A minimalistic approach for evapotranspiration estimation using the Prophet model

Abstract

Reference evapotranspiration, ETo (mm d -1 ) is estimated by the FAO Penman-Monteith equation (Allen et al. 1998) as:

Statistical analysis of modified Hargreaves equation for precise estimation of reference evapotranspiration

“…ET oPM can also be calculated by using the SPEI package of R Software with input variables maximum Temp, minimum Temp, WS, Hum and sunshine hours. Whereas the variable R s can be estimated with the help of available climatic data and location of the station (Rahman et al, 2020).…”

A novel Hypertuned Prophet based power saving approach for IoT enabled smart homes

“…Since electrical generation does not exhibit saturating growth, the following piecewise linear growth (Equation 4) model is used: $$$$ \mathrm{g}\left(\mathrm{t}\right)=\left(\kappa +a{\left(\mathrm{t}\right)}^{\mathrm{T}}\delta \right)\mathrm{t}+\left(m+a{\left(\mathrm{t}\right)}^{\mathrm{T}}\gamma \right).\kern0.5em $$$$ In Equation (4), $$$ \kappa $$$ denotes the growth rate, $$$ \delta $$$ denotes adjustment rate, $$$ m $$$ denotes the offset parameter, trend changepoints is denoted by $$$ \gamma $$$ and $$$ a $$$(t) denotes a vector defined as Equation (5). 38 Suppose there are s changepoints at times $$$ {s}_j $$$, j = 1,…, s. We define a vector of rate adjustments $$$ \delta \in {R}_S $$$, where $$$ {\delta}_j $$$ is the change in rate that occurs at time $$$ {s}_j $$$. …”

“…In Equation ( 4), 𝜅 denotes the growth rate, 𝛿 denotes adjustment rate, m denotes the offset parameter, trend changepoints is denoted by 𝛾 and a(t) denotes a vector defined as Equation (5). 38 Suppose there are s changepoints at times s j , j = 1, … , s. We define a vector of rate adjustments 𝛿 ∈ R S , where 𝛿 j is the change in rate that occurs at time s j .…”

Probabilistic Water Demand Forecasting Using Quantile Regression Algorithms

“…Machine and statistical learning algorithms (see, e.g., Alpaydin, 2014; Hastie et al., 2009; James et al., 2013; Witten et al., 2017) can be reliably automated and applied at scale (Papacharalampous et al., 2019). Therefore, they are befitting and increasingly adopted for solving urban water demand forecasting problems (see, e.g., Duerr et al., 2018; Herrera et al., 2010; Herrera et al., 2011; Lee & Derrible, 2020; Nunes Carvalho et al., 2021; Quilty & Adamowski, 2018; Quilty et al., 2016; Smolak et al., 2020; Xenochristou & Kapelan, 2020; Xenochristou et al., 2020; Xenochristou et al., 2021), and several other water informatics problems (see, e.g., Althoff, Dias, et al., 2020; Althoff, Filgueiras, & Rodrigues, 2020; Althoff, Bazame, & Garcia, 2021; Markonis & Strnad, 2020; Rahman, Hosono, Kisi, et al., 2020; Rahman, Hosono, Quilty, et al., 2020; Sahoo et al., 2019; Scheuer et al., 2021; Tyralis, Papacharalampous, & Langousis, 2021; Tyralis & Papacharalampous, 2017; Xu, Chen, Zhang, & Chen, 2020; Xu, Chen, Moradkhani, et al., 2020).…”

“…PAPACHARALAMPOUS AND LANGOUSIS 10.1029/2021WR030216 2 of 19 applied at scale (Papacharalampous et al, 2019). Therefore, they are befitting and increasingly adopted for solving urban water demand forecasting problems (see, e.g., Duerr et al, 2018;Herrera et al, 2010;Herrera et al, 2011;Lee & Derrible, 2020;Nunes Carvalho et al, 2021;Quilty & Adamowski, 2018;Quilty et al, 2016;Smolak et al, 2020;Xenochristou et al, 2021), and several other water informatics problems (see, e.g., Althoff, Dias, et al, 2020;Althoff, Bazame, & Garcia, 2021;Markonis & Strnad, 2020;Rahman, Hosono, Kisi, et al, 2020;Rahman, Hosono, Quilty, et al, 2020;Sahoo et al, 2019;Scheuer et al, 2021;Tyralis & Papacharalampous, 2017;Xu, Chen, Moradkhani, et al, 2020).…”