The River Subansiri, one of the largest tributaries of the Brahmaputra, makes a significant contribution towards the discharge at its confluence with the Brahmaputra. This study aims to investigate an appropriate model to predict the future flow scenario of the river Subansiri. Two models have been developed. The first model is an artificial neural network (ANN)-based rainfall-runoff model where rainfall has been considered as the input. The future rainfall of the basin is calculated using a multiple non-linear regression-based statistical downscaling technique. The proposed second model is a hybrid model developed using ANN and the Soil Conservation Service (SCS) curve number (CN) method. In this model, both rainfall and land use/land cover have been incorporated as the inputs. The ANN models were run using time series analysis and the method selected is the non-linear autoregressive model with exogenous inputs. Using Sen's slope values, the future trend of rainfall and runoff over the basin have been analyzed. The results showed that the hybrid model outperformed the simple ANN model. The ANN-SCS-based hybrid model has been run for different land use/land cover scenarios to study the future flow scenario of the River Subansiri.

Subasiri河是雅鲁藏布江最大的支流之一，在与雅鲁藏布江的汇合处对排放物做出了重大贡献。这项研究旨在研究一个合适的模型，以预测苏班西里河的未来流量情况。已经开发了两种模型。第一个模型是基于人工神经网络（ANN）的降雨-径流模型，其中降雨被视为输入。流域的未来降雨量是使用基于多元非线性回归的统计降尺度技术计算得出的。拟议的第二个模型是使用ANN和水土保持服务（SCS）曲线数（CN）方法开发的混合模型。在这个模型中，降雨和土地利用/土地覆盖都作为输入。使用时间序列分析运行ANN模型，选择的方法是带有外源输入的非线性自回归模型。利用Sen的斜率值，分析了流域内降雨和径流的未来趋势。结果表明，混合模型优于简单的人工神经网络模型。基于ANN-SCS的混合模型已经针对不同的土地利用/土地覆盖情景进行了研究，以研究Subansiri河的未来流量情景。