Nowadays, great attention has been attributed to the study of runoff and its fluctuation over space and time. There is a crucial need for a good soil and water management system to overcome the challenges of water scarcity and other natural adverse events like floods and landslides, among others. Rainfall–runoff (R-R) modeling is an appropriate approach for runoff prediction, making it possible to take preventive measures to avoid damage caused by natural hazards such as floods. In the present study, several data-driven models, namely, multiple linear regression (MLR), multiple adaptive regression splines (MARS), support vector machine (SVM), and random forest (RF), were used for rainfall–runoff prediction of the Gola watershed, located in the south-eastern part of the Uttarakhand. The rainfall–runoff model analysis was conducted using daily rainfall and runoff data for 12 years (2009 to 2020) of the Gola watershed. The first 80% of the complete data was used to train the model, and the remaining 20% was used for the testing period. The performance of the models was evaluated based on the coefficient of determination (R2), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), and percent bias (PBAIS) indices. In addition to the numerical comparison, the models were evaluated. Their performances were evaluated based on graphical plotting, i.e., time-series line diagram, scatter plot, violin plot, relative error plot, and Taylor diagram (TD). The comparison results revealed that the four heuristic methods gave higher accuracy than the MLR model. Among the machine learning models, the RF (RMSE (m3/s), R2, NSE, and PBIAS (%) = 6.31, 0.96, 0.94, and −0.20 during the training period, respectively, and 5.53, 0.95, 0.92, and −0.20 during the testing period, respectively) surpassed the MARS, SVM, and the MLR models in forecasting daily runoff for all cases studied. The RF model outperformed in all four models’ training and testing periods. It can be summarized that the RF model is best-in-class and delivers a strong potential for the runoff prediction of the Gola watershed.

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This study assessed the ability of two models, Local Linear Regression (LLR) and Artificial Neural Network (ANN) to estimate monthly potential evaporation from Pantagar, US Nagar (India) which falls under sub-humid and subtropical climatic zone. Observations of relative humidity, solar radiation, temperature, wind speed and evaporation have been used to train and test the developed models. A comparison was made between the estimates provided by the LLR model and ANN model. Results shown that the models were able to well learn the events they were trained to recognize. For ANN model the correlation coefficient for training period is 0.9311 and for testing period is 0.9236 and the value of root mean square error for training period is 1.070 and for testing period it is 0.9863. In case of LLR model the correlation coefficient for training period is 0.9746 and for testing period is 0.9273 and value of root mean square error for training period is 0.6121 and for testing period it is 1.5301.

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Pan evaporation modeling is an essential part of water resources management and water budget governance. The study's objective was to examine the suitability of regression and tree-based techniques for estimating pan evaporation from climatic variables. Multiple linear regression (MLR), multivariate adaptive regression splines (MARS), support vector machine (SVM) and random forest (RF) techniques are employed for weekly pan evaporation modeling for the Ranichauri station situated in the Mid-Himalayan region of Uttarakhand, India. The determination of the most appropriate inputs among climatic variables to map evaporation was done by regression approaches. The data was divided into two parts: the first three years of data used for calibration and the remainder of the one-year data used for model validation. Statistical indices such as root mean square error (RMSE), Nash Sutcliffe coefficient of efficiency (NSE), and coefficient of determination (R2) were used to assess the performance of weekly pan evaporation estimating models. Based on scatter plots, the results are under-predicted and over-predicted for the weekly pan evaporation values. The results showed that the values of the RMSE values ranged from 0.542 to 0.689, the NSE values ranged from 0.953 to 0.974, and the highest R2value was found for the SVR model for the testing period. Therefore, the SVR model was found to be superior and can be applied to predict weekly pan evaporation values for the Ranichauri site. Doi: 10.28991/HEF-2022-03-01-07 Full Text: PDF

The commonest malignant ovarian tumour in the adolescent group is yolk sac tumour. It is commonly encountered in adolescents and young women. Incidence is 1% of all ovarian tumours. We reported a case of yolk sac tumour in a 9-year-old girl who presented with intermittent lower abdominal pain, not settling with medical management. Abdominal ultrasonogram showed a left adnexal echogenic mass measuring 5×6 cm with cystic spaces and internal vascularity. MRI abdomen showed T2/STIR hetero intense mass indenting the uterus and posterior bladder wall, multiple bilateral internal iliac, external iliac, left common iliac, aortic and bilateral inguinal nodes along with minimal ascites were seen. She underwent laparoscopy with trucut biopsy which showed moderate nuclear atypia with occasional Schiller-Duval body. Medical oncologist opinion was obtained and she was advised 4 cycles of chemotherapy with carboplatin, bleomycin and etoposide. Later she was planned for laparoscopic cytoreductive surgery. Laparoscopy showed rudimentary uterus, residual left ovarian mass, bilateral normal tubes and small pre-pubertal right ovary. Hence, left salpingo-oophorectomy, infra colic omentectomy and suspicious residual deposits of 1×1 cm near the right broad ligament were removed. Histopathological report of ovary showed no evidence of any residual malignancy. Peritoneum and omentum were free of tumour. Following laparoscopic cytoreductive surgery she is on follow up with AFP till date which is in declining levels and almost reached a normal value.

: Evaporation is one of the main elements affecting water storage and temperature in the hydrological cycle and it plays an important role in evaluation of water availability. Considering the difficulty involved in direct method of evaporation estimation and limitation of empirical methods, an attempt has been made to estimate evaporation by multiple linear regression with the aid of gamma test (GT). The data of meteorological parameters viz., average temperature (T avg ), wind speed (W), average relative humidity (Rh avg ) and sunshine hours (S) were used as input parameters and evaporation was considered as output parameter. The performance of developed model was evaluated in terms of mean squared error (MSE) and correlation co-efficient (r). In developed model, MSE was found to be 1.13 and 0.92 in training and testing phase, respectively. The model demonstrated good values of correlation co-efficient, respectively as 0.91 and 0.95 for training and testing period indicating the suitability of model to estimate the evaporation.