Application of stochastic models in predicting Lake Malawi water levels

Makwinja, Rodgers; Phiri, Titus B.; Kosamu, Ishmael Bobby Mphangwe; Kaonga, Chikumbusko Chiziwa

doi:10.5897/ijwree2017.0740

Cited by 8 publications

(2 citation statements)

References 18 publications

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“…Previous hydrological studies have assessed the trends and relationships between the water level in Lake Malawi [44,45], the discharge in the Shire River [46], and the observed and potential impacts of climate change on the local hydrology [47,48] and hydropower generation [49], as well as the perceived risks and potential adaptation options under climatic and socio-economic uncertainty in the Shire River Basin [50]. The literature has also highlighted that the lake level is highly sensitive to climate variability [46], with cyclic fluctuations in levels being largely subject to annual rainfall patterns and seasonal precipitation and temperature variables anticipating lake level changes by approximately two months.…”

Section: Introductionmentioning

confidence: 99%

Monitoring hydropower reliability in Malawi with satellite data and machine learning

Falchetta

Kasamba

Parkinson

2020

Environ. Res. Lett.

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Hydro-climatic extremes can affect the reliability of electricity supply, in particular in countries that depend greatly on hydropower or cooling water and have a limited adaptive capacity. Assessments of the vulnerability of the power sector and of the impact of extreme events are thus crucial for decisionmakers, and yet often they are severely constrained by data scarcity. Here, we introduce and validate an energy-climate-water framework linking remotely-sensed data from multiple satellite missions and instruments (TOPEX/POSEIDON. OSTM/Jason, VIIRS, MODIS, TMPA, AMSR-E) and field observations. The platform exploits random forests regression algorithms to mitigate data scarcity and predict river discharge variability when ungauged. The validated predictions are used to assess the impact of hydroclimatic extremes on hydropower reliability and on the final use of electricity in urban areas proxied by nighttime light radiance variation. We apply the framework to the case of Malawi for the periods 2000-2018 and 2012-2018 for hydrology and power, respectively. Our results highlight the significant impact of hydro-climatic variability and dry extremes on both the supply of electricity and its final use. We thus show that a modelling framework based on open-access data from satellites, machine learning algorithms, and regression analysis can mitigate data scarcity and improve the understanding of vulnerabilities. The proposed approach can support long-term infrastructure development monitoring and identify vulnerable populations, in particular under a changing climate.

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Section: Introductionmentioning

confidence: 99%

Monitoring hydropower reliability in Malawi with satellite data and machine learning

Falchetta

Kasamba

Parkinson

2020

Environ. Res. Lett.

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“…1). Makwinja et al (2017) developed a stochastic model for forecasting lake levels based on historical levels. Calder et al (1994) attributed lake level changes from 1896 to 1967 to rainfall alone, but assumed constant evaporative demand and forest cover.…”

Section: Introductionmentioning

confidence: 99%

Multivariate framework for the assessment of key forcing to Lake Malawi level variations in non-stationary frequency analysis

Ngongondo

Zhou

2020

Environ Monit Assess

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Lake Malawi in south eastern Africa is a very important freshwater system for the socio-economic development of the riparian countries and communities. The lake has however experienced considerable recession in the levels in recent years. Consequently, frequency analyses of the lake levels premised on time-invariance (or stationarity) in the parameters of the underlying probability distribution functions (pdfs) can no longer be assumed. In this study, the role of hydroclimate forcing factors (rainfall, lake evaporation, and inflowing discharge) and low frequency climate variability indicators (e.g., El Nino Southern Oscillation-ENSO and the Indian Ocean Dipole Mode-IODM) on lake level variations is investigated using a monthly mean lake level dataset from 1899 to 2017. Non-stationarity in the lake levels was tested and confirmed using the Mann-Kendall trend test (α = 0.05 level) for the first moment and the F test for the second moment (α = 0.05 level). Change points in the series were identified using the Mann-Whitney-Pettit test. The study also compared stationary and non-stationary lake level frequency during 1961 to 2004, the common period where data were available for all the forcing factors considered. Annual maximum series (AMS) and peak over threshold (POT) analysis were conducted by fitting various candidate extreme value distributions (EVD) and parameter fitting methods. The Akaike information criteria (AIC), Bayesian information criteria (BIC), deviance information criteria (DIC), and likelihood ratios (RL) served as model evaluation criteria. Under stationary conditions, the AMS when fitted to the generalized extreme value (GEV) distribution with maximum likelihood estimation (MLE) was found to be superior to POT analysis. For the non-stationary models, open water evaporation as a covariate of the lake levels with the GEV and MLE was found to have the most influence on the lake level variations as compared with rainfall, discharge, and the low frequency climatic forcing. The results are very critical in flood zoning especially with various planned infrastructural developments around the lakeshore.

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A systematic review on machine learning algorithms used for forecasting lake‐water level fluctuations

Chakravarthy¹,

Bharanidharan

Rajaguru³

2022

Concurrency and Computation

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Summary Globally, the water‐level fluctuations in lakes are a dynamic and complex process. The fluctuation is characterized by higher non‐linearity and stochasticity, making it quite hard to forecast for future planning. However, the advent of machine learning algorithms in recent decades provides significant improvement in forecasting such fluctuations. This work provides a systematic review of the machine learning algorithms (ML) used for characterizing lake‐water level dynamics. Among those ML algorithms, the paper reviews seven distinct as well as popular algorithms namely, neural network model, support vector machines, extreme learning machine, artificial neuro‐fuzzy inference systems, evolutionary and hybrid algorithms, and deep learning models. The sample inputs, splitting of data, performance metrics, and its comparison are discussed with future scope. Moreover, the strength and limitations of those algorithms are also examined. Finally, the review in this paper gives a newer vision for water resource planners and hydrologists for sustainable lake management.

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Application of stochastic models in predicting Lake Malawi water levels

Cited by 8 publications

References 18 publications

Monitoring hydropower reliability in Malawi with satellite data and machine learning

Monitoring hydropower reliability in Malawi with satellite data and machine learning

Multivariate framework for the assessment of key forcing to Lake Malawi level variations in non-stationary frequency analysis

A systematic review on machine learning algorithms used for forecasting lake‐water level fluctuations

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