Rabindra Nath Barman

Impact of Climate Change on the Availability of Virtual Water Estimated with the Help of Distributed Neurogenetic Models

Impact of climate change on virtual water of a tropical multireservoir system was estimated with the help of models developed by neural network and genetic algorithm. Virtual water or embedded water or embodied water, or hidden water refers to the water used in the production of goods or services. For instance, it takes 1,300 m3 of water on an average to produce 1 t of wheat. The precise volume can be more or less depending on climatic conditions and agricultural practice. The virtual water has major impacts on productive use of water and global trade policy especially in water-scarce regions. The impact of climate change on virtual water could open a path for the efficient use of virtual water in the face of climatic uncertainties, which may directly impact availability of raw water. The present study tried to estimate the future virtual water with the help of neurogenetic models, which estimates stream flow as function of various hydrological, meterological variables, and basin characteristics. The models prepared were distributed in nature and also consider temporal variability. In total, two models were prepared with rainfall, time of concentration, and catchment loss as input and stream flow as output. One model was prepared by classifying the dataset, based on the magnitude of the variable, and the other model was prepared with normal dataset. First, the better performing model was identified and then output from RCM-PRECIS model was applied to the chosen model to estimate the impact of climate change on stream flow. The estimation results were used to calculate the amount of virtual water, and the result was compared with the present-day virtual water to analyze the change in virtual water availability due to climate change. According to the results, model prepared with normal dataset was identified as a better model, and from the estimations it could be concluded that virtual water availability would increase in case of both A2 and B2 scenario of climate change where the change would be more pronounced in case of the latter.

Application of Parity Classified Neurogenetic Models to Analyze the Impact of Climatic Uncertainty on Water Footprint

Water footprint of an individual, community, or business is defined as the total volume of freshwater that is used to produce the goods and services consumed by the individual or community, or produced by the business. Neurogenetic models were widely used in the prediction of hydrologic variables, and outcome of such applications were found to be satisfactory. The irregular rainfall and temperature pattern, and degradation of watersheds were causing worldwide reduction of water availability (UNFCC). As water footprint is directly related to water availability and also shows the demand from industrial consumers, the present study tried to estimate the impact of climate change on water footprint between two river basins of East India with the help of neurogenetic models. The climate change scenarios were generated with the help of PRECIS climate models, and future runoff was estimated by a neurogenetic model trained with orthopareto dataset. The output from the neurogenetic model, named as PARITYCGD, was compared with a neurogenetic model trained with normal dataset (NGHYD) and conceptual hydrologic models. According to the results, the neurogenetic model trained with orthopareto dataset was selected as the better model among the five models, which shows that neural models trained with orthopareto dataset learn a problem better than a neurogenetic model trained with normal dataset. From the prediction of stream flow, water footprint of the sampling regions were calculated and according to the estimations, water footprint would be reduced in both A2 and B2 climate change scenarios where reductions would be more pronounced in A2 than in B2. Although, due to data dependency of neurogenetic models, the PARITYCGD model may not work for other basins but for the present study, it was found to have better accuracy than the conceptual hydrologic model.

Estimation of Reservoir Discharge with the Help of Clustered Neurogenetic Algorithm

This chapter presents a new approach of reservoir out flow prediction using a clustered neurogenetic algorithm. The algorithm combines the learning ability of artificial neural networks with searching capability of the genetic algorithm. The model is tested on the Panchet reservoir in river Damodar using the historical, hydrological, and water supply dataset. The values of the input parameters are classified into six groups based on the magnitude of the input parameters. The results showed a highly adaptive and flexible investigating ability of the model in prediction of nonlinear relationships among different variables.

Estimation of the Spatial Variation of Water Quality by Neural Models and Surface Algorithms

The present study was a continuation of the scientific investigation described in Chapter 9. The present research tried to estimate spatial variation of water quality, expressed by Weighted Average Water Quality (WAWQ), from the estimated spatial variation of stream flow as explained in Chapter 9. The relationship between WAWQ and stream flow was estimated with the help of neurogenetic models, and the spatial variation was predicted by radial basis surface algorithm. According to the results, upstream of Damodar River was found to have low quality of water than the upstream of river Barakar, downstream of river Damodar, and the entire river networks of Rupnarayan. But in the future, quality of river water will be estimated to degrade with time for both the scenarios of climate change, which was depicted by the surface diagrams of the future, where area of low WAWQ circles were seemed to be increased with time from 2010 to 2100. The change was more or less similar for both A2 and B2 scenario of climate change.

Estimation of the Spatial Variation of Pollution Load by Neural Models and Surface Algorithms

The present study tried to predict spatial variation of water pollutants with the help of two pollution factors: spatial variation of stream flow and spatial variation of water quality and neurogenetic algorithms. The two pollution factors were, respectively, industrial pollution (IP) factor, which identifies the intensity and presence of industrial pollutants from common water quality parameters that got influenced due to the release of industrial effluents in a river and organic pollution (OP) factor, which tries to estimate the intensity of organic pollutants from common quality parameters that get affected due to anthropogenic presence in the adjacent catchments. A neurogenetic model was prepared to estimate industrial pollution (IP) and organic pollution (OP) factors where observed stream flow data from 42 gauged and ungauged sampling points within two river networks in the Eastern India and land use of adjacent catchments of the sampling points were taken as input. The IP and OP factors are prepared to be directly proportional to water pollution, that is, if the factors are more than 0.7, water is polluted and if the same are less than 0.5, water is not polluted at all. The pattern identification capability of neurogenetic models enforces the authors for selection of neurogenetic models for the prediction of the above two factors. After the model was validated with the help of common validation equations, the selected model was applied to predict future IP and OP of the same region due to changed climate scenario generated by PRECIS climate model. The output of PRECIS was fed to PARITYCGD model (9), which estimated the stream flow due to the changed climatic scenario that was again used to predict IP and OP of the sampling points. The estimated values were fed to a surface algorithm to show the spatial variation of the two factors within the river basins. According to the results, area under water pollution from industries were more than the area that was not under pollution during the A2 scenario of climate change, but in B2 the trend reverses and more area without industrial pollution would emerge. But in case of water polluted by organic wastes, more area was predicted to be without pollution than area under pollution in case of A2 scenario and for B2 scenario of climate change the area without pollution will get increased in a fast rate from 2010 to 2100 and in 2071–2100 the increase would be maximum. As A2 scenario was predicted to be economic but without any restrictions on CO2 emission, the future land use was generated as industrially active but still area under pollution was more in A2 than in B2, which was imagined to be environmentally stable and with severe restrictions on CO2 emission.

