Faculty Publications
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Item Prediction of daily pan evaporation using support vector machines(CAFET INNOVA Technical Society cafetinnova@gmail.com 1-2-18/103, Mohini Mansion, Gagan Mahal Road, Domalguda, Hyderabad 500029, 2014) Pammar, L.; Deka, P.C.Water scarcity globally has lead to severe problems in water management. Understanding the rate of evaporation, from surface water resources is essential for precise management of the water balance. However, evaporation is difficult to measure experimentally due to its nature. Preparing reliable forecasts of evaporation has become an essential element towards efficient water management. The objective of this paper is to predict daily pan evaporation using different kernel functions of Support Vector Machines (SVM's) based regression approach for the meteorological data obtained for the region 'Lake Abaya' which is located in the Great Rift Valley, southern part of Ethiopia. The meteorological parameters considered for study includes daily details of mean-temperature (T), wind speed (W), sunshine hours (Sh), relative humidity (Rh), rainfall (P). Among the kernel functions used for study, the polynomial kernel function proved its credibility by showing improved performance in training and testing periods. The evidence for performance of polynomial kernel function was seen in terms of correlation coefficient (CC) obtained for training and testing is respectively 0.940, 0.956 which is acceptable. © 2014 CAFET-INNOVA TECHNICAL SOCIETY.Item An extreme learning machine approach for modeling evapotranspiration using extrinsic inputs(Elsevier B.V., 2016) Patil, A.P.; Deka, P.C.Precise estimation of evapotranspiration is crucial for accurate crop-water estimation. Recently machine learning (ML) techniques like artificial neural network (ANN) are being widely used for modeling the process of evapotranspiration. However, ANN faces issues like trapping in local minima, slow learning and tuning of meta-parameters. In this study an improved extreme learning machine (ELM) algorithm was used to estimate weekly reference crop evapotranspiration (ETo). The study was carried out for Jodhpur and Pali meteorological weather stations located in the Thar Desert, India. The study evaluated the performance of three different input combinations. The first input combination used locally available maximum and minimum air temperature data while the second and third combination used ETo values from another station (extrinsic inputs) along with the locally available temperature data as inputs. The performance of ELM models was compared with the empirical Hargreaves equation, ANN and least-square support vector machine (LS-SVM) models. Root mean squared error (RMSE), Nash-Sutcliffe model efficiency coefficient (NSE) and threshold statistics (TS) were used for comparing the performance of the models. The performance of ELM model was found to be better than the Hargreaves and ANN model. The LS-SVM and ELM displayed similar performance. ELM3 models, with 36 and 33 neurons in hidden layer were found to be the best models (RMSE of 0.43 for Jodhpur and 0.33 for Pali station) for estimating weekly ETo at Jodhpur and Pali stations respectively. The results showed that ELM is a simple yet efficient algorithm which exhibited good performance; hence, can be recommended for estimating weekly ETo. Furthermore, it was also found that use of ETo values from another station can help in improving the efficiency of ML models in limited data scenario. © 2016 Elsevier B.V.Item Trend and variability of hydrometeorological variables of Tikur Wuha watershed in Ethiopia(Springer, 2020) Ketema, A.; Dwarakish, G.S.The study assessed monthly, seasonal, and annual variability and trend of hydrometeorological variables for 1978–2017 of Tikur Wuha watershed in Ethiopia. The Mann-Kendall trend test and Sen’s slope estimator were employed for the trend and size of the trend, respectively. Besides, the coefficient of variation has been computed for variability analysis. The areal average annual rainfall exhibited an insignificant declining trend with a magnitude of 20.8 mm/decade at a watershed scale. The watershed has been suffering from irregular and erratic rainfall during the dry season. Temperature exhibited a statistically significant rising trend with minimum temperature rises faster than that of the maximum temperature. The streamflow of the Tikur Wuha River was found to be increasing at the rate of 21.16 MCM/decade. The increasing trend of streamflow without the corresponding increase of rainfall in the watershed needs further investigation. © 2020, Saudi Society for Geosciences.Item Computational investigation of air solid flow in a spray dryer for effluent treatment(Scientific Publishers, 2020) Singh, S.K.; Ali, B.In this work, the hydrodynamics and evaporation rate of the co-current spray dryer is numerically investigated through ANSYS Fluent (CFD). The performance of the spray dryer depends on the geometry, operating conditions, and underlying hydrodynamics in