Precise quantification of evaporation has a vital role in effective crop modelling, irrigation scheduling, and agricultural water management. In recent years, the data-driven models using meta-heuristics algorithms have attracted the attention of researchers worldwide. In this investigation, we have examined the performance of models employing four meta-heuristic algorithms, namely, support vector machine (SVM), random tree (RT), reduced error pruning tree (REPTree), and random subspace (RSS) for simulating daily pan evaporation (EPd) at two different locations in north India representing semi-arid climate (New Delhi) and sub-humid climate (Ludhiana). The most suitable combinations of meteorological input variables as covariates to estimate EPd were ascertained through the subset regression technique followed by sensitivity analyses. The statistical indicators such as root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE), Willmott index (WI), and correlation coefficient (r) followed by graphical interpretations, were utilized for model evaluation. The SVM algorithm successfully performed in reconstructing the EPd time series with acceptable statistical criteria (i.e., NSE = 0.937, 0.795; WI = 0.984, 0.943; r = 0.968, 0.902; MAE = 0.055, 0.993 mm/day; and RMSE = 0.092, 1.317 mm/day) compared with the other applied algorithms during the testing phase at the New Delhi and Ludhiana stations, respectively. This study also demonstrated and discussed the potential of meta-heuristic algorithms for producing reasonable estimates of daily evaporation using minimal meteorological input variables with applicability of the best candidate model vetted in two diverse agro-climatic settings.

No abstract

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.

No abstract

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.