Estimation of the Spatial Variation of Stream Flow by Neural Models and Surface Algorithms

The present study tried to estimate spatial variation of stream flow in face of climatic uncertainty. Seven neurogenetic models with different but related input variables were used to predict stream flow of both gauged and ungauged sub-basins of two river networks of Eastern India’s intratropical region. The models were validated and the better model was selected for estimation of stream flow. PARITYCGD was found to be the better model due to its lower RMSE and higher efficiency than any other considered models. The PARITYCGD model was then compared with three conceptual hydrologic models, and here also it was selected as the better model due to higher efficiency and reliability and lower RMSE and uncertainty than the other considered conceptual models. The radial basis surface interpolation was now used to generate spatial variation of stream flow within the two river networks due to the generated weather scenarios by PRECIS climate models. According to the results, the degradation of catchment, which is generally interpreted from high magnitude of stream flow, was observed in north-west and north-east region within the two river networks as per the surface diagram generated from model predictions due to observed rainfall and land-use data. For the future climatic data, spatial variation of stream flow was found to be concentrated in the same two regions only the areas showed an increasing trend, which was more in A2 scenario than in the case of B2 scenario of climate change. The model output was applied to generate spatial variation of water quality and pollution, which are explained in Chapters 10 and 11.

Impact of Stressed Climatic Condition on a Small Tropical Tributary

The present study tried to analyze the impact of stressed climatic condition on a small tropical tributary of river Hooghly with the help of neural network and genetic algorithm. The stressed conditions of a basin were represented by six categories. According to the results, the retentivity of the catchment is poor that is why even in positively overstressed climatic condition the catchment responded with low discharges. The impacts of change on ground cover, water demand, and land use were ignored or taken to be the same for the different conditions that were applied to evaluate the basin response.

Designing configuration of shell-and-tube heat exchangers using grey wolf optimisation technique

Organic Rankine cycles (ORC) is illustrated with various solutions for low rank waste heat revival. The process of ORC can be analysed by the property of the thermodynamic limits. The configuration of shell-and-tube heat transformer is used to obtain the ORC with cycle limits mutually. Depend on biogeography-based optimisation method is used to improve a new shell and tube heat transformer optimisation model method, in this review. The significant intention of the proposed technique is to frame a mathematical modelling with the optimisation techniques, from this modelling to find optimal tube configuration and optimal weight of shell-and-tube heat exchanger. Mathematical modelling is utilised to predict the exergetic plant efficiency, energetic cycle efficiency and electrical power. Different optimisation techniques are used to obtain the optimal weights α and β of the mathematical modelling. All optimum results demonstrate that the attained error values between the output of the experimental values and the predicted values are closely equal to zero in the designed model. From the results, the minimum error 96.066% is determined by mathematical modelling to attain in GWO algorithm.

Use of Forest Index or PLANOBAY in Estimation of Water Availability Due to Climate Change

The present study tried to estimate future water availability with the help of Forest Index or Plantation-Prioritized Basin Yield Estimation (PLANOBAY) Hydrologic model, which is a multi-event, discharge prediction model based on variation of discharge with basin area and canopy cover. RCM-PRECIS model was applied to generate future weather scenarios. The observed rainfall along with Vegetated Area Index (VAIn) was used as input to estimate basin runoff. Presence of vegetated area (forest, plantations, cropped land) in any basin would impact the quantity of basin runoff as vegetated areas could hold water with greater capacity than any nonvegetated area. Hence the estimation of runoff from vegetated and nonvegetated catchment must differ and for former, models must include or consider the relationship between vegetated area and the amount of basin runoff. In PLANOBAY, VAIn represents the relationship between vegetated area and basin runoff. VAIn represented the variance of basin area and vegetated area with respect to basin runoff. A neurogenetic model was developed to identify the patterns associated with VAIn, rainfall, and basin runoff. Dataset of 3 decades (1970–2002) was employed to train the model. After the successful completion of training, models were compared with three conceptual models, namely, Hydrologic Engineering Centre – Hydrologic Modeling System (HECHMS), Trend Research Manual of 1955 (TR55), and MODified RATional (MODRAT) hydrologic model. The better model among the four was identified with the help of root mean square error (RMSE), correlation coefficient (r), coefficient of efficiency (E), and first-order uncertainty analysis (U). Future water availability was estimated with the help of estimated stream flow from the selected model, estimated rainfall from PRECIS climatic model-generated weather scenarios, and Water Budget equation. According to the results, PLANOBAY model was selected as better model among the four, and according to the estimations from the same model, future water availability of the two river basins would reduce for both A2 and B2 scenario of climate change where the water scarcity would be more pronounced in A2 than in B2.