Water is known as the most precious gift of nature for growth of civilization as well as a destructive element causing mass devastation. Flood hazards have become ever increasing natural disasters resulting in the highest economic damage among all kinds of natural disasters around the world. The country India is full of rivers and rainfall patterns are heavily influenced by monsoon. Thus occurrence of flood remains an inevitable feature in most parts of the country. The large river systems like Ganga, Brahmaputra, Godavari and Mahanadi influence the flood scenario of the country. Mahanadi is the 6l largest river system in India. The river is also known for its huge water potential and frequent flood devastations. Chhatisgarh and Orissa states of India cover almost 99% of the catchment area of Mahanadi basin. Currently a number of developmental projects are going on in these two states. For these projects well defined flood estimate formulae are required. The lower reach of Mahanadi basin is in the state of Orissa and flood is a permanent threat to this reach. Hirakud reservoir is the only major flood controlling structure in the basin. The downstream area of Hirakud is around 58000 km2. It remains uncontrolled and experiences frequent floods. Flood damages can be reduced drastically by adopting various non-structural measures such as flood frequency prediction and flood forecasting. In the present study efforts have been made to develop regional flood formulae for the entire Mahanadi basin using hybrid and prioritized variables based approach for studying the impact of climate variability. For the lower reach of Mahanadi basin (downstream of Hirakud dam) flood forecasting models have been developed using machine learning techniques like ARIMA and ARNN models. The performance of hybrid models has been compared with single and conceptual models.

Broad Objectives In the present study the impact of climate variability and flood problem of Mahanadi basin has been addressed by developing regional flood formulae for the uncontrolled portion of the basin and by developing a flood forecasting model using ARIMA and ARNN models for the lower reach. The objectives are summarized as follows:
i. Development of regional flood formulae for Mahanadi basin.
ii. Development of a flood forecasting model for the reach downstream of Hirakud, and
iii. Development of a key raingauge network for Kantamal sub-basin of lower Mahanadi basin for flood forecasting.

Motivated from these discussions, this study proposes a novel hybrid ARIMA-ARNN (AARNN) model that captures complex data structures and linear plus nonlinear behavior of Mahanadi river flood data sets like rainfall and temperature. In the first phase of the proposed model, ARIMA catches the linear patterns of the data set. Then the ARNN model is employed to capture the nonlinear patterns in the data using residual values obtained from the base ARIMA model. The proposed model has easy interpretability, robust predictability and can adapt seasonality indices as well. Through experimental evaluation, we have shown the excellent performance of the proposed hybrid AARNN model for the Mahanadi river flood forecasting data sets.

India is now the world’s third largest economy in purchasing power of parity terms. Its per capita income has quintupled. Gains in poverty reduction have been the fastest ever the country has experienced. But a lot more remains to be done if the country is to make its “trust with destiny”. Our product markets have been reformed, but the same cannot be said for labour, land, supply chain activities and to some extent capital markets. India still ranks 130th among 189 countries in ease of doing business, trailing countries it wants to compete with by a wide margin. Though in the literature a number of different criteria have developed to design supply chain network for the models; the key managerial policies: integrated facility location, inventory allocation, preservation technology investment, forward financing (fully) and promotional activity investment, which are broadly provided for logistics planners with a high-level optimal solution. In practice, the deterioration rates of sea foods, fruits, milk products, flowers, medicine, and food can be reduced by centralizing warehousing facilities, using refrigeration equipment, or by applying drying or vacuum packing technology. Such goods are commonly distributed through multi-echelon supply chain networks. Also, forward financing has being existed commonly in today’s business. Integrating deteriorating inventory with preservation efforts and considering forward financing are essential in designing supply chains. Specifically, this study will determine the following DC network design decisions: influence area of each; Joint replenishment cycle time at DCs; preservation technology investment; Promotional effort; and economic order quantity.

Objectives of the Study
The objective of this study is to provide logistics network planners with a high-level solution for integrated facility location, inventory allocation, preservation effort problems, and promotional effort by considering perishable items and forward financing (fully). The following objectives have been framed to conduct the study in the context of supply chain network design models:

• To determine the optimal influence area for each distribution centres (DCs) in cluster i, i=1,2 .......N.
• To determine the optimal joint replenishment cycle time.
• To determine the optimal investment in preservation technology.
• To determine the optimal investment in promotional effort.
• To determine the optimal order quantity.
• To determine the effects of the parameters on decision and profits under forward financing.