Shreya Nivesh

Present study deals with the determination of crop irrigation requirement of major cultivated crops in Balangir district, Odisha, India. The main crops include Paddy, Pulses, Cotton, Sesame, Groundnut and Mango. The irrigation water requirements and scheme water supply for each crop in the district were determined by using CROPWAT 8.0 model. Reference evapotranspiration was calculated using Food and Agriculture Organization-Penman Montieth equation. The effective rainfall was calculated using USDA S.C. method. Modelling results showed that actual irrigated area in the district is 17794 km2 and net irrigation demand for the actual irrigated area is 0.9 BCM. This study might be useful to prevent over or under irrigation and planning water management strategies in the district.

The present study was undertaken to estimate the suspended sediment load from the Vamsadhara river basin comprising of 7820 km area, situated between Mahanadi and Godavari river basins. Three daily input data groups or cases were employed using Artificial Neural Network (ANN), Adaptive NeuroFuzzy Inference System (ANFIS), Fuzzy Logic (FL), Multiple Linear Regression (MLR) and Sediment Rating Curve (SRC) to find the effect of different inputs on the suspended sediment load. Input 1 consists of Pt, Qt, Qt−1, St−1 as inputs to the model to predict St. Input 2 consists of Pt-1, Qt, Qt-1, St-1 and Input 3 consist of Pt-1, Qt, Qt-2, St-1. The developed models were trained and tested. Three statistical parameters: root mean square error (RMSE), correlation coefficient (r) and coefficient of efficiency (CE) were used to compare the results of the models. Based on the performance analysis results revealed that the ANFIS model (RMSE-44.02 kg/sec, r-0.995 and CE-99.06%) outperformed other soft...

Accurate estimation of suspended sediment load carried by rivers is of utmost importance in the soil and water conservation practices in the watershed and also in large number of hydro-environmental issues such as planning, design and operations of reservoirs, dams and environmental impact assessment. This study explores the abilities of statistical models to improve the accuracy of rainfall-streamflow-suspended sediment relationships in daily suspended sediment estimation. In this study, a comparison was made between multiple linear regression and artificial neural networks (ANNs) for the Vamsadhara river catchment. Daily rainfall-runoff and suspended sediment data were used as inputs and outputs. The performance results based on three different types of indicators viz. root mean square error (RMSE), correlation coefficient (r) and coefficient of efficiency (CE) revealed that ANN (RMSE-110.15 kg/sec, r-0.97 and CE value 94.22 % ) can predict sediment load more efficiently than trad...

Present study deals with the determination of crop irrigation requirement of major cultivated crops in Balangir district, Odisha, India. The main crops include Paddy, Pulses, Cotton, Sesame, Groundnut and Mango. The irrigation water requirements and scheme water supply for each crop in the district were determined by using CROPWAT 8.0 model. Reference evapotranspiration was calculated using Food and Agriculture Organization-Penman Montieth equation. The effective rainfall was calculated using USDA S.C. method. Modelling results showed that actual irrigated area in the district is 17794 km 2 and net irrigation demand for the actual irrigated area is 0.9 BCM. This study might be useful to prevent over or under irrigation and planning water management strategies in the district.

Accurate estimation of suspended sediment load carried by rivers is of utmost importance in the soil and water conservation practices in the watershed and also in large number of hydro-environmental issues such as planning, design and operations of reservoirs, dams and environmental impact assessment. This study explores the abilities of statistical models to improve the accuracy of rainfall-streamflow-suspended sediment relationships in daily suspended sediment estimation. In this study, a comparison was made between multiple linear regression and artificial neural networks (ANNs) for the Vamsadhara river catchment. Daily rainfall-runoff and suspended sediment data were used as inputs and outputs. The performance results based on three different types of indicators viz. root mean square error (RMSE), correlation coefficient (r) and coefficient of efficiency (CE) revealed that ANN (RMSE-110.15 kg/sec, r-0.97 and CE value 94.22 %) can predict sediment load more efficiently than traditional models like multiple linear regression.