such systems. To predict the air-solid flow in a spray dryer, the Euler-Lagrangian CFD model is used to track the particles in the dryer. The continuous phase turbulence is predicted using RNG version of k-turbulence model. To quantify the flow pattern, a horizontal line is considered and spatial variation of velocity profiles are analyzed. The predicted air velocity variation was found to be maximum at the center of the core. Further, the airflow pattern is analyzed for various operating temperatures and feed properties. It was found that airflow pattern influences particle behavior with minimum deposition rates on each section of the wall when air temperature is 350 K. © 2020 Scientific Publishers. All rights reserved.Item Time series forecasting of temperature and turbidity due to global warming in river Ganga at and around Varanasi, India(Springer Science and Business Media Deutschland GmbH, 2022) Das, N.; Sagar, A.; Bhattacharjee, R.; Agnihotri, A.K.; Ohri, A.; Gaur, S.The fluctuation in the river ecosystem network due to climate change-induced global warming affects aquatic organisms, water quality, and other ecological processes. Assessment of climate change-induced global warming impacts on regional hydrological processes is vital for effective water resource management and planning. The global warming effect on river water quality has been analyzed in this work. The river Ganga stretch near the Varanasi region has been chosen as the study area for this analysis. The air temperature has been predicted using the seasonal autoregressive integrated moving average (SARIMA) and the Prophet model. The Prophet model has shown better accuracy with a root mean square percent error (RMSPE) value of 3.2% compared to the SARIMA model, which has an RMPSE value of 7.54%. The river temperature, turbidity, and nighttime radiance values have been predicted for the years 2022 and 2025 using the long short-term memory (LSTM) algorithm. The anthropogenic effect on the river has been evaluated by using the nighttime radiance imageries. The predicted average river temperature shows an increment of 0.58 °C and 0.63 °C for the city and non-city river stretches, respectively, in 2025 compared to 2022. Similarly, the river turbidity shows an increment of 1.21 nephelometric turbidity units (NTU) and 1.17 NTU for the city and non-city stretch, respectively, in 2025 compared to 2022. For future predicted years, the nighttime radiance values for the region situated near the city river stretch show a significant rise compared to the region that lies nearby the non-city river stretch. © 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.Item New Approach for Monitoring the Underground Coal Mines Atmosphere Using IoT Technology(International Information and Engineering Technology Association, 2023) Tripathi, A.K.; Mangalpady, M.; Prasad, S.; Pavan, J.; Kant, R.; Choubey, C.K.Because the atmosphere in underground coal mines contains toxic and flammable gasses, assessing the well-being of miners at all times while working in underground coal mines is an important task. The hazardous environment in underground coal mines reduces the miners' performance, which negatively affects the overall productivity of the mines. Therefore, it is necessary to regularly monitor the environment of underground mines so that appropriate safety measures can be taken. In this work, an IoT-based system was proposed using sensors to detect the concentration of mine gasses, air temperature, and humidity in the environment of underground mines. The developed wireless monitoring system was tested under laboratory conditions for measuring carbon dioxide, carbon monoxide, methane gas, air temperature, and humidity. The proposed monitoring system allows to store the measurement data that will help in predicting future hazardous conditions through artificial neural network and machine learning. The results of this research will help to introduce an innovative monitoring technology in underground coal mines so that miners' safety can be improved by changing safety measures from preventive to predictive. © 2023 Lavoisier. All rights reserved.Item Future transition in climate extremes over Western Ghats of India based on CMIP6 models(Springer Science and Business Media Deutschland GmbH, 2023) Shetty, S.; Umesh, P.; Shetty, A.The effect of climate change on the tropical river catchments in the Western Ghats of India is studied using the Coupled Model Intercomparison Project-6 data (CMIP-6). Multi-model ensembles of rainfall and temperature are constructed using the Random Forest ensemble technique for bias-corrected GCMs in the near future (2014–2050) and far future (2051–2100) horizons. For the two catchments each in the southern, central, and northern Ghats, the trend in minimum and maximum temperatures, precipitation, and other indices are calculated. By 2100, dry sub-humid and humid catchments will see a higher increase in mean annual temperature than per-humid central catchments. In future decades, the warm days and nights increase by 45–50% and 