Daily suspended sediment yield estimation using soft-computing algorithms for hilly watersheds in a data-scarce situation: a case study of Bino watershed, Uttarakhand

Evaluate effect of 126 pre-processing methods on various artificial intelligence models accuracy versus normal mode to predict groundwater level (case study: Hamedan-Bahar Plain, Iran)

“…These methods include physically models, experimental models, and numerical models such as finite difference, finite volume, finite element, and element-free methods. These models involve specific knowledge of the physical characteristics of the study area, complex boundary layers, more assumptions and large dataset which makes it more expensive, labour consuming, tedious etc [ 1 , [10] , [11] , [12] , [13] ]. Nowadays, various machine learning techniques has been proved the capability to overcome the traditional techniques limitations and shown to be precise estimation of different parameters or events with multi time scale in the complex hydrology modelling studies [ [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] ].…”

Sensitivity of daily reference evapotranspiration to weather variables in tropical savanna: a modelling framework based on neural network

“…Soft computing techniques have the capacity over conventional approaches to solving complex problems of various hydrologic processes (Chandwani et al 2015). Regarding its capability to handle highly complex nonlinear problems, the application of various soft computing approaches has attracted the interest of many international researchers during the last few years (Bajirao et al 2021;Tulla et al 2024). Hence, techniques like ANN, ANFIS, gene expression programming (GEP) and deep learning (DL), among others, Extended author information available on the last page of the article have been the extraordinary technology of reference evapotranspiration modelling (Kisi and Alizamir 2018;Tikhamarine et al 2020;Kushwaha et al 2022).…”