Shiva Hari Achet's research while affiliated with Roosevelt University and other places

Publications (7)

Conference Paper
Landslide hazard assessment portraying spatial distribution has gained widespread recognition over the past two decades. This paper presents two methods of landslide hazard evaluation namely; Analytic Network Process (ANP) and Competitive Neural Networks (CNN) techniques. The ANP is a qualitative approach founded on the subjective opinion of expert...
Article
Hydraulic connectivity on hillslopes and the existence of preferred soil moisture states in a catchment have important controls on runoff generation. In this study we investigate the relationships between soil moisture patterns, lateral hillslope flow, and streamflow generation in a semi-arid, snowmelt-driven catchment. We identify five soil moistu...
Article
This paper presents a case study of landslide monitoring and evaluation at Okharpauwa, 19 km Chainage along Kathmandu–Trishuli highway in Nepal. An attempt has been made to predict slope movements using backpropagation neural network (BPNN). A Matlab-based BPNN model is developed, and the data from the case study are used to train and test the deve...
Article
Full-text available
A MATLAB based backpropagation neural network (BPNN) model has been developed. Two major geo-engineering applications, namely, earth slope movement and ground movement around tunnels, are identified. Data obtained from case studies are used to train and test the developed model and the ground movement is predicted with the help of input variables t...
Article
An experiment to describe streamflow generation and model the water budget under various semi-arid climatic conditions is ongoing in the Dry Creek basin, near Boise, Idaho. The coarse-grained soils and steep slopes of the region generate runoff primarily in the vadose zone, which extends over the entire soil profile above impermeable granite bedroc...
Article
An Integrated Approach to Modeling The Impact Of Timber Harvest On Streamflow: A GIS Based Hydrologic Model By Shiva Hari Achet Chairperson of the Supervisory Committee: Prof. Susan M. Bolton Forest Management and Engineering Division, College of Forest Resources An integrated modeling approach to assess the impact of timber harvest on peak, mean d...
Article
We investigate the timing of hydrologic connectivity in Dry Creek, a semi-arid watershed near Boise, Idaho using two-process based models, SHAW and HYDRUS2D. Hydrologic connectivity is the condition in which regions of the hillslope, and the hillslope-stream system, are connected via lateral flow pathways, and is an important factor that controls r...

Citations

... Therefore, it is an efficient and powerful methodology for sustainable development mapping and determining where many criteria can be taken into account, and covers all involved aspects [6]. In light of GIS-MCDA-based sustainability assessment, a review of GIS-MCDA research revealed a variety of methods employed earlier by researchers, such as the fuzzy analytical network process (FANP) [16,17], GIS-MCDA-based sensitivity and uncertainty analysis [18][19][20][21]), GIS-MCDA with techniques for ordering preferences by similarity to ideal solution-TOPSIS [21] and integration of empirical models of benchmark with GIS-MCDA methods [22]. Some researchers also compared results of fuzzy based GIS-MCDA with traditional approaches for deriving more accurate results [17,[23][24][25]. ...
... The Simultaneous Heat and Water (SHAW) model has been used to estimate seedbed temperature and moisture as a function of soil conditions and weather inputs (Flerchinger et al., 2012;McNamara et al., 2005). Thermal, wet-thermal, and hydrothermal models have then been applied to predict germination response to seedbed microclimate for evaluation of individual site characteristics and potential species response (Hardegree et al., 2016;Roundy et al., 2007). ...
... Gupta and Sharma (2011) also found that neural network applications in structural engineering have greatly decreased over the last decade. Application of ANNs has been performed successfully in many studies of researchers since the early 1900s for predicting capacity of pile foundations under axial and lateral loads Chan, Chow, and Liu 1995, Lee and Lee 1996, Rahman et al. 2001, Hanna, Morcous, and Helmy 2004, Das and Basudhar 2006, designing and optimizing weights of steel and truss structures (Adeli and Park 1995a, 1995b, Tashakori and Adeli 2002, Kang and Yoon 1994, calculating tunnels and underground opening structures (Lee and Sterling 1992, Shi, Ortigao, and Bai 1998, Shi 2003, Neaupane and Achet 2004, Yoo and Kim 2007, and predicting seismic responses of building structures and bridges (Oh et al. 2020, Lagaros and Papadrakakis 2012, Asteris et al. 2019, 2009,Huang and Huang 2020. Recently, the available computational power for routine training has been greatly enhanced using multiple graphics processing units (GPUs), which has let researchers train larger networks to overcome the above limitations and numerical instability. ...
... Besides, the sigmoid function provides a continuous and non-linear variation between the output neurons and the input variables [67]. Therefore, the sigmoid function was widely implemented for modeling of geotechnical problems with the ANN method [49,[67][68][69]. ...