Informatics Publishing Limited

Prince Gupta ¹, Satanand Mishra ², S. K. Pandey ¹

Affiliations
1 VNS Group, RGPV, Bhopal, IN
2 CSIR-AMPRI, Bhopal, IN

Abstract

Rainfall is very important parameter in hydrological model. Many techniques and models have been developed for rainfall time series prediction. In this study an artificial neural network (ANN) based model was developed for rainfall time series forecasting. Proposed model used Multilayer perceptron (MLP) network with back propagation algorithm for training. Discharge and rainfall data are took as input parameter for ANN model to predict rainfall time series. Data preprocessing and model’s sensitivity analysis were executed. Collected data is divided in three sets for optimal neural network training. The first set is the training set, used for calculate the gradient and updating the network weights and biases. The second set is the validation set. The error on the validation set is follow during the training process. The third set is test set. It is used to compare different models. Different topologies of Neural Networks were created with change in hidden layer, number of processing element and activation function. Mean Absolute error (MAE), Mean Squared error (MSE) and correlation coefficient (CC) are used to evaluate the model performance. On the basis of these evaluation parameter results, it is found that multilayer perceptron (MLP) network predict more accurate than other traditional models.

Keywords

Data Mining, Artificial Neural Network, Back-Propagation, Rainfall-Runoff Prediction.

Full Text

   

Rakesh Purviya ¹, H. L. Tiwari ¹, Satanand Mishra ²

Affiliations
1 Dept. of Civil Engg., MANIT, Bhopal, IN
2 CSIR-AMPRI, Bhopal, IN

Abstract

Hydrologic cycle are rather very complex and it is very difficult to predict the behaviour of runoff based on temporal data sets of hydrological process, as these are often very large and difficult to analyse and display. Clustering can be done by the different number of algorithms such as hierarchical, partitioning, grid and density based algorithms. This paper is original concerns in two main aspects. First, it provides an evolutionary algorithm for clustering starting from data mining mechanism, tasks and its learning. Second, it provides a taxonomy that highlights some very important aspects in the context of clustering algorithms, namely, hierarchical, partitional algorithms, density based, grid based and model-based. A number of references are provided that describe applications of evolutionary algorithms for clustering in different domains as well as in Hydrology. Also, in this paper a brief overview of temporal data mining concepts including time series sequences are discussed.

Keywords

Temporal, Clustering, Data Mining, Hierarchical, Hard and Soft Clustering, Hydrological Process, Time Series Sequences.

Full Text

   

Rahi Jain ¹, Riddhi Panse ², Satanand Mishra ³

Affiliations
1 Centre for Technology Alternatives for Rural Areas, Shailesh J. Mehta School of Management, Indian Institute of Technology-Bombay, Mumbai 400 076, IN
2 Shailesh J. Mehta School of Management, Indian Institute of Technology-Bombay, Mumbai 400 076, IN
3 Advanced Materials and Processes Research Institute, Bhopal 462 024, IN

Abstract

Wind power is an important renewable energy generation technology, but the location of wind potential and wind power plant installation are not in complete sync with each other. Many national, state and local variables other than wind potential play a role in site selection. The weights given to different local variables during wind power investment decisions are not known and are difficult to estimate in data paucity settings in India. Accordingly, this study proposes a framework to estimate the weights given to different local parameters in wind power investment decisions. We use the case study of select districts in Maharashtra, India to test the framework. The investment predictions based on priority of local factors estimated by the proposed approach are in agreement with the actual investment in the wind energy sector.

Keywords

Agricultural Hierarchy Process, Renewable Energy, Wind Portal, Wind Power Plant.

Full Text

   

Rahi Jain ¹, Bakul Rao ¹, Satanand Mishra ²

Affiliations
1 Centre for Technology Alternatives for Rural Areas, Indian Institute of Technology-Bombay, Powai, Mumbai 400 076, IN
2 Advanced Materials and Processes Research Institute, Bhopal 462 026, IN

Abstract

India launched the National Mission on Medicinal Plants (NMMP) to provide livelihood opportunities for rural entrepreneurs. This study determines effec-tiveness of the mission in achieving rural sustainabi-lity. A case model of Khirvire village, Maharashtra using system dynamics approach is considered to identify the possible externalities which can challenge NMMP effectiveness. It is found that interventions most preferred by the policy and finance are of less preference for the village system. The study concludes that NMMP in its current design will not be sufficient for adequate activities dissemination in the Indian villages.

Keywords

Approach, Interventions, Medicinal Plants, Rural Development, System Dynamics.

Full Text