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Soil erosion is the worldwide most significant threat leading to land degradation and water resources deterioration. Identification and prioritization of critical erosion prone areas is an important consideration for policy makers to implement best management strategies that are more sustainable in future for long term use of these natural resources. The present study focuses to identify the specific erosion prone areas within the watershed with the help of hydrological modeling. SWAT model has been used for the identification of critical erosion areas in the Damodar catchment at two levels: watershed and hydrological response unit (HRUs). The derived spatial prioritization maps at watershed and HRUs level indicated that prioritization at watershed scale is not-sufficient methodology for prioritizing the critical soil erosion regions. The critical area identification and prioritization at HRUs level may be more efficient option to achieve the objective of soil erosion control for the policy makers. The HRUs level based analysis showed that about 67.51 % area of Damodar catchment is under critical erosion condition within which a combination of sandy loam soil with agriculture and wasteland landuse is more prone to soil erosion. The results of this study also indicate that erosion is quite sensitive to landuse and soil type within the watershed with other factor of topography and must be utilize to identify the specific patches for an effective soil erosion management rather than planning of whole watershed management which may be a cost intensive option.

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Sustainable management of water resources requires identification and management of critical erosion areas for reducing the reservoir sedimentation. A processbased distributed model SWAT (Soil and Water Assessment Tool) was used to identify critical erosion watersheds in Damodar catchment and tested soil and water management strategy to reduce sediment transport to reservoirs for improving their useful life. The model was calibrated and validated using measured runoff and sediment yield from two watersheds and two reservoir inflows. The validated model was also tested for its appropriateness by comparing the identified critical erosion area of the catchment with the erosion map prepared by Soil Conservation Department (SCD), Damodar Valley Corporation (DVC). The results show that the critical erosion area identified using modeling results matched spatially well with the DVC manually prepared area. Further, the validated model has been used to simulate the sedimentation in the reservoirs. The simulated sedimentation rate is 1.12 and 3.65 Mm 3 /year, respectively, for Konar and Panchet reservoirs for the studied period (1997)(1998)(1999)(2000)(2001), which is reduced to 0.98 and 1.80 Mm 3 /year, respectively, when the critical watersheds are treated with conservation measures. As a result of model identified and implemented management strategy, Konar and Panchet reservoirs will have an additional useful life of 8 and 85 years, respectively. Results show a successful incorporation of distributed hydrological modeling for identifying critical watersheds, developing effective management strategy for controlling soil erosion, reducing reservoir sedimentation and improving their useful life.

The aim of this research is to solve the problem that the intrusion detection model of industrial control system has low detection rate and detection efficiency against various attacks, a method of optimizing BP neural network based on Adaboost algorithm is proposed. Firstly, principal component analysis (PCA) is used to preprocess the original data set to eliminate its correlation. Secondly, Adaboost algorithm is used to continuously adjust the weight of training samples, to obtain the optimal weight and threshold of BP neural network. The results show that there are 13817 pieces of data collected in the industrial control experiment, of which 9817 pieces of data are taken as the test data set, including 9770 pieces of normal data and 47 pieces of abnormal data. In addition, as a test data set of 4000 pieces, there are 3987 pieces of normal data and 13 pieces of abnormal data. It can be seen that the average detection rate and detection speed of the algorithm of optimizing BP neural network by Adaboost algorithm proposed in this paper are better than other algorithms on each attack type. It is proved that Adaboost algorithm can effectively solve the intrusion detection problem by optimizing BP neural network.

Urban floods are different type of flooding event as compared to normally occurring riverine floods which is very often seen along the river banks during heavy rainfall in monsoons. Continuous human interventions in natural vegetative land for rapid Urbanization activities has given rise to Urban Flooding. So, there is a need for capacity analysis of existing storm networks and identification of overflow locations is the need of the study. Hence, in the present study an attempt has been made to simulate Urban Flood scenario for a semi Urban catchment using Storm Water Management Model (SWMM). The whole area is divided into 20 sub catchments and the data acquired from 2017 rainfall events is used for modelling. The study area is represented in SWMM by the help of Master Plan AutoCAD maps having drain lines and Reduced Levels (R.L.s) information. From this detailed elevation information of various nodes and length of pipe lines has been estimated to make the schematic view of the study area in SWMM. The focus of the present study is to model runoff conditions using Dynamic wave method of flood routing and Green-Ampt Infiltration model in SWMM. The results showed that SWMM has capability to model and interpret flows at various channel sections and nodes for mitigating floods. Due to unavailability of gauged flow data the model Parameters needs calibration for more reliable results. Model has effectively given catchment responses for peak flow and volume of runoff which is considered as one of the essential components of Urban drainage planning to mitigate the risk of flood.

In today’s world concrete is one of the major construction materials. With the growth in industrialization and urbanization the demand for the concrete has taken a new pace. Therefore, to fulfill the demand huge amounts of natural resources has to be exploited for the production of the cement because natural resources and raw materials are major constituents in the production. At the same time huge quantities of industrial and agricultural wastes are generating in developing countries and are posing serious risk to the environment as well as human health. So, by utilizing these wastes as a supplementary material in construction reduces the usage of natural resources in the cement as well as decreases the threat of wastes in the environment. Many researchers have proved the effective utilization of wastes in the construction industry as they are more reliable and promote sustainability. This paper reviews waste generation and its statistics as well as environmental implication caused by wastes. It also high lights the possible ways of wastes that can be used in construction, preparation of blocks, insulators etc. This study also provides summary of existing research about usage of Agri and industrial wastes in the construction industry. In addition, paper shows application of wastes in real time construction.

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