40–70%, respectively, with twofold warming in the winter season. Under a climate change scenario, annual rainfall increases in Vamanapuram, Ulhas, and Purna, while Chaliyar, Netravati, and Aghanashini catchments experience a decrease in rainfall in the far future with an increase in pre-monsoon rainfall. The southern catchments are anticipated to have contrasting variations in the rainfall extremes; northern catchments face a substantial increase in very wet to extremely wet days and medium to heavy rainfall. In all catchments (excluding Vamanapuram), cumulative wet days increase with a decrease in cumulative dry days. After the mid-twenty-first century, humid to per-humid catchments encompass an increase in cool nights, whereas it disappears in dry sub-humid catchments of the Ghat. Interestingly, warming tendencies begin to slow down after 2050. This investigation can assist in comprehending the regional climate extremes in the Western Ghats to formulate better climate risk planning and adaptation strategies. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.Item Revitalizing temperature records: A novel framework towards continuous data reconstruction using univariate and multivariate imputation techniques(Elsevier Ltd, 2024) Yashas Kumar, H.K.; Varija, K.Data gaps are a recurring challenge in climate research, hindering effective time series analysis and modeling. This study proposes a novel two-step data imputation framework to address temperature time series with a long continuous gap surrounded by predictor stations with sporadic missingness. The method leverages iterative gap-filling Singular Spectrum Analysis (SSA) for the small sporadic gaps, followed by multivariate techniques like Inverse Distance Weightage (IDW), Kriging, Spatial Regression Test (SRT), Point Estimation method of Biased Sentinel Hospital-based Area Disease Estimation (P-BSHADE), Random Forest (RF), Support Vector Machines (SVM), and MissForest (MF) for the longer gap. Once the sporadic gaps are effectively addressed with SSA, the method carefully applies multivariate techniques to impute the long continuous gap. Prioritizing accuracy, comprehensive cross-validation with class-based statistical indicators are employed to minimize any potential biases introduced by the imputation process. The study shows the effectiveness of SSA in filling small sporadic gaps using an optimal window length (M ? 365 days) and eigentriple grouping (ET = 30). Notably, for maximum temperature, P-BSHADE and SVM achieve an impressive accuracy (e.g., Legates's Coefficient of Efficiency (LCE), 0.75?0.44, Combined Performance Index (CPI), 6.3%?19.1%) attributed to their ability to capture spatial and/or temporal heterogeneity. While SRT and P-BSHADE offers acceptable performance for minimum temperature (e.g., LCE, 0.51?0.27, CPI, 0.7%?23.7%), the study also uncovers a complex interplay between missing data, predictor stations, and autocorrelation affecting imputation accuracy. This suggests that the reduced performance of certain techniques likely stems from the decline in spatial and spatiotemporal autocorrelation between the target station and its predictors. Overall, this study presents a promising framework for handling complex missing data scenarios often encountered in climate time series analysis, paving the way for more robust and reliable analysis and modeling. © 2024 Elsevier B.V.Item Economic and environmental benefits of cool pavements: a case study of Bhubaneswar city(Springer, 2025) Anupam, B.R.; Chandra Sahoo, U.C.; Vinoj, V.; Rath, P.The change of land use from natural lands to built-up areas is one of the key reasons for the urban heat island (UHI) effect, because of absorbance and storing of heat energy. Roads and streets cover a significant fraction of the urban fabric and are continuously exposed to solar radiation. This study examines the impact of pavement surface temperature on urban air temperature. Measurements were made across the Bhubaneswar city to capture the temperature and relative humidity along the major arterial roads. The study quantified the UHI effect and evaluated the benefits of cool pavements in reducing air temperature and improving energy efficiency. The study reveals a strong relation between pavement surface temperature and near-surface air temperature. It was found that up to 1.5 m above the pavement surface, the impact of pavement surface temperature on the air temperature is substantial. On a particularly hot summer day, the air temperature just above the pavement surface and at 1.5 m above the surface was observed to be higher than the surrounding ambient air temperature by up to 7.4 °C and 2 °C, respectively. Based on the measurements taken during this limited period, the peak UHI intensity in Bhubaneswar city was found to be ~ 1.9 °C, which is high, if the current developments of the city are taken into consideration. This study also indicates that significant economic and environmental benefits can be achieved with the adoption of cool pavement technologies. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.