Thyroid hormones were significantly decreased in undialyzed CKD patients as compared to healthy controls.