Discharge Forecasting By Applying Artificial Neural Networks At The Jinsha River Basin, China

Abstract

Flood prediction methods play an important role in providing early warnings to government offices. The ability to predict future river flows helps people anticipate and plan for upcoming flooding, preventing deaths and decreasing property destruction. Different hydrological models supporting these predictions have different characteristics, driven by available data and the research area. This study applied three different types of Artificial Neural Networks (ANN) and an autoregressive model to study the Jinsha river basin (JRB), in the upper part of the Yangtze River in China. The three ANN techniques include feedforward back propagation neural networks (FFBPNN), generalized regression neural networks (GRNN), and the radial basis function neural networks (RBFNN). Artificial Neural Networks (ANN) has shown Great deal of accuracy as compared to statistical autoregressive (AR) model because statistical model cannot able to simulate the non-linear pattern. The results varied across the cases used in the study; based on available data and the study area, FFBPNN showed the best applicability, compared to other techniques.

Survey of computational intelligence as basis to big flood management: challenges, research directions and future work

“…Rainfall-runoff analysis (Astel, Walna, Simeonov, & Kurzyca, 2008) 354 daily water level data Statistical Water level forecasting (Sehgal, Sahay, & Chatterjee, 2014) Hourly water stage data Statistical Real-time flood stage forecasting (Yu et al, 2006) (Chang & Chao, 2006) Rainfall computer sensor and RS instrument collect real-time rainfall data Statistical Flash flood and debris flow disaster (Jinxing et al, 2002) GIS-based data from floods Statistical Flood debris flow assessment (Lin et al, 2012) Radar automated meteorological data acquisition system Statistical Early warning system for flood debris flow (Osanai et al, 2010) Daily data of sediment load and discharge Statistical Modeling of sedimentation yield and runoff (Rahim & Akif, 2015) River flow forecasting Statistical River flow estimation using nearby river flow data. (Tayyab, Zhou, Zeng, & Adnan, 2016) Flow forecasting in basins with limited data Statistical (i) There is no prior data, and (ii) only limited data is available (1 year for the Swedish catchment and 1 season for the Mekong River). (Ashrafi, Chua, Quek, & Qin, 2017) Water demand forecasting Statistical This method was tested using 3 years of daily water demand and meteorological data for the city of Calgary, Alberta, Canada.…”

Intelligent Agriculture and Its Key Technologies Based on Internet of Things Architecture

“…Traditional agricultural products cannot guarantee the quality of agricultural products after experiencing multi-level channels, and the time is longer, and the cost of manpower and material resources is higher. Of course, there are also many other problems, such as low utilization rate of arable land, backward agricultural infrastructure and low economic productivity of small farmers, which may lead to the vicious development of agriculture [24]. Once agriculture encounters more serious problems, the whole society will also suffer from crises.…”

Intra- and Inter-Annual Variability of Hydrometeorological Variables in the Jinsha River Basin, Southwest China

“…The Jinsha River Basin originates from the upper reaches of the Yangtze River in China. The Jinsha River Basin flows through the five topographical geomorphic units of China including the Qinghai-Tibet Plateau, the Western Sichuan Plateau, the Hengduan Mountains, the Yunnan-Guizhou Plateau and the Southwestern Sichuan Basin [43]. The total length of the river is 3486 km, accounting for 77% of the total length of the upper reaches of the Yangtze River, and the control area of the river basin is 480,000 km 2 , accounting for 50% of the total area of the upper reaches of the Yangtze River [44,45].…”