Rain gauge network design for flood forecasting using multi-criteria decision analysis and clustering techniques in lower Mahanadi river basin, India

Kar, Anil K.; Lohani, A. K.; Goel, N. K.; Roy, Guy

doi:10.1016/j.ejrh.2015.07.003

Cited by 31 publications

(17 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The recent improvements in the efficiency of remote sensing (RS) and geographic information system (GIS) technologies have initiated a revolution in hydrology, particularly in flood management, which can fulfil all the requirements for flood prediction, preparation, prevention, and damage assessment (Tehrany, Pradhan, & Jebur 2013). Among different GIS-based flood models presented in the literature, artificial neural networks (Kia et al, 2011), frequency ratio (FR) , logistic regression (Pradhan 2010), adaptive network-based fuzzy inference system (Chau et al 2005), multi-layered feed forward network (Kar et al, 2015), decision trees (Tingsanchali & Karim 2010;Merz et al 2013;Tehrany et al 2013), and support vector machines (SVMs) (Zhou et al 2013;Tehrany et al 2014) are the most widespread techniques that utilize RS and GIS tools. Although flood forecasting and prediction models are available, the accuracy of flood prediction maps remains a critical issue.…”

Section: Introductionmentioning

confidence: 99%

Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS

Mojaddadi

Pradhan

Nampak

et al. 2017

Geomatics, Natural Hazards and Risk

334

132

View full text Add to dashboard Cite

In this paper, an ensemble method, which demonstrated efficiency in GIS based flood modeling, was used to create flood probability indices for the Damansara River catchment in Malaysia. To estimate flood probability, the frequency ratio (FR) approach was combined with support vector machine (SVM) using a radial basis function kernel. Thirteen flood conditioning parameters, namely, altitude, aspect, slope, curvature, stream power index, topographic wetness index, sediment transport index, topographic roughness index, distance from river, geology, soil, surface runoff, and land use/cover (LULC), were selected. Each class of conditioning factor was weighted using the FR approach and entered as input for SVM modeling to optimize all the parameters. The flood hazard map was produced by combining the flood probability map with flood-triggering factors such as; averaged daily rainfall and flood inundation depth. Subsequently, the hydraulic 2D high-resolution sub-grid model (HRS) was applied to estimate the flood inundation depth. Furthermore, vulnerability weights were assigned to each element at risk based on their importance. Finally flood risk map was generated. The results of this research demonstrated that the proposed approach would be effective for flood risk management in the study area along the expressway and could be easily replicated in other areas.

show abstract

Section: Introductionmentioning

confidence: 99%

Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS

Mojaddadi

Pradhan

Nampak

et al. 2017

Geomatics, Natural Hazards and Risk

334

132

View full text Add to dashboard Cite

show abstract

“…The recent improvements in the efficiency of Remote Sensing (RS) and GIS technologies have initiated a revolution in hydrology, particularly in flood management, which can fulfill all the requirements for flood prediction, preparation, prevention, and damage assessment [51]. Among different GIS-based flood models presented in the literature, artificial neural networks [52], frequency ratio [53], logistic regression [54], adaptive network-based fuzzy inference system [55], multi-layered feed-forward network [56], decision trees [57], [58], and support vector machines [59], [60] are the most widespread techniques that utilize RS and GIS tools [61]. Although flood forecasting and prediction models are available, the accuracy of flood prediction maps remains a critical issue.…”

Section: Related Workmentioning

confidence: 99%

Risk Assessment Inference Approach Based on Geographical Danger Points Using Student Survey Data for Safe Routes to School

2020

View full text Add to dashboard Cite

Safe Routes to School is very important for students to have good physical and psychologically healthy in school life. For providing safe routes based on risk analysis, finding out dangerous points and areas can be a target to avoid dangerous locations by pedestrians and drivers. However, analyzing the risk assessment to derive the safe routes requires a large amount of data with a certain time of observation by experts. Deep learning is a solution to provide information regarding safe routes based on expert knowledge. In this paper, we propose a risk assessment inference approach using a Recurrent Neural Network (RNN) model with Long-Short Term Memory (LSTM) cells based on geographical information for safe routes to school. However, geographical information including coordinates is difficult used in learning-based inference models because of the series of float values. For training the RNN model with the geographical data, coordinates of routes and danger points are translated to be geohash through the geohash converter. The geohash data with other data of features are fused and inputted to the one-hot encoder. The one-hot encoded data is used in the inputs of the RNN model to train the LSTMs. The input data of the training model is derived by the risk index model that is proposed to calculate the risk index based on distances of route coordinates and danger points. Therefore, the risk index is correlated with the training dataset. Through the proposed inference approach, the geographical information including multiple coordinates is enabled to be trained by RNN as a geohash-based input string. Moreover, the input string with other features is fused to support the one-hot encoding to get a better result in RNN models.

show abstract

“…The runoff computation from ungauged or poorly gauged catchments is a serious challenge in developing countries like India where higher operation and maintenance cost differed gauging on small and medium rivers. The knowledge-based or data-driven hydrological models were developed and used by researchers to extend runoff records and address modelling issues (Kar et al 2015(Kar et al , 2017). The hydrological model can be classified into three broad groups, namely metric, physical and conceptual models (Beck et al 1990).…”

Section: Introductionmentioning

confidence: 99%

Comparative evaluation of conceptual and physical rainfall–runoff models

2020

View full text Add to dashboard Cite

The design of water resource structures needs long-term runoff data which is always a problem in developing countries due to the involvement of huge cost of operation and maintenance of gauge discharge sites. Hydrological modelling provides a solution to this problem by developing relationship between different hydrological processes. In the past, several models have been propagated to model runoff using simple empirical relationships between rainfall and runoff to complex physical model using spatially distributed information and time series data of climatic variables. In the present study, an attempt has been made to compare two conceptual models including TANK and Australian water balance model (AWBM) and a physically distributed but lumped on HRUs scale SWAT model for Tandula basin of Chhattisgarh (India). The daily data of reservoirs levels, evaporation, seepage and releases were used in a water balance model to compute runoff from the catchment for the period of 24 years from 1991 to 2014. The rainfall runoff library (RRL) tool was used to set up TANK model and AWBM using auto and genetic algorithm, respectively, and SWAT model with SWATCUP application using sequential uncertainty fitting as optimization techniques. Several tests for goodness of fit have been applied to compare the performance of conceptual and semi-distributed physical models. The analysis suggested that TANK model of RRL performed most appropriately among all the models applied in the analysis; however, SWAT model having spatial and climatic data can be used for impact assessment of change due to climate and land use in the basin.

show abstract

Rain gauge network design for flood forecasting using multi-criteria decision analysis and clustering techniques in lower Mahanadi river basin, India

Cited by 31 publications

References 44 publications

Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS

Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS

Risk Assessment Inference Approach Based on Geographical Danger Points Using Student Survey Data for Safe Routes to School

Comparative evaluation of conceptual and physical rainfall–runoff models

Contact Info

Product

Resources

About