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.
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